esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Exhaust Gas Detection  
The experiment was carried out to understand the role of plasma in the decomposition of precursors. The research was divided into two groups: one was an experimental group with the 200 W atmospheric pressure microwave plasma, and the other was without plasma. When the plasma is turned off at room temperature, we measure the initial concentration of the exhaust gas and then use gas chromatography to detect the specific density of the exhausted gas treated by the different temperatures of 400, 500, 600, 700, 800, 900, and 1000 \({}^{\circ}\)C, with and without plasma. The ethanol content rates are obtained by comparing the ethanol content at different temperatures with the original content. Through the comparative analysis of exhaust gas composition at different temperatures, the roles of microwave plasma and temperature can be figured out.  
Figure 3 shows the ethanol content rates at the temperature of 400, 500, 600, 700, 800, 900, and 1000 \({}^{\circ}\)C, with and without microwave plasma. When the plasma is turned off, the apparatus will become an atmospheric pressure CVD; the ethanol content rates decreased with the increase of furnace temperatures. When the furnace temperature was as high as 1000 \({}^{\circ}\)C, the ethanol content rate was 26.86%. However, the ethanol content rate was all about 1% at any furnace temperature when the microwave plasma was turned on. In the experiment, no other organic hydrocarbon except ethanol was found in the gas chromatograph test results, indicating that the active group did not recombine into hydrocarbons. Therefore, we can conclude that the 200 W atmospheric pressure microwave plasma almost completely decomposes the precursor.  
Figure 2: The measured spectrums of Ar-H\({}_{2}\) and Ar-ethanol-H\({}_{2}\) microwave plasma in atmospheric pressure.
## 3 Problem Description and Model
Customer satisfaction \(A\) and total completion time \(B\) are two optimization objectives. Based on the above descriptions and notations, the following mathematical model is established. \[\text{Maximum }A=\sum_{i=1}^{n}\max\Bigl{(}0,t_{i}-t_{i}^{0}\Bigr{)} \tag{1}\]  
\[\text{Minimum }B=\max_{1\leq k\leq n}t_{k}^{e} \tag{2}\]  
Subject to:  
\[\max_{1\leq i\leq n}v_{i}\leq c,\forall k,f, \tag{3}\]  
\[\sum_{b=1}^{T_{k,f}}w_{p_{k,f}}\leq c,\forall k,f, \tag{4}\]  
\[\sum_{b=0}^{T_{k,f}-1}d\Bigl{(}l_{k,f,b},l_{k,f,b+1}\Bigr{)}+d\Bigl{(}l_{k,f,T_ {k,f}},s_{0}\Bigr{)}\leq v\times e,\forall k,f, \tag{5}\]  
\[t_{k}^{e}=t_{k,T_{k}}+q_{k,T_{k}},\forall k, \tag{6}\]  
\[t_{k,f}= \begin{cases}0,&f=1\\ t_{k,f-1}+q_{k,f-1},&f\geq 2\end{cases} ,\forall k, \tag{7}\]  
\[q_{k,f}=\sum_{b=0}^{T_{k,f}-1}d\Bigl{(}l_{k,f,b},l_{k,f,b+1}\Bigr{)}+d\Bigl{(}l _{k,f,T_{k,f}},s_{0}\Bigr{)}+t^{d}+T_{k,f}\times t^{e},\forall k,f, \tag{8}\]  
\[p_{k,f,b}=\sum_{i=1}^{n}i\times x_{k,f,b,i},\forall k,f,b, \tag{9}\]  
\[\sum_{i=1}^{n}x_{k,f,b,i}=1,\forall k,f,b, \tag{10}\] \begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(G\) & Total weight of packages \\ \(m\) & Number of express cabinet \\ \(s_{j}\) & The \(j^{th}\) station \\ \(S=\{s_{0},s_{1},\ldots,s_{m}\}\) & Set of stations \\ \(d=\{s_{j},s_{j}\}\) & Distance between \(s_{j}\) and \(s_{j}\), \\ \(r\) & Radius of area \\ \(t\) & Initial temperature of SA framework \\ \(S^{intr}\) & The scheduling scheme \\ \(T_{k}\) & Number of flights for \(u_{k}\) \\ \(T_{k,f}\) & Number of packages delivered by \(u_{k}\)’s \(f^{th}\) flight \\ \(p_{k,f,b}\) & Number of \(b^{th}\) package on \(u_{k}\)’s \(f^{th}\) flight \\ \(l_{k,f,b}\) & Destination of \(b^{th}\) package on \(u_{k}\)’s \(f^{th}\) flight \\ \(t_{k,f}\) & Start time of \(u_{k}\)’s \(f^{th}\) flight \\ \(q_{k,f}\) & Task execution duration of \(u_{k}\)’s \(f^{th}\) flight \\ \(t_{k}^{e}\) & Time for \(u_{k}\) to complete all its tasks \\ \(A\) & Customer satisfaction \\ \(B\) & Total completion time \\ \(B^{intr}\) & Maximum total completion time \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._\[\sum_{k=1}^{h}\sum_{f=1}^{T_{k}}\sum_{b=1}^{T_{k,f}}x_{k,f,b,i}=1,\forall i, \tag{11}\]  
\[x_{k,f,b,i}\in\{0,1\},\]  
