esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of NTP Treatment on Intracellular Reactive Species Generation  
Similarly, we used an ROS detection kit containing DCFH-DA fluorescent probes (Beydine, Shanghai) to detect the ROS entering the cell for 4 h incubation after NTP treatment. Unlike the results of [PERSON] et al. (25 s and 30 s treatment groups showed a substantial decrease in intracellular ROS content) [18], our study found that the intracellular ROS increased in a time-dependent manner with the extension of plasma treatment time compared with the control group (Figure 3A,B), which was in agreement with a time-dependent increase in the preceding extracellular ROS. Furthermore, the ROS effect validation experiment (Figure 1B) found that the cell viability of the NTP-treated group was similar to that of the control group. Therefore, combined with the drastic reduction in ROS in the culture medium for 4 h after NTP treatment, we concluded that the entry of low levels of extracellular ROS into the cells promoted cell viability.  
H\({}_{2}\)O\({}_{2}\) was an important component of ROS produced by NTP. In a previous study, low-dose H\({}_{2}\)O\({}_{2}\) in honey entered cells through aquaporin 3 to exert a wound healing effect [45], and ROS produced by low-dose NTP have been shown to promote fibroblast proliferation [18] and wound angiogenesis [24]. However, high concentrations of ROS produced by erianin inhibited the proliferation of HaCaT cells [44] and induced their apoptosis. In addition, ROS produced by high-dose NTP had an anti-tumor effect [46]. In this paper, our findings showed a time-dependent increase in intracellular ROS concentration with prolonged NTP treatment, consistent with the increasing trend of ROS concentration in the assay medium. Therefore, ROS generated by low-dose NTP acted as cell messengers within the cell and played a role in promoting cell viability, while ROS generated by high-dose NTP inhibited HaCaT cell viability, which was consistent with the results of previous studies [18; 24; 46]. However, the specific mechanism of ROS produced by NTP entering HaCaT cells to exert their effects remained to be further explored.  
Figure 2: Extracellular NO and H\({}_{2}\)O\({}_{2}\) content. (**A**) The concentration of NO in the cell culture medium after NTP treatment for 0, 5, 10, 15, 20 and 25 s. (**B**) The concentration of H\({}_{2}\)O\({}_{2}\) in the cell culture medium for 0 h and 4 h after NTP treatment for 0, 5, 10, 15, 20, and 25 s. Data represent the mean \(\pm\) SD of three independent experiments. “ns” was not considered statistically significant. * \(p<0.05\), **\(p<0.01\)**, ***\(p<0.001\), ***\(p<0.0001\) with ANOVA compared with the control.
## 2 Detection System Architecture and Signal Characteristics
### Received Signal Strength Characteristics Analysis  
Electromagnetic wave propagation loss in free space according to the Friis formula is:  
\[P_{r}=P_{t}D_{t}D_{r}(\frac{\lambda}{4\pi R})^{2}, \tag{1}\]  
where \(P_{r}\) is the received power, \(P_{t}\) is the transmit power, \(D_{t}\) is the gain of the transmit antenna, \(D_{r}\) is the gain of the receive antenna, \(R\) is the distance between the receiver and the transmitter and \(\lambda\) is the wavelength of electromagnetic wave.  
Due to electromagnetic waves have reflection, refraction, diffraction and other phenomena during the propagation. However, the doppler effect of electromagnetic waves which will increase reflection while increasing the received signal strength is not obvious when vehicle was running at low speed or static. In order to simplify the model, we only consider the reflection and refraction of electromagnetic waves, the simplified model is shown in Figure 3.  
