esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Flash Drought Definition  
SM can be used as a key variable to identify flash droughts [(20; 23)]. The main crops in the four major agricultural regions of southern China are rice and wheat, with root depths that are generally within 50 cm [(74; 75)]. Therefore, we applied 0-50 cm SM (SMCI 1.0) to monitor flash droughts in the study area. Daily SM was fused to a pentad mean (5-day average) to reduce the noise in daily variability and then converted to a percentile series (excluding February 29 in leap years). The percentile of SM for a given grid was determined by all pentad series (73 pentads in a year) for the entire study period (2001-2020) for that grid. We applied the following criteria to identify flash droughts [(20; 23)], as categorized into four specific cases (scenarios):  
\[\text{Case1}=\left\{\begin{aligned} & SM\left(t_{pr\text{c1}}\right)\ \geq\ 40\text{th percentile}\\ & 20\text{th percentile}<SM(t_{1,2})<40\text{th percentile}\\ & SM(t_{3,\ldots,n})\leq\ 20\text{th percentile},\ \ \ 5\leq n\leq 18 \end{aligned}\right. \tag{6}\]  
\[\text{Case2}=\left\{\begin{aligned} & SM\left(t_{pr\text{c1}}\right)\ \geq\ 40\text{th percentile}\\ & 20\text{th percentile}<SM(t_{1})<40\text{th percentile}\\ & SM(t_{2,\ldots,n})\leq\ 20\text{th percentile},\ \ \ 4\leq n\leq 18 \end{aligned}\right. \tag{7}\]  
\[\text{Case3}=\left\{\begin{aligned} & SM\left(t_{pr\text{c1}}\right)\ \geq\ 40\text{th percentile}\\ & SM(t_{1})\leq\ 20\text{th percentile}\\ & 20\text{th percentile}<SM(t_{2})<40\text{th percentile}\\ & SM(t_{3,\ldots,n})\leq\ 20\text{th percentile},\ \ \ 5\leq n\leq 18 \end{aligned}\right. \tag{8}\]  
\[\text{Case4}=\left\{\begin{aligned} & SM\left(t_{pr\text{c1}}\right)\ \geq\ 40\text{th percentile}\\ & SM(t_{1,\ldots,n})\leq\ 20\text{th percentile},\ \ \ 4\leq n\leq 18 \end{aligned}\right. \tag{9}\]  
where SM(tpre1) represents the SM of the pentad before the flash drought onset, and t1 and tn denote the beginning and termination dates of flash droughts. These four cases of flash droughts exhibit the following features: (1) the pentad mean SM percentile experiences a significant decrease, transitioning from a value greater than or equal to the 40 th percentile at tpre1 to a value less than or equal to the 20 th percentile within four pentads (rapid phase); (2) the duration of the drought phase was characterized by SM below or equal to the 20 th percentile for a minimum of three consecutive pentads to ensure a substantial impact; (3) a flash drought event lasts a minimum of four pentads and a maximum of 18 pentads (distinguish a prolonged drought event); (4) the flash drought came to an end when SM again exceeded the 20 th percentile. The onset speed of flash drought increased from Case 1 to Case 4 (Figure 2).
## 2 Theoretical Model of a System in the Presence of Gaussian Noise  
Figure 1 presents the block scheme of a communication system for physical layer using Offset Quadrature Phase Shift Keying (OQPSK) modulation and spreading of the signals as used in wireless sensor networks. When only Gaussian noise is present in the channel the fading coefficient is set to be one, _i.e._, \(\alpha=1\). In order to develop theoretical model of the whole system with \(N\) correlators, the analysis will start with the case when the symbols are generating at the source output in binary form and detected at the receiver side using one correlator. Generally, for the system with \(N\)-correlators, the source generates message bits which are converted into symbols \(b_{jn}(k)\). To each symbol a spreading sequences \(c_{in}(k)\) is assigned to obtain a chip sequence \(m(k)=b_{jn}(k)c_{in}(k)\) that comes to the input of a multiplexer. If a symbol is in binary form, \(b_{jn}(k)=\pm 1\), then the system represents a direct sequence spread spectrum system (DSSSS) and the receiver has one correlator as it was said before. If a combination of \(K\) bits represents a symbol then the number of sequences sent is \(N=2^{K}\), which is equal to the number of required correlators at the receiver side. Thus, in the following sections, two cases, a single and \(N\)-correlator receiver, will be analyzed.