Equations (1) and (2) describe how optimization objectives are calculated. Customer satisfaction \(A\) is significantly more important than total completion time \(B\). Constraint (3) is the weight limit of packages. Constraints (4-5) ensure the maximum payload and flight distance of UAVs. Equations (6)-(9) present the calculation methods of the relevant parameters. \(x_{k,f,b,i}\) is a decision variable, taking \(1\) if \(b^{th}\) package on \(u_{k}\)'s \(f^{th}\) flight is \(p_{i}\), taking \(0\) otherwise. That taking \(1\) means loading the package, and taking \(0\) means unloading the package. Constraints associated with decision variables are \(10\)-\(11\).
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 3 LSTM-CRF Model with the Integrity Algorithm  
The proposed element recognition method based on the LSTM-CRF model is mainly composed of the LSTM-CRF model and integrity algorithm. This section starts by giving an overview of our model. Next, we will describe these two parts of the proposed method in detail.
# Magnetic Resonance-Electrical Properties Tomography by Directly Solving Maxwell's Curl Equations
###### Abstract  
Magnetic Resonance-Electrical Properties Tomography (MR-EPT) is a method to reconstruct the electrical properties (EPs) of bio-tissues from the measured radiofrequency (RF) field in Magnetic Resonance Imaging (MRI). Current MR-EPT approaches reconstruct the EP profile by solving a second-order partial differential wave equation problem, which is sensitive to noise and can induce large reconstruction artefacts near tissue boundaries and areas with inaccurate field measurements. In this paper, a novel MR-EPT approach is proposed, which is based on a direct solution to [PERSON]'s curl equations. The distribution of EPs is calculated by iteratively fitting the RF field calculated by the finite-difference-time-domain (FDTD) technique to the measured values. To solve the time-consuming problem of the iterative fitting, a graphics processing unit (GPU) is used to accelerate the FDTD technique to process the field calculation kernel. The new EPT method was evaluated by investigating a numerical head phantom, and it was found that EP values can be accurately calculated and were relatively insensitive to simulated RF field errors. The feasibility of the proposed method was further validated using phantom-based experimental data collected from a 9.4 Tesla (T) Magnetic Resonance Imaging (MRI) system. The results also indicated that more accurate EP values could be reconstructed from the new method compared with conventional methods. Moreover, even in the absence of phase information of the RF field, the proposed approach is still capable of offering robust EPT solutions, thus having improved practicality for potential clinical applications.  
e _Article_  
Magnetic Resonance-Electrical Properties Tomography (MR-EPT) 13 March 2020; 10 May 2020
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 5 Further Research and Improvement Possibilities
### Algorithms Refinement  
In the case of project rollout, many more HPUs will be connected to MOLOS.CLOUD. More devices equals more data. More data equals more reliable hypotheses. For now, reference characteristics were determined based on only one HPU. From a statistical point of view, it is definitely not enough. Nevertheless, industrial practice often does not allow performing innovative projects at scale without proof of concept (PoC).  
More data could give fresh insight into data. New dependencies, trends, and features could be extracted. More models could be trained and, as a result, more anomalies could be shown.  
Ideally, when new HPUs are plugged in, automatic model retraining should take place. The timer-triggered Azure Function can train models on new data. As a result, new models in the form of *joblib files and JSON files with polynomial coefficients for each characteristic are uploaded to Azure BLOB storage. There are other methods of iterative model retraining in Azure, but discussion on it is beyond the scope of this article [88].  
A summary of the architecture discussed in this paragraph is shown in Figure 11.  
