esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Typical OES Emitted from LS-APPJ  
OES and temporally-spatially resolved OES can be employed to record the behaviors of active species and plasma dynamics [24; 25]. Figure 6 shows the typical OES emitted from the BPPJ in range of 300-480 nm and 575-800 nm to detect the active species produced in the plasma jet. The pulse peak voltage, pulse repetition rate, and gas flow rate are kept at 24 kV, 150 Hz, and 3.5 L/min, respectively. The emission spectra are mainly composed of the spectral lines of He (3p\({}^{3}\)P\({}^{\circ}\to\) 2s\({}^{3}\)S, 388.9 nm), He (3p\({}^{1}\)P\({}^{\circ}\to\) 2s\({}^{1}\)S, 501.6 nm), He (3d\({}^{3}\)D \(\to\) 2p\({}^{3}\)P\({}^{\circ}\), 587.6 nm), and He (3d\({}^{1}\)D \(\to\) 2p\({}^{1}\)P\({}^{\circ}\), 667.8 nm); the spectral lines of oxygen atoms O (3p\({}^{5}\)P \(\to\) 3s\({}^{5}\)S\({}^{\circ}\), 777.4 nm); the bands of OH (A\({}^{2}\Sigma\to\) X\({}^{2}\Pi\)); the second positive system of nitrogen N\({}_{2}\) (C\({}^{3}\Pi_{\rm u}\to\) B\({}^{3}\Pi_{\rm g}\)); and the first negative system of nitrogen ions N\({}_{2}\)\({}^{+}\) (B\({}^{2}\Sigma_{\rm u}^{+}\to\) X\({}^{2}\Sigma_{\rm g}^{+}\)). The presence of these spectra indicates that excited species, such as O, OH, N\({}_{2}\) (C\({}^{3}\Pi_{\rm u}\)), N\({}_{2}\)\({}^{+}\) (B\({}^{2}\Sigma_{\rm u}^{+}\)), and He, coexist in the plasma jet. Table 1 lists the main physiochemical processes and their rate constants in the plasma jet for the production of the above active species.  
The effect of the pulse peak voltage on the emission intensities of N\({}_{2}\) (C\({}^{3}\Pi_{\rm u}\to\) B\({}^{3}\Pi_{\rm g}\)), N\({}_{2}\)\({}^{+}\) (B\({}^{2}\Sigma_{\rm u}^{+}\to\) X\({}^{2}\Sigma_{\rm g}^{+}\)) and He (3s\({}^{3}\)S \(\to\) 2p\({}^{3}\)P, 706.5 nm) is investigated next, and the results are illustrated in Figure 7. It is clearly seen that all emission intensities rise with the increase in the pulse peak voltage, because the mean kinetic energy of electrons increases in a stronger electric field owing to the increase in voltage, and more excited particles are produced. Notably, the emission intensity of N\({}_{2}\)\({}^{+}\) (B\({}^{2}\Sigma_{\rm u}^{+}\to\) X\({}^{2}\Sigma_{\rm g}^{+}\)) presents the most significant growth trend. Furthermore, the intensities emitted from the BPPJ are obviously higher than those emitted from the PPPJ and NPPJ for all three particles. This indicates  
Figure 5: Plasma jet length of BPPJ, PPPJ, and NPPJ varying with pulse peak voltage.  
Figure 6: Typical OES in range of 300–480 nm (**a**) and in range of 575–800 nm (**b**) emitted from BPPJ.  
that a bipolar pulse voltage is more beneficial for the excitation of a plasma jet compared to a unipolar pulse voltage.  
The reaction rate constants are calculated when the gas temperature is 300 K.
# High-Throughput Bit-Pattern Matching under Heavy Interference on FPGA
## 3 Core Circuit Description
### Lesser Pattern Identifier  
We assume that the entire input pattern is \(n\) bits long. We xnor the input pattern with one memory entry (respective bit positions are fed to xnor gates). Then we break down these bits in \(q\) symbols of \(m\) bits each, so we have \(q\times m=n\). This fragmentation is not aimed at creating sub-patterns. It is a strategy, the ultimate purpose of which is to enable the circuit to perform pattern matching at maximum frequency. Each symbol of the \(m\) xnored bits is then driven through a pipelined adder (Section 2.1). The sum of the xnor outputs gives us an estimation of how close the input pattern to our pattern is. After the adder tree, each sum is passed through a comparator. If the sum is equal to the number of bits per symbol (\(m\)), then the output of the comparator becomes 1, indicating that the correct symbol was detected. Then, by checking the output of the comparators, we can determine how many symbols were detected. If all the \(m\) symbols were detected, then the pattern was detected. An implementation of it is shown in Figure 2 for \(q=3\) and \(m=5\). All values are marked on the wires of the circuit for convenience and clarity. The number that the comparators use to determine whether the symbol was detected or not is known as the weight of the lesser identifier. In Figure 2, the weight of the identifier is 5.  