# A Novel Tool for Biodiversity Studies: Earthworm Classification via NGS and Neural Networks
## 2 Materials and Methods
### Direct RNA Libraries Preparation and Sequencing  
Following quality and integrity verification, total RNA was used for library preparation. The total RNA group was polyadenylated by _E. coli_ Poly(A) Polymerase as described [49]. Briefly, 1 ug total RNA, 1 \(\mu\)L (5U) _E. coli_ Poly(A) Polymerase, 2 \(\mu\)L 10\(\times\)_E. coli_ Poly(A) Polymerase Reaction Buffer, 2 \(\mu\)L Adenosine triphosphate (New England Biolabs, Ipswich, MA, USA) in a 20-\(\mu\)L reaction solution were incubated at 37 \({}^{\circ}\)C for 30 min. After that, polyadenylated RNAs were purified from enzyme reactions using the Monarch RNA Cleanup Kit (New England Biolabs, Ipswich, MA, USA) according to the manufacturer's instructions.  
RNA samples were processed into libraries using the ONT SQK-RNA002 kit (Oxford Nanopore Technologies, Oxford, UK), following the protocols provided by the manufacturer. The library was loaded onto an R9.4.1 flow cell, and the sequencing runs were carried out using the ONT MinKNOW software (version v3.4.12, ONT) to monitor and generate the data. Read quality was checked at EPI2 ME (version 24.08-01), wf-alignment (version v1.2.0) workflow, and filtered using Prowler software v1 to exclude low-quality reads (Q \(<\) 10) [[https://github.com/ProwlerForNanopore/ProwlerTrimmer](https://github.com/ProwlerForNanopore/ProwlerTrimmer)]([https://github.com/ProwlerForNanopore/ProwlerTrimmer](https://github.com/ProwlerForNanopore/ProwlerTrimmer)) (accessed on 1 August 2024) [50].  
To assign taxonomic affiliations to the obtained sequences, we used the software pipeline CCMetagen v1.2.3 (ConClave-based Metagenomics) [51] that utilizes the ConClave sorting scheme [52]. Taxonomic assignment was carried out utilizing the entire NCBI nucleotide collection.
## 3 Magnetic Levitation Control System
### Experimental Evaluation  
#### 3.3.1 Magnetic Levitation at Standstill  
The control performance of the developed magnetic levitation wafer transfer system was verified through experiments under various conditions. Figure 9a is the transient response characteristic result for the magnetic levitation under the conditions of the initial-take-off-landing state. In the case of a linear controller, the levitation control performance in a transient region may deteriorate since the operating point is further away from the equilibrium point. To minimize this effect, ensuring robustness against the modeling errors and ensuring sufficient relative stability, such as gain margins, are important. The control inputs for the 5-DOF motion of the system are shown in Figure 9b. To achieve a smooth transient response, the reference signal from the landing state to the levitation state was set to gradually change in the form of a ramp rather than a step. As a result, as shown in the figure, the system smoothly converges to the steady state without significant overshoot during the state transition. After the state transition is complete, the 5-DOF variation is \(\pm 0.0048\) mm, \(\pm 0.0072\) mm, \(\pm 1.29\times 10^{-5}\) rad, \(\pm 1.40\times 10^{-5}\) rad, and \(\pm 8.54\times 10^{-6}\) rad for the \(x\)-, \(z\)-, \(\theta_{x}\)-, \(\theta_{y}\)-, and \(\theta_{z}\)-axes, respectively.  