Algorithm refinement could also be investigated in the context of used input variables. More variables could be considered as input to anomaly detection algorithms. This includes variables already measured, as well as potential new variables measured from additional sensors. The solution is deployed in production and for that reason, it is not possible to modify algorithms and to change their input variables. However, for future deployments, it is not excluded to expand the solution with additional sensors after detailed consultation with domain experts from PONAR Wadowice. To be coherent with the solution developed so far, potentially new sensors would also be preferably connected to the PLC input module.  
Figure 10: IoTEdge ecosystem enriched in ML model deployed as Docker container.
## 4 Materials and Methods
### Bacterial Cultivation and Inoculation  
Paraburkholderia phytofirmans PsJN (DSMZ, Germany) (here, referred to as PsJN) was routinely grown on Luria-Bertani (LB) media (1.2% agar _w/v_ with no salt). A day before inoculation, a single bacterial colony from an agar plate was aliquoted into an LB broth, and the bacterial solution was cultured overnight in an orbital shaker (150 rpm) at 30 \({}^{\circ}\)C. The cell suspension was measured for optical density (OD600) using a portable spectrophotometer and when an OD600 value of 0.8 (equivalent to 108 colony-forming unit (CFU) mL\({}^{-1}\)) was reached, cells were centrifuged down, washed, and serially diluted with Hoagland media to obtain a concentration of 104 CFU mL\({}^{-1}\). A study by [PERSON] et al. 2013 [PERSON] et al. (2013) reported this bacterial concentration as the optimum for plant-growth promotion in Arabidopsis. Inoculation was performed by pipetting 10 \(\upmu\)L of either PsJN-bacterial inoculum (inoculated) or Hoagland solution as mock inoculant (control) onto each seed. After all of the seeds received appropriate treatments, the black collars with seed-sown holes were sealed with parafilm, and the inoculated and control plates were transferred to separate growth chambers with varying temperatures.
# A Feature Extraction Method Using Auditory Nerve Response for Collapsing Coal-Gangue Recognition
## 1 Introduction
Although the above studies, based on acoustic and vibration signals, have achieved good results under certain conditions, practical applications in the top-coal caving process are hindered by the high background noise and the complex environment of an underground coal mine. Up to now, it still depends on workers to distinguish coal and gangue by hearing impact sounds of the hydraulic support. Considering that the recognition ability of the human auditory system shows superior robustness as compared with conventional automatic recognition technology [14], we propose a new feature extraction technology integrated with an auditory nerve (AN) response model, which simulates the human auditory system. The primary contributions of the work can be summarized as follows:  
(1) Inspired by the important roles of auditory in coal-gangue recognition during an actual top-coal caving process, we proposed an original feature extraction method integrated with an auditory nerve (AN) response model and auditory spectrum. In this method, the AN response model was used to process coal and gangue's acoustic pressure signals, and each characteristic frequency of such model's output constituted the auditory spectrum. The distribution properties of the auditory spectrum were analyzed, and the effective statistical features were extracted. (2) Because each type of feature behaves differently at various frequencies, we proposed a feature selection method integrated with variance to reduce noise and redundant information of original feature set and verified the effectiveness of this dimensionality-reduction method. (3) Through the analysis of the AN-based support vector machine (SVM) recognition results and comparison experiments, we demonstrated that more accuracy and generality than traditional extracting feature methods on the basis of signal processing have been achieved in this proposed method. The rest of this paper is organized as follows: In Section 2, we illustrate vibrational signals' acquisition and preprocessing; in Section 3, we introduce an AN response model and feature extraction and selection based on auditory spectrum; in Section 4, we describe the experimental results of the proposed method and its prediction results are compared with that of the recognition methods based on other commonly used features; finally, the conclusions we reached are described in Section 5. Figure 1 shows the proposed coal-gangue recognition framework on the basis of AN response.
## 1 Introduction
In the ETAM, coherent simulation results (obtained based on a set of equispaced phase thicknesses added to the initial thickness of the incoherent layer) were averaged to optically model the mixed coherent-incoherent multilayer structure. However, the above-mentioned optical modeling methods, which can only consider the effects of the incoherent layer on the absorption characteristics, were also insufficient to obtain the realistic J-V characteristics of a CIGS solar cell owing to their limitations in the electrical model. In addition, it has yet to be investigated how the optical effect of the front incoherent layer affects the J-V characteristics of a CIGS solar cell. In this study, we investigated the effects of the incoherent front cover glass on the J-V characteristic of a CIGS solar cell. An incoherent cover glass was optically modeled as a 1-\(\upmu\)m initial layer plus a set of equispaced phase layers (EPLs), the thicknesses of which are determined based on the ETAM. Both the optical and electrical characteristics of a CIGS solar cell were numerically calculated using an FEM-based integrated optoelectronic model. The changes in the optical power dissipation, absorptivity, and electron-hole pair generation rates were calculated depending on the variation of the equispaced phase thickness of the EPL. To obtain the ETAM-applied calculation, which includes the incoherent property of a thick cover glass layer, coherent optical simulation results were averaged over 10 equispaced phase thicknesses of the EPL. By comparing the J-V characteristics of the CIGS solar cell calculated between coherently and incoherently modeled cover glass layers, we calculated the percent of deviation of the short-circuit current density (J\({}_{\text{SC}}\)) and open-circuit voltage (V\({}_{\text{OC}}\)), which can be considered to be the inaccuracy of the calculation when a thick cover glass layer is inappropriately modeled as a coherent layer.