According to the law of conservation of energy, we can get the following formula:  
\[P_{t}=P_{\mathit{refl}}+P_{\mathit{refr}}, \tag{2}\]  
where \(P_{\mathit{refl}}\) is the electromagnetic wave energy reflected by the vehicle. \(P_{\mathit{refl}}\) is the electromagnetic wave energy refracted by the vehicle. Substituting Equation (2) into Equation (1) we can get:  
\[P_{r}=\Big{(}P_{\mathit{refl}}+P_{\mathit{refr}}\Big{)}D_{t}D_{r}\big{(}\frac{ \lambda}{4\pi R}\big{)}^{2}, \tag{3}\]  
In Equation (3), the two parameters of \(R\) and \(\lambda\) remain unchanged. From Figure 3a, we assume that the receiver can fully receive the electromagnetic wave energy from the transmitter when no vehicle is present on the receiving antenna. In other words, the electromagnetic wave energy is not reflected by the vehicle. If there is a vehicle on the receiving antenna, the vehicle will reflect and refrac the electromagnetic waves, resulting in lower electromagnetic energy received by receiver than no vehicle above it. Therefore, when a vehicle enters the sensing area of the detector, the received signal strength (RSS) value sampled by the receiver will be changed significantly.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 3 Sources and Detectors for Lidar Imaging Systems in Autonomous Vehicles
### Photodetectors
#### 3.2.2 Photodetectors in Lidar Imaging Systems
Generally speaking, when fixed system noise is the limiting noise factor the performance of APDs is much better than devices with ordinary PIN photodiodes. However, increasing gain also increases the excess noise factor, so there exists an optimal operating gain for each operating condition, usually well below the actual maximum gain, where the maximum SNR performance can be obtained. Linear-mode APDs (in contrast to Geiger-mode APDs or SPADs, described next) present output signals amplified by a gain and proportional to the incoming light. Compared to PIN photodiodes, they have comparable bandwidth, but can measure lower light levels, and thus may be used in a variety of applications requiring high sensitivity such as long-distance communications, optical distance measurements, and obviously for lidar. However, they are not sensitive enough  
Figure 12: Schematic I-V curve in different photodetectors showing the different behaviour of gain. for single-photon detection. They are a mature and widely available technology, so APDs are also available in array form, in multiple sizes, in either 1D or 2D arrays, with photosensitive areas up to \(10\times 10\) mm, especially in Si. Large InGaAs arrays, on the contrary, are hard to find and prohibitively priced at present.
## 3 Genetic Algorithm for Gaussian Decomposition  
The returned laser pulses are distorted by the cross-section characteristics (i.e., the scattering properties of targets) even though the transmitted pulses are Gaussian functions. However, it is assumed that the returned laser pulse from a small grid follows a Gaussian distribution in the time domain and then the ELPW summing the returned pulses is also represented by a series of Gaussian functions [29]. According to the analysis work of returned waveform [38], more than 98% of the observed waveforms with a topographic lidar could be correctly fitted with the sum of Gaussian functions. Therefore, the ELPW can be fitted with a mixture of Gaussian functions as follows [39]:  
\[f(X,t)=\sum_{i=1}^{n}A_{i}\exp\left(-\frac{(t-\mu_{i})^{2}}{2\sigma_{i}^{2}}\right), \tag{23}\]  
where \(n\) is the number of Gaussian functions, and Gaussian parameters of \(X_{i}=\{A_{i},\mu_{i},\sigma_{i}\}\) are the amplitude, position and half-width of the \(i\)th Gaussian function, respectively.  
The Gaussian parameters are determined by the Gaussian decomposition so that Equation (23) fits the ELPW given by Equation (7) with a prescribed accuracy \(\varepsilon\) using some number of Gaussian functions:  
\[\zeta(X)=\frac{1}{N_{T}}\sum_{k=1}^{N_{T}}\left[P_{r}(t_{k})-f(t_{k})\right]^ {2}<\varepsilon, \tag{24}\]  
where \(t_{k}\) is the discrete time, and \(N_{T}\) is the total number of discrete time intervals. There are several methods for the Gaussian decomposition, e.g., non-linear least square approach, maximum likelihood estimate, and stochastic approach [38]. The genetic algorithm (GA) is employed for the Gaussian decomposition in this study, which is a parameter optimization technique inspired by natural evolution using the concepts of mating, mutation, crossover, and selection [40; 41].  