System characteristics can be analyzed based on the force and moment applied to each DOF in the steady state. For example, the weight of the system can be estimated from \(F_{z}\), and the CG can be estimated from the applied moment acting in the steady state. An analysis of \(F_{z}\) confirmed that the actual weight was approximately 225 kg, similar to the design values specified in Table 2. Additionally, the analysis of \(M_{y}\) confirms that the CG is slightly shifted in the \(x\)-direction. In fact, this system has a center of gravity deflected by approximately 20 mm in the \(-x\)-direction according to the robot's posture and mass distribution inside the control box, which were not considered during the initial design.
## 2 Methods
### Preprocessing  
Outliers were eliminated using signal processing to extract useful features from the PPG and ECG signals. First, NaNs were eliminated across all signals to preserve the alignment for each subject. PPG signals were normalized to different values for each subject using the min-max method [43]. The ECG and ABP signals were not normalized to extract only time-domain features, and the preservation of the original ABP units (mmHg) was required for estimating the SBP and DBP. The effective features were extracted from the PPG and ECG wave signals. A Kaiser window with a 35 Hz cutoff frequency and 3 dB signal bandwidth was used to eliminate high-frequency noise. Subsequently, low-frequency noise was eliminated using a Kaiser window with a 0.0665 Hz cutoff frequency and 3 dB bandwidth. Thereafter, ECG, PPG, and ABP signals were prepared into 20 s segments. The segmented signals with minimum and maximum values above or below a specific threshold were discarded. Finally, each set of 20 s window of denoised ECG, PPG, and ABP signals was segmented into smaller segments containing fewer cardiac cycles, providing an input for the feature extraction. After preprocessing, 1725 records were obtained.  
Figure 1: Block diagram of the proposed method using a GP-based feature selection and decision process (GFSDP) algorithm. (**a–j**) stand for process steps.
## 5 Pressure Measurement Experiments for Shockwave Impact
The maximum pressure of the \(p_{s1}\) curve appears at the \(k\)th point, which is displayed at the center of the \(X\)-axis display range (\(x_{min}\) to \(x_{max}\)) of the \(L1\) graph. The display range of the other seven graphs is also \(x_{min}\) to \(x_{max}\). In Figure 13, the display ranges of the eight graphs are all set to \(N_{x}\) = 200, \(x_{min}\) = 6400, and \(x_{max}\) = 6600. We display the maximum pressure value of each curve above the corresponding curve graph. As shown in the tank and output pressure curves in Figure 13, when a shockwave is generated, the tank pressure will first drop sharply from a high pressure of about 600 kPa to a low pressure of about 140 kPa. Then, the output pressure will have a high-pressure pulse wave (about 1060 kPa). From the pressure curves of the \(L1\) graph to the \(L6\) graph, we can see that the sensors \(s_{p1}\) to \(s_{p4}\) can detect the pressure pulses (\(p_{simx}>0\), \(i=1\),..., 4), while the sensors of \(s_{p5}\) and \(s_{pg}\) cannot (\(p_{simx}=0\), \(i=5\), 6). Therefore, the shockwave's maximum emitting distance (\(d_{max}\)) can reach \(L4\) (30 cm).
## 2 Related Work
Despite facing challenges posed by adverse weather conditions, the research emphasizes the continuous potential for advancements in target detection and denoising techniques, positioning this study as a cornerstone toward more robust and reliable computer vision systems capable of operating under various weather conditions. Based on the literature review presented, the research in this area is deemed worthy of further exploration. Notably, the study revealed that the impact of incorporating synthetic rainfall images on model detection accuracy is not universally consistent, emphasizing the importance of the image synthesis method employed. Additionally, the diversity of rainy weather images is influenced by the entire training data augmentation process, while the choice of pre-trained models significantly influences detection outcomes. Furthermore, the predominance of studies focusing on YOLO series detectors highlights a research gap concerning Transformer-based detection models. Motivated by these insights, this study aims to propose a novel data-driven approach to construct object detection models under rainy weather conditions, emphasizing the generation of training data and the selection of pre-trained models, with the goal of achieving significant advancements in this field and providing valuable insights for interested researchers.