## Materials and Methods
### Catalyst Characterization  
Scanning electron microscopy (SEM) was used to observe the surface microstructure (XL-30 ESEM, Phillips, Amsterdam, The Netherlands). Energy dispersive spectroscopy (EDS) was carried out on an X-MAXN 80 TLE windowless EDS (Oxford Instruments, Oxford, UK) toget the dispersion of the metal on the catalyst surface by EDS elemental mapping. Specific surface areas, pore volume, and pore size distribution of the catalyst were measured by nitrogen absorption at 77 K (Micrometrics ASAP-2020 Analyzer, Micro-merities, Norcross, GA, USA) using the Brunauer-Emmet-Teller (BET) method. Before each adsorption measurement, approximately 0.3 g of the sample was degassed in N\({}_{2}\) at 300 \({}^{\circ}\)C for 4 h. X-ray powder diffraction (XRD) was performed to elucidate the crystal phase structure (Rigaku D/MAX RA) with Cu K\({}_{\alpha}\)radiation operated at 40 kV and 100 mA. The intensity data were collected in the range of 10-80\({}^{\circ}\). Photoelectron spectroscopy (XPS) was used to analyse the surface atomic state of the catalyst with Al K\({}_{\alpha}\) X-rays (Thermal ESCALAB 250, Thermo Fisher Scientific, Waltham, MA, USA). The sample charging effects were compensated by calibrating all binding energies (BE) with the adventitious C1s line at 284.6 eV. The concentrations of Mn, Ce, O, and S on the catalyst surface were calculated from the peak intensity. Thermogravimetry differential thermal analysis (TG-DTA) of the samples was performed (NetzschThermoAnalyzer STA 449C, Netzsch, Waldkraiburg, Bavarian State, Germany) with a heating rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) in flowing air. Fourier transform infrared (FT-IR) spectroscopy was performed using a Bruker Vector Fourier transform infrared spectrometer. The FT-IR spectra were recorded over the 800-4000 cm\({}^{-1}\) range to determine the sulfate and ammonium groups on the catalyst surface.
## 4 Results
### Evaluation Metrics  
Precision, recall, and mean average precision (mAP), which are standard metrics in object recognition, were used to evaluate model performance. Precision (P) is the proportion of the model's true positive (TP) predictions out of all positive predictions. Recall (R) measures how successfully the model detects all true positive samples. Using the AP metric, which summarises the precision-recall curve and measures the precision at various recall levels for a given class, we found the average accuracy metrics (mAPs) of the model across all classes for different IoU thresholds. To evaluate the detection accuracy of the model, the overall average precision at the IoU (Intersection Over Union) threshold of 0.5, shown at [EMAIL_ADDRESS], was used.  
In addition, FPS (frames per second), GFLOPs (giga floating point operations per second), model size, and number of parameters were considered to evaluate the real-time performance and computational efficiency of the model. Model size and number of parameters affect both memory footprint and inference time. GFLOPs are a measure of computational complexity, helping to compare the resource requirements for inference on different hardware platforms. FPS indicates how many image frames can be inferred in one second. Inference time refers to the time it takes the model to make a detection in one frame of image.  
In this study, the inference time is taken as the sum of the pre-process, inference, and NMS times for each image as the average of the test image inferences. In addition, as an indicator of a lightweight and efficient green deep learning model, the carbon footprint generated during the running of the models was measured. The total carbon footprint during inference from test images was calculated by multiplying the energy consumption of the hardware (Watts), the execution time (hours), and the emission factor (kgCO\({}_{2}\)e/kWh) using the Python language CodeCarbon library ([[https://github.com/mlco2/codecarbon](https://github.com/mlco2/codecarbon)]([https://github.com/mlco2/codecarbon](https://github.com/mlco2/codecarbon)) (accessed on 23 July 2024)).