It is clear that the lesser pattern identifier alone cannot be used to identify large patterns (\(n>100\)). If there are too many symbols (\(q>>1\)) then the outputs of the comparators are too many to check in one cycle, thus increasing the critical path. If the symbols are too large, the adder trees require adders comprising many bits, which increases the critical path. For these reasons, the actual pattern matching will be performed using the cascaded pattern identifier.
# Acoustic Roughness Measurement of Railhead Surface Using an Optimal Sensor Batch Algorithm
###### Abstract  
Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict noise around tracks. Conventional systems developed to measure surface roughness have large deviations in measured values or low repeatability. The recently developed automatic mobile measurement platform known as Auto Rail Checker (ARCer) uses three displacement sensors to reduce measurement deviation and increase the accuracy of existing systems. This paper proposes enhancing the chord offset synchronization algorithm applied to the existing ARCer for high measurement precision with only two displacement sensors. As a result, when the two sensor-based measurement algorithm was applied, the spectrum level at \(\lambda\) = 0.314 m, the wavelength amplification associated with wheel diameter, was reduced to at least 6 dB in comparison with that of the three sensors based algorithm. We also verified the accuracy of the proposed batch algorithm through a field test on an operating rail track with a corrugated rail surface.  
acoustic roughness; rolling noise; rail surface measurement; optimal sensor batch; chord offset measurement method +
Footnote †: journal: Applied Physics Letters A
# A2C: Attention-Augmented Contrastive Learning for State Representation Extraction
## 5 Results and Analysis
### Generalization  
There were one hundred environment settings used as the testing set to evaluate the generalization of RL. We recorded the number of collisions and SPL at each episode, which can be seen in Figure 7 and Table 4. A2C caused the fewest collisions and the smallest standard deviation, which indicates that the representation distilled from A2C provided more efficient obstacle information than others. Autoencoding did not achieve the same advanced performance as learning efficiency; even the number of collisions caused by it could reach about 250. If we regard autoencoding as a variant of attention-removed A2C (as mentioned above, in autoencoding, the raw image and the restored image can be regarded as a pair of similar pictures), lacking the attention mechanism may account for this phenomenon because removing the attention mechanism from A2C worsened the generalization performance of A2C. Hence, the attention mechanism is significantly helpful in capturing obstacle information.  
In terms of SPL, the absolute value of SPL cannot reflect the advantages and disadvantages of state representations, because navigating to the target is not only related to the state representation, but also related to the performance of the policy modules like GRU. Therefore, we paid more attention to their relative performance. On the one hand, as shown in Table 5, A2C did not achieve the largest mean SPL value, which shows that it was relatively unsuitable for storing spatial geometry information. However, on the other hand, different from navigating in the simulator, avoiding collisions should be given much more priority in real robotic control because collisions may damage the robot and abort the navigation process. Therefore, although curvature, normal, and denoise achieved better SPL performance than A2C, they may perform worse than A2C in reality because their mean value, standard deviation, and maximum value in Table 4 are significantly larger than A2C, which means a greater probability of collisions. Moreover, considering that A2C had the best learning efficiency, we argue that A2C is the best choice for state representation in reality.  
Figure 6: Loss curves of A2C and the attention-removed A2C in the state representation learning phase.
## 5 Experiments and Discussions
### Parameter Setting  
To test the feature extraction performance of the proposed scheme, the proposed scheme is compared with wavelet, Contourlet, DST, GLCM, etc. Some important parameters are listed as follows.  
DNST: The parameters chose [1 2], [0 1 2], etc. Take an example to explain parameters. When the parameter is set to [1 2], the first scale has eight shearlet direction filters, and the second scale has 16 direction filters, which produced a total of 24 high-frequency subbands and one low frequency subband. The number of DNST features is \((8+16+1)\times 2=50\).  
GLCM: The gray level of the original image was compressed to 8, 16, 24 levels, etc. The distance parameter was set to 1.  