The GA is briefly addressed in this section because it is only used as a tool to find Gaussian parameters. The GA works by building a population of chromosomes which is a set of potential solutions to Equation (24). The fitness \(\zeta(X,t)\) of each chromosome in the population is evaluated and then a new population is created by repeating the following steps until the fitness of a new chromosome is satisfied: selection, crossover, and mutation. A probabilistic selection is performed based on the chromosome's fitness such that the better chromosomes have an increased chance of being selected to reproduce into the next generation. The selected chromosomes, generally called parents, are to be mixed and recombined for the production of offspring in the next generation. In order to facilitate the GA evolution cycle, there are two fundamental operators: crossover and mutation. Based on the crossover and mutation probabilities, crossover takes two chromosomes and produces two new offsprings while mutation alters one chromosome to produce a single new offspring. A more complete explanation of the GAs can be found in the book by [PERSON] [40]. The Gaussian decomposition based on the GA was implemented using the Matlab toolbox [42] and the flowchart of the GA process is shown in Figure 1.
###### Abstract  
The Asynchronous Transfer Mode (ATM) is an efficient technology for call relays, and it transmits information from multiple services including data, video, or voice. This information is conveyed at ATM multiplexers in small fixed-size packets called _cells_. The acceptable cell loss probability at ATM multiplexers is about \(10^{-12}\). Important Sampling (IS) is an efficient method for estimating tiny probabilities that cannot be achieved by traditional Monte Carlo (MC) methods. This research presents a novel approach for evaluating the tail probability in the MMPP/D/1 queue system utilizing importance sampling simulation in the ATM network. To generate more rare events, a virtual queue is implemented in the dequeue process by decreasing the processing rate in the queue. In this way, the tail probability can be estimated on a real-time network.  
Monte Carlo; importance sampling; MMPP/D/1 queue +
Footnote †: This paper is an extended version of our paper published in Proceedings of the Ninth International Symposium on Information and Communication Technology-SoICT 2018.
## 1 Introduction
In a survey conducted in London, 80% of respondents considered the final disposition of medications a problem when they are discarded into the waste and domestic sewage systems. According to the World Health Organization ([PERSON], 2016), this occurs in many countries due to the following occurrences--medical consultations that generate drug prescriptions (50-70%); medicines prescribed or dispensed in an inadequate manner (50%); prescription of erroneous antibiotics (75%); sale of antibiotics without a medical prescription (75%); and the use of medications for only a single day of treatment (50%). Similarly, according to [PERSON] et al. (2016) research shows that many patients discontinue drug treatment and even self-medicate with other drugs, which further increases the generation of drug waste. On the other hand, concern about the impact of medicinal waste upon the environment has been increasing. In a Dutch survey, [PERSON] et al. (2015) found that 80% reported concern about the effects of medicinal waste on the environment and, as a result, returned medications to pharmacies or chemical waste facilities. Pharmaceuticals of various therapeutic classes, including antibiotics, hormones, and anti-inflammatory medications, have been detected in domestic sewage, surface water, and groundwater, in concentrations that range from nanograms to milligrams per liter ([PERSON], 2016). There are intriguing reports in the literature that have documented the ecologically-relevant effects on a range of target organisms, due to exposure to sublethal concentrations of pollutants, including antibiotics. The occurrence of such residues in the environment contribute to the resistance of microorganisms to antibiotics, in addition to resulting in toxicological problems for certain living organisms ([PERSON], 2016). Twelve pharmaceutical products that included antibiotics, painkillers, antiepileptics and lipid regulators, were detected and analyzed in water samples, from three major urban rivers in China, and an algae growth inhibition test was conducted as part of a preliminary evaluation of the ecotoxicology, induced by pharmaceutical products in the rivers. The concentrations of caffeine and paracetamol were relatively high; the preliminary assessment of ecotoxicological risk found that the presence of azithromycin, clarithromycin, and caffeine may create an ecotoxicological risk for urban rivers ([PERSON], 2011). A study that evaluated the destination and ecotoxicity of various cytostatic compounds in the environment, revealed that urinary excretion was the main contributor to various cytostatic wastes, within the water cycle ([PERSON], 2011; [PERSON], 2011). Another study that was conducted by [PERSON] and collaborators ([PERSON] and others, 2010), evaluated the occurrence and treatment of five estrogenic hormones and ten beta blockers in wastewater treatment plants, in fourteen rural and urban areas, in France. According to the authors, the analysis of free and total estrogens showed that more than 84% of the estrogens, in the dissolved phase of the samples, were in the free form.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 3 Materials and Methods
### Loss Function  
In this research, there are many parameters of the haze restoration network model. We chose the loss \(L_{2}\) to effectively train the network. The loss \(L_{2}\) is the mean square loss, which has a relatively stable solution and can converge more effectively. Formula (10) explains the calculation.  