# A Review of Sharp-Switching Band-Modulation Devices
## 5 Alternative Applications of Sharp Switching
### Bio-Sensing  
The generic _NPNP_ structure and overall functionality of a band-modulation device can be achieved by omitting the back-gate action and instead using surface charges attached to the ungated region (Figure 22a). Negative surface charges would induce the necessary _P_-type electrostatic doping that, together with V\({}_{\text{FG}}\) > 0, gives rise to _S_-shaped characteristics. The sensing parameter is again the turn-on voltage V\({}_{\text{ON}}\), which is highly dependent on the amount of surface charge. In Figure 22b, a very small variation in charge density (10\({}^{11}\) cm\({}^{-2}\)) leads to a shift of V\({}_{\text{ON}}\) as large as 1 V, showing outstanding detection capability.  
The region of maximum sensitivity can be adjusted by modifying the length of the ungated region: _long_ for small charge concentrations, or _short_ for higher concentrations (Figure 22b). A complementary device, able to sense positive charges, can be designed by relocating the gate next to the cathode (right-hand scheme in Figure 22a). With V\({}_{\text{FG}}\) < 0, the NPNP configuration is preserved.
## 3 Results and Discussion  
### Validation  
#### 3.1.1 Numerical Results  
Figure 4a shows that the two data sets were consistent in INT. The slight difference in \(e\) and \(m\) was \(D_{0}\). \(DI\) in DL1 and DL2 was calculated as \(D\)/\(D_{0}\). The strain differences between \(e\) and \(m\) of DL1 and DL2 are illustrated in Figure 4b,c. The \(DI\) of DL1 and DL2 was 31.3 and 98.6, respectively, which indicated that the calculated \(DI\) effectively distinguished the damage severity in the DL1 and DL2 scenarios.
# In-Band OSNR Measurement Method for All-Optical Regenerators in Optical Domain
## 1 Introduction
And then, the output OSNR of the optical regenerator is simulated and compared with the calculated value by the EVM in Section 4. The applicability of the in-band measurement method at different data rates is also investigated and discussed. Finally, Section 5 draws the conclusions of the paper.
# Retrieval of Chemical Oxygen Demand through Modified Capsule Network Based on Hyperspectral Data
## 4 Conclusions  
The capsule network is a recently proposed deep learning network, and only limited studies have explored its potential applications. Motivated by the novelty of the capsule network, this study attempted to use it for hyperspectral regression. Herein, a modified three-layer capsule network for water COD prediction based on 1-D hyperspectral data was provided. It was tested using a real dataset collected in the Baiyangdian area in North China. In addition, a comparable BP network architecture was designed to compare with the capsule network. The capsule network achieved better results, with a limited training set of samples. Moreover, the architecture of the original capsule network was largely simplified because the authors found it unnecessary to use many channels and large kernels. It was also demonstrated that denoising is not necessary for our model, making the model more universally applicable. To the authors' knowledge, the complexity of the capsule network has not been well explored, and further efforts should be devoted to investigating its potential. The fact that this simple capsule network gave unparalleled performance in COD prediction is an early indication that this method is very useful for 1-D regression problems.  
Conceptualization, C.D. and L.Z.; methodology, C.D.; software, C.D.; validation, C.D.; formal analysis, C.D.; investigation, C.D.; data curation, C.D.; writing--original draft preparation, C.D.; writing--L.Z. and Y.C.; visualization, C.D.; supervision, L.Z.; project administration, L.Z.  
This work was supported by the National Natural Science Foundation of China (No.41830108).  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \(R^{2}\) & **MSE** \\ \hline Original spectrum & 0.7812 & 0.0112 \\ Removing bands near 1400 nm, 1900 nm and 2500 nm & 0.7513 & 0.0128 \\ Removing bands near 1900 nm and 2500 nm & 0.7562 & 0.126 \\ Removing bands near 2500 nm & 0.7528 & 0.0127 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results of removing some bands from the original spectra (coefficient of determination, \(R^{2}\); mean square error, MSE).  
Figure 9: (**a**) Original spectra and (**b**) Denoised spectra used as inputs to the capsule network model in this study.  
**Acknowledgments:** We thank [PERSON], [PERSON], [PERSON] for their effort on the collection of water samples in Baiyangdian area. We thank [PERSON] from Liwen Bianji, Edanz Editing China for Editing the English text of a draft of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.