Wavelet: The wavelet type chose \"Haar,\" \"db2,\" etc. The decomposition level was set to 2,3,4.  
Contourlet: The Laplacian filter chose \"9-7,\" and the directional filter chose \"pkva.\" The direction parameter chose [2 3 4], [3 4 5], etc.  
DST: The scale filter chose \"Symmlet\" with the fourth-order vanishing moment, scale parameter was set to [2 1 1], [2 1 1 0], etc. The directional parameters were set to [2 2 2], [2 2 3 3], etc.  
KSR: The kernel type chose \"Gaussian,\" and the kernel parameter was set to 0.001.  
We chose the radial basis kernel (RBF) as the kernel function of the SVM classifier, and the kernel parameter gamma \(\gamma\) was set to iterate through all values from 0 to 4 with step length 0.01. The other parameters took the default values. Our experiments were based on using a ThinkPad E440 PC equipped with a 2.29 GHz Intel i7 processor and 8 GB of RAM. The application software is MATLAB published by MathWork company.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 4 Photocatalytic Oxidation
### Mechanism
This recombination process reduces the efficiency of photocatalytic reactions because it decreases the number of electrons and holes available for participation in oxidation/reduction reactions that generate reactive oxygen species (ROS) [81]. Recombination of electron-hole pairs occurs within tens of nanoseconds. Hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), produced during the reactions, acts as an oxidizing agent capable of trapping photogenerated electrons, thereby diminishing the rate of charge carrier recombination. When the recombination of charge carriers occurs, it releases a certain amount of energy, which can be detected as a photoluminescence emission spectrum. There are two primary types of photoluminescence: band gap photoluminescence, which pertains to the separation of photogenerated charge carriers [82], and excitonic photoluminescence, which provides insights into oxygen vacancies and surface defects [83]. By analyzing excitonic photoluminescence, researchers can deduce other factors affecting photocatalysis, such as the efficiency of electron trapping and transfer. Consequently, the photoluminescence spectrum serves as an indicator of the recombination rate: a low intensity suggests minimal charge carrier recombination, indicating enhanced photocatalytic activity, while a high intensity signifies a high recombination rate [84]. For example, (Figure 1A), the photoluminescence spectrum of P25 TiO\({}_{2}\) reveals a high recombination rate, as evidenced by the peak's high intensity. In contrast, another sample (USPNT) shows significantly lower photoluminescence intensity, indicating a reduced recombination rate. The largest peak in the photoluminescence spectrum, often associated with band gap photoluminescence, corresponds to the radiation energy nearly equal to the band gap of the sample. Additional peaks within a specific wavelength range may correspond to excitonic photoluminescence associated with surface defects and oxygen vacancies. \begin{table}
\begin{tabular}{c c} \hline
**Reaction Type** & **Reaction** \\ \hline Photoexcitation & Semiconductors + hv \(\rightarrow\) h\({}^{+}\) + e\({}^{-}\) \\ Oxidation reaction & OH\({}^{-}\) + h\({}^{+}\) \(\rightarrow\) OH \\ Reduction reaction & O\({}_{2}\) + e\({}^{-}\) \(\rightarrow\) O\({}_{2}^{-}\) \\ Ionization of water & H\({}_{2}\)O \(\rightarrow\) OH + H\({}^{+}\) \\ Protonation of superoxide & O\({}_{2}^{-}\) + H\({}^{+}\) + HOO- \\ Formation of H\({}_{2}\)O\({}_{2}\) & HOO: + H\({}^{+}\) \(\rightarrow\) H\({}_{2}\)O\({}_{2}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Reactions occurring during photocatalytic oxidation [80]. In another example, the largest peak in the photoluminescence spectrum (Figure 1B) at about 395 nm is associated with the band gap photoluminescence and the corresponding radiation energy is almost equal to the band gap of the sample. The other four peaks between 440 and 500 nm correspond to the excitonic photoluminescence and are associated with the oxygen vacancies and defects of the sample surface.
## 2 Materials and Methods
### Molecular Analyses  
The RNA was extracted using an miRNeasy Mini Kit (Qiagen, Valencia, CA) according to the manufacturer's protocols. The quality and quantity of the RNA extracts were evaluated using agarose gel electrophoresis and a MaestroNano MN-913 (MaestroGen Inc., Las Vegas, NV, USA) spectrophotometer.  