\[L_{2}=\frac{1}{N}\sum_{x=1}^{N}\sum_{i}(f_{i}(x)-J_{i}(x))^{2},(i=2,3,4,5,6,7,8, 11,12) \tag{10}\]  
\(f_{i}(x)\) and \(J_{i}(x)\) represent the dehazing image and the real haze-free image, respectively, \(i\) represents the band and N represents the number of image pixels.
# Natural Photosensitizers in Clinical Trials
## 4 Natural Photosensitizers Pending Clinical Trials
### Pheophoride a  
Pheophoride a is a catabolite of chlorophyll obtained mainly from algae [88; 89; 90]. This compound is characterized by photoactive properties, so it has been tested as a photosensitizer [91]. ROS generated by the combination of PDT and the photosensitizer in question induce damage to various cellular structures, leading to apoptosis of cancer cells. Pa-PDT also significantly increases the levels of pro-apoptotic proteins, while decreasing the levels of anti-apoptotic proteins. Pa-PDT not only promotes apoptosis, but also inhibits the proliferation, migration, and invasion of cancer cells. It works by inhibiting EMT and reducing the expression of matrix metalloproteinases, which are crucial in the process of tumor invasion [92; 93].  
Previous work has demonstrated its potential for use in photodynamic therapy for uterine sarcoma, prostate cancer, hepatocellular carcinoma, and glioma [92; 94; 95]. Pa-PDT was tested in vivo for the treatment of Hep3B hepatocellular carcinoma xenografts in nude mice. The therapeutic regimen consisted of intravenous administration of Pa at a dose of 300 ug/kg and subsequent light irradiation at 126 J/cm\({}^{2}\) and 610 nm wavelength for 30 min after 24 h. It was shown that after 14 days, Pa-PDT significantly reduced tumor size by 57% compared to the control group [96]. Another study tested Pa-PDT in the treatment of MCF-7 breast cancer cell xenografts in mice. The therapeutic regimen consisted of intravenous administration of Pa at a dose of 2.5 mg/kg and subsequent 6 h irradiation of the tumors with a diode laser at 675 nm \(\pm\) 3 nm at a dose of 90 J/cm\({}^{2}\). On day 15, the size of tumors in the Pa-PDT group was on average 50% smaller compared to the control groups. In two mice (15.4%) in the Pa-PDT group, the tumor had completely regressed [97]. The study by [PERSON] et al. compared intravenous administration of this PS with intraperitoneal administration. One week after inoculation of oral squamous cell carcinoma cells with AT-84, one test group of mice was administered Pa at a dose of 10 mg/kg intravenously into the tail, while the other was administered Pa at a dose of 30 mg/kg intraperitoneally. After 24 h, irradiation was carried out at a wavelength of 664 nm The light dose was 100 J/cm\({}^{2}\). After 15 days of treatment, the tumor volume in the group that received Pa intraperitoneally decreased by about 70% compared to the control group at the end of the study period. In the group with intraperitoneal administration of Pa, tumor growth inhibition was 43.4% compared to the control group [98].
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 5 Methodology  
### Wnmf  
WNMF uses the non-negative measurement data matrix \(A\in\mathbb{R}^{m\times n}\) shown in Figure 6. Here, let the set of measurement points be \(O=\{o_{1},o_{2},\ldots,o_{m}\}\) and the set of parameters constituting the measurement data be \(P=\{p_{1},p_{2},\ldots,p_{n}\}\). \(a_{ij}\in\{a_{11},a_{12},\ldots,a_{nm}\}\) represents the value of parameter \(p_{j}\) measured at the measurement point \(o_{i}\). Values of parameters not measured are incorporated into matrix \(A\) as missing values (=0). In WNMF, this matrix \(A\) is approximately decomposed into matrices \(U\in\mathbb{R}^{m\times k}\) and \(V\in\mathbb{R}^{k\times n}\), and their inner product is taken to predict the missing values (values indicated as 0 in matrix \(A\)). This can be represented by Equation (3) as:  
\[A\approx UV\]  
Figure 5: Various time-related graphs and Fourier spectrum: (**a**) time–voltage graph; (**b**) time–acceleration graph; (**c**) time–velocity graph; (**d**) example of Fourier spectrum.  