## 2 Mathematical Model
### Parameters  
The model involves several parameters mostly dealing with lower and upper bounds and costs. First, we begin with two parameters determining the size of the model. Let  
\[\begin{array}{ll}N&\text{be a total number of disposal periods}\\ Z&\text{be a total number of removals from the reactor.}\end{array}\]  
In addition, we define two sets of indices: the set of periods \(\mathcal{N}=\{1,\ldots,N\}\) and the set of removals from the reactor \(\mathcal{Z}=\{1,\ldots,Z\}\). Note that part of the removals are done before the first disposal period begins. In order to link the removals from the reactor and periods, we introduce two parameters:  
\[\begin{array}{ll}a&\text{the last removal before the first disposal period}\\ b&\text{the disposal period when the last removal is done.}\end{array}\]  
In the following, we specify notation and measurement units for some physical magnitudes:  
\begin{tabular}{l l} \(M_{i}\) & number of assemblies belonging to the removal \(i\in\mathcal{Z}\) \\ \(Q\) & length of one disposal tunnel [m] \\ \(A_{i,j}\) & storage time of an assembly belonging to the removal \(i\in\mathcal{Z}\) \\  & in the period \(j\in\mathcal{N}\) [period] \\ \(P_{i,j}\) & decay heat power of an assembly belonging to the removal \(i\in\mathcal{Z}\) \\  & in the period \(j\in\mathcal{N}\) [W]. \\ \end{tabular}  
The next seven parameters describe the cost information needed as an input data for the model:  
\begin{tabular}{l l} \(C_{AS}\) & storage cost per one assembly per period [\(\in\)] \\ \(C_{IS}\) & costs related to the interim storage per period [\(\in\)] \\ \(C_{SP}\) & cost of a storage place per one assembly [\(\in\)] \\ \(C_{CA}\) & cost of one canister [\(\in\)] \\ \(C_{EF}\) & costs related to operating the encapsulation facility per period [\(\in\)] \\ \(C_{DT}\) & cost of a disposal tunnel per meter [\(\in\)] \\ \(C_{CT}\) & cost of a central tunnel per meter [\(\in\)]. \\ \end{tabular}  
Finally, we give some parameters related to the upper and lower bounds:  
\begin{tabular}{l l} \(R\) & minimum storage time of an assembly [period] \\ \(K\) & maximum capacity of a canister \\ \(T\) & minimum number of canisters disposed in one period \\ \(U\) & maximum number of canisters disposed in one period \\ \(p_{max}^{low}\), \(p_{max}^{up}\) & lower and upper bound for the maximum average power of \\  & a canister [W] \\ \(d_{CA}^{low}\), \(d_{CA}^{up}\) & lower and upper bound for the distance between canisters [m] \\ \(d_{DT}^{low}\), \(d_{DT}^{up}\) & lower and upper bound for the distance between disposal \\  & tunnels [m]. \\ \end{tabular}
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Aerial Sensing for ITS
#### 3.4.2 Advanced Aerial Sensing  
Road user detection and tracking support advanced aerial ITS sensing applications. For example, in [192; 202], the researchers developed new methods for traffic shock-wave identification and synchronized traffic flow pattern recognition under oversaturated traffic conditions. [PERSON] et al. [203] conducted a thorough study on traffic conflict based on extracted road user trajectories from UAV. The Safety Space Boundary concept in the paper is an informative design for conflict analysis. One of the most useful applications using UAV is traffic flow parameter estimation: Traditional research in this field focused on using static UAV videos for macroscopic parameters extraction. [PERSON] et al. (2014) led a pioneering research work to extract a variety of critical macroscopic traffic parameters, such as annual average daily traffic (AADT). Later on, a new method was proposed by [PERSON] et al. (2015), in which they adopted image registration and motion information to stitch images and obtain fundamental traffic flow parameters. Lately, [PERSON] et al. developed a series of efficient and robust frameworks to estimate aggregated traffic flow parameters (speed, density, and volume) (2016; 2017; 2018). Because of the potential benefits of higher-resolution data in ITS, microscopic traffic parameter estimation has been conducted (2019). [PERSON] et al. proposed a method to extract naturalistic trajectory data from UAV videos at a relatively less congested intersection using static UAV video (1954). [PERSON] et al. (2021) developed an advanced framework composed of lane detection, vehicle detection, vehicle tracking, and traffic parameter estimation that can estimate 10 different macroscopic and microscopic parameters from a moving UAV.