The RNA was used as a template for the RT-qPCR assay to assess the _NFE2L2_ and _KEAP1_ gene expression. The reaction was performed using SYBR Green I chemistry (Sensi-FAST SYBR No-ROX One-Step, Bioline, London, UK) and a LightCycler(r) 480 Instrument II(Roche Life Science, Basel, Switzerland). Commercially available oligonucleotide-specific primers were used (Sigma-Aldrich, St. Louis, MO, USA) (Table 1). The reaction parameters were as follows: reverse transcription, 45 \({}^{\circ}\)C--10 min; activation, 95 \({}^{\circ}\)C--2 min; 45 cycles: denaturation, 95 \({}^{\circ}\)C--5 s; annealing, 60 \({}^{\circ}\)C--10 s; and extension, 72 \({}^{\circ}\)C--5 s. Finally, a melting curve analysis was performed. Each PCR was repeated three times with three replicates for each sample.  
To evaluate the expression of the selected miRNAs, a reverse transcription reaction was conducted using an miRCURY LNA RT Kit (Qiagen, Valencia, CA, USA). The cDNA samples that were obtained were used in a qPCR assay. The miRNA level of miR-28-3p, miR-29b-1-5p, miR-141-3p and miR-365a-3p was evaluated using a miRCURY LNA SYBR\({}^{\otimes}\) Green PCR Kit, LightCycler\({}^{\otimes}\) 480 Instrument II (Roche Life Science, Basel, Switzerland) and the appropriate miRCURY LNA miRNA PCR assays (Qiagen, Valencia, CA, USA). The following thermal profile was applied: activation, 95 \({}^{\circ}\)C--2 min; 45 cycles: denaturation, 95 \({}^{\circ}\)C--10 s; annealing/extension, 56 \({}^{\circ}\)C--60 s. Finally, a melting curve analysis was performed. All of the qPCR reactions were conducted in triplicate for each cDNA sample.  
The tested miRNAs that regulate the expression of _NFE2L2_ mRNA (miR-28-3p, miR-29b-1-5p and miR-365a-3p) and _KEAP1_ mRNA (miR-141-3p) were selected in silico using the miRTar database ([[http://mirtar.mbc.nctu.edu.tw/human/](http://mirtar.mbc.nctu.edu.tw/human/)]([http://mirtar.mbc.nctu.edu.tw/human/](http://mirtar.mbc.nctu.edu.tw/human/)) accessed on 18 January 2023) and also based on a previous report [29; 30].  
The relative expression levels of the mRNAs and miRNAs were calculated based on the 2\({}^{-\Delta\text{CT}}\) relative quantification method after normalization with the reference controls: for mRNA--\(\beta\)_actin_ and for miRNA--_RNU5G_[31]. Small nuclear RNA, such as _RNU5G_, is constitutively and relatively stably expressed across different human tissues and often used to evaluate the miRNA expression. RNU5G also showed a low variation among our samples.
## 3 Optimization
### Optimized Workflow Characteristics
#### 3.2.2 Physics Simulation  
A physics simulation was considered to increase the shadow rendering quality and include only viable part orientations in order to reduce the number of training images required to reach the maximum classification accuracy. Here, images of unlikely part orientations would be excluded by simulating the effect of gravity when dropping the parts onto a plane, mimicking a worker placing the parts. This is illustrated in Figure 9. The probability that a worker would place the pictured part on its smaller face is considerably lower compared to it being placed on one of its greater faces, as he would seek to balance it, which is unlikely in an industrial scenario. Note that fewer training images would result in lower rendering costs during the proposed workflow. As the computational effort  
Figure 8: Comparison of the different network structures’ classification accuracy on the test set. The networks were trained on different numbers of images for either five or twenty epochs.  
required for the physics simulation is neglectable compared to the image rendering process that includes realistic lighting and shadow casting, an integration could be beneficial to accelerate the workflow.  
The influence of including simulated gravity was evaluated by classifying the developed test set using the VGG-16 network trained on different numbers of training images with and without the physics simulation. In this evaluation, the parts were placed in varying orientations directly on the ground plate for the networks trained without the gravity simulation.  
Figure 10 shows the influence of including the gravity simulation on the classification accuracy on the test set for different numbers of images per part. Here, the classification accuracy distribution obtained with the gravity simulation is shown in red, and that obtained without the gravity simulation is shown in blue. As can be seen, the accuracy increased with an increasing number of images for both cases before slightly dropping again at 400 images. On average, the accuracy obtained with the gravity simulation was 5.38% higher. It further showed lower variation. Based on these findings, it was decided to include the physics simulation in the proposed workflow.  