Here, \(k\) denotes the rank, which is smaller than \(m\) or \(n\). Generally, as the rank increases, the prediction accuracy of the missing values increases, and as the rank decreases, the prediction accuracy decreases. However, too large a rank may increase the computational load and reduce prediction accuracy. Therefore, it is important to choose an appropriate rank when executing WNMF. WNMF is a learning method that aims to improve prediction accuracy for missing values by using only measured value elements [30]. WNMF learns matrices \(U\) and \(V\) by solving the optimization problem defined by Equation (4):  
\[\min_{U,V}\|W\circ(A-UV)\|_{F}^{2} \tag{4}\]  
Here, initial values of matrices \(U\) and \(V\) are given randomly. Furthermore, \(\circ\) represents element-wise matrix multiplication, and \(\|\cdot\|_{F}\) denotes the Frobenius norm of a matrix. The matrix \(W\in\mathbb{R}^{m\times n}\) represents a weight matrix and is defined by Equation (5):  
\[w_{ij}=\begin{cases}1&\text{if }a_{ij}\in A^{o}\\ 0&\text{if }a_{ij}\in A^{u}\end{cases} \tag{5}\]  
Here, \(A^{o}\) represents the set of measured values contained in matrix \(A\), and \(A^{u}\) represents the set of missing values contained in matrix \(A\). Thus, when \(w_{ij}=1\), it indicates that it is used for learning, and when \(w_{ij}=0\), it indicates that it is not used for learning. The optimization problem in Equation (4) can be solved by updating matrices \(U\) and \(V\)\(t\) times until they converge, using Equations (6) and (7):  
\[u_{ik}^{(t)}=u_{ik}^{(t-1)}\frac{(W\cdot A^{(t-1)})V_{ik}^{(t-1)T}}{(W\cdot U^ {(t-1)T}V^{(t-1)T})V_{ik}^{(t-1)T}} \tag{6}\]  
\[v_{kj}^{(t)}=v_{kj}^{(t-1)}\frac{U^{(t-1)T}(W\cdot A^{(t-1)T})_{ik}}{U^{(t-1)T} (W\cdot U^{(t-1)T}V^{(t-1)})_{ik}} \tag{7}\]
###### Abstract
In 2000, [PERSON] proposed an ApEn technique, Sample Entropy (SampEn) [30], which overcomes bias from self-matching in ApEn. SampEn results are independent of data length, and the method is powerful and has no major flaws. Hence, SampEn is the most widely used entropy method for physiological signals and is the second most widely used in all clinical applications (after Shannon entropy). ApEn [28] and SampEn [30] are the two most commonly used methods for handling biological data. In this study, SampEn was selected because its results are more consistent than those of ApEn, it achieves faster processing of short data sets, and it is more common in the medical literature, facilitating comparisons. A study investigating SampEn RR data are correlated with normalized HF (nHF) but negatively correlated with normalized LF (nLF) and LF/HF parameters in cardiac control, confirming that the complexity of cardiac interval time series is associated with the ANS functional status [31]. SampEn at the highest intensity tends to decline during the exercise [32]. Another study demonstrated that both SampEn RR and SampEn-breath-to-breath (BB) were significantly different for exercise and non-exercise signals. Analyzing SampEn trend line gradients is an effective method for fatigue detection [33]. Sports players often overtrain, resulting in fatigue and an inability to continue training; overtraining could be avoided by ensuring that training is in the sub-max range. Although metrics for ACP assessment are well understood, further research is required regarding those for sub-max physical load assessment. In the literature, fatigue has been shown to affect the complexity of physiological signals. This study aims to identify a novel scientific indicator of fatigue to increase the efficiency of training.