Figure 9: The same part placed in an improbable orientation (**a**) standing up on a small face and in a probable orientation (**b**) laying down on a greater face on a ground plate, under the consideration of gravity.
# Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
## 3 Data Compression Techniques
### Adaptive Huffman Encoding  
Huffman coding involves an analysis of the source sequence's probability. In the case that this information is not easily accessible, the Huffman encoding process transforms into a two-step procedure: in the first step, the statistics are gathered, and in the second step, the source is encoded. Adaptive algorithms based on statistics of previously encountered symbols were created independently to transform this technique into a one-pass approach [35]. To encode the \((\mathrm{k}+1)^{\mathrm{th}}\) symbol, we might theoretically recompute the code each time a symbol is delivered by utilizing the Huffman coding process. However, because of the high amount of work required, this strategy would be impractical.  
Adaptive Huffman coding employs a technique where neither the transmitter nor the receiver knows the source sequence's statistics prior to transmission. Nodes in both transmitter and receiver have a weight of 0 and correspond to all symbols that have not yet transmitted (NYT). To keep track of the symbols that have been broadcast, the tree will be updated by adding new nodes to the tree as transmission occurs. Before the transmission begins, the transmitter and receiver agree on a set code for each symbol. In Adaptive Huffman coding, this process becomes a one-pass procedure. NYT are represented by a single node with a weight of zero in both the transmitter and receiver's trees. Symbols are added to the tree as they are broadcast, and the tree is rearranged using an update mechanism while the transmission continues. Each end of the signal has the same root node as the other. Both the transmitter and the receiver utilize the same method for updates. As a result, the encoding and decoding operations are kept synchronized [42].  
1. Encoding Procedure: Initially, there is just one node in the tree at both the encoder and the decoder, which is the NYT node. As a result, the very first symbol that emerges has a predetermined code-word. When encoding a symbol for the second time, we transmit the code for the NYT node, followed by the fixed code for the symbol that was already agreed upon, unless we are dealing with the very first symbol again. By following the Huffman tree from its root to the NYT node, we may retrieve its corresponding code. This notifies the receiver that the Huffman tree does not yet include a node corresponding to the symbol whose code is about to be received. If a symbol has to be encoded and there is an external node in the tree that corresponds to it, a path from the root node to the external node can be used to produce the symbol's code.
2. Update Procedure: To perform the update method, the nodes must be arranged in a predetermined order. Using node numbers, this order is maintained.  
Figure 3: The flowchart of the Hybrid Model.
## 1 Introduction
### Contribution and Paper Organization  
Under the framework of the TF masking method, we propose a novel underdetermined convolutional mixing UBSS algorithm for TF overlapped communication signals with an unknown number of sources. Firstly, the observed signals are converted to the sparse domain to obtain the linear clustering characteristic. Consequently, the effects of multipath and time delay are eliminated by calculating short-time spectral power; Secondly, the short-time spectral power ratio of the observed signals is converted into the angular distribution, and the number of source signals can be estimated through the KDE algorithm; Then the contribution degree of the source signal in the observed signal is obtained by the designed contribution degree function. Finally, the TF soft mask is constructed based on the contribution degree, which is employed to separate the mixed signals. The main contribution of the paper can be summarized in the following:  
* A source number estimation method based on the KDE algorithm followed by detecting the peak points of the distribution is proposed, which can effectively estimate the number of sources before the signal separation.
* We design a contribution degree function based on the cosine distance, by which the contribution degree of the source signal to the mixed signal can be obtained. The contribution degree clearly reflects the mixing degree of the source signal at each frequency point, and it is of significant importance to improve the separation accuracy.
* The TF soft mask matrix is constructed based on the contribution degree function. Compared with the traditional binary mask, the TF soft mask can improve the separation accuracy for TF overlapped signals.
* The proposed blind source separation method for TF overlapped communication signal extends the application of the SCA algorithm, which reduces the requirement of signal sparsity for the classical SCA blind source separation algorithm.  
The rest of this paper is structured as follows. The problem statement is given in Section 2, which includes the introduction of the effect on separation based on the classical SCA methods caused by TF aliasing. Subsequently, Section 3 describes the process of the algorithm in detail, which mainly consists of two parts: source number estimation and source signal recovery. Afterward, the separation performance of this algorithm is tested by the simulations in Section 4. Lastly, the conclusions are drawn in Section 5.