## 4 Discussion
In a three-time interval study, alterations in 5-HT levels were demonstrated with a significantly higher 5-HT level in rats' colons 8 weeks after the induction of STZ-diabetes, followed by a progressive decrease at 16 and 25 weeks, while at later time points, there were no differences in the 5-HT levels between the diabetics and age-matched controls [43]. The inconsistent data from the 1980s and 1990s might be a result of the difference in the duration of diabetes and the difference in the methods used. Our results are the first to verify the beneficial effects of insulin treatment on the proportion of myenteric 5-HT neurons in different gut segments in 10-week STZ-induced diabetic rats. Immediate insulin treatment prevented the diabetes-induced increased proportion of 5-HT-IR neurons at the control level. Our data are also in good agreement with the findings of [PERSON] et al. [30] regarding the preventive role of insulin therapy on intestinal 5-HT content in 5-week allocan-induced diabetes; however, in that stage of the disease, the 5-HT content was increased only in the duodenum and unchanged in the rest of the gut. The segment-specific protective effects of insulin treatment on the neurochemical changes induced by STZ-diabetes have been revealed in some aspects. [PERSON] et al. [34] found that insulin treatment has beneficial effect in the ileum and colon but not in the duodenum and jejunum on the density of nitregric neurons. [PERSON] et al. [9] detected that insulin therapy restored the enhanced heme oxygenase 2 protein level to the control level in the duodenum and colon of diabetic rats. The roles of neuronal 5-HT are generally associated with GI motility, but some of the recently discovered less conventional features of 5-HT-IR neurons should be mentioned [44]. The 5-HT-IR neurons are among the first in the gut to arise, and they coexist later-born neurons. It is possible that 5-HT may be a neuronal growth factor promoting the development/survival of some neurons being among the last to be born including dopaminergic, GABAergic, nitregric, and calcitonin gene-related peptide-expressing neurons [45]. In support of this, the essential role of 5-HT in the growth and maintenance of enteric neurons [46] and intestinal mucosal epithelium in adult mice were demonstrated [47]. Recent studies focus on the role of 5-HT signaling in enteric neurogenesis in adulthood both in health and diseases [48; 49]. The importance of gut microbiota in maintaining the serotonergic network through release of 5-HT and the activation of the 5-HT4 receptor has also been illustrated in a recent study [50]. Significant differences were seen in the composition of luminal microbiota in the fecal samples collected from the ileum and colon of diabetic rats [51]. These findings suggest mechanisms linking microbial dysbiosis to GI disorders. The abovementioned multifaceted role of neuronal 5-HT implies more complex underlying mechanisms in the structural and functional alterations of the ENS in the diabetic state.
## 1 Introduction
The high-throughput protein production pipeline (Figure 1; see references [1,8] for more detail) starts with parallel cloning (1 in Figure 1) of multiple truncated constructs of the target gene (in one or more expression vectors and hosts) and testing the yield of the recombinant proteins from small-scale (1-3 mL) cultures; After IMAC purification, SDS-PAGE and coomassie staining, clones are classified as no expression, low expression (2 in Figure 1), where band of correct size is a minor component among contaminating proteins; and high expression (3 in Figure 1), where a band of correct size is the major species. Intact mass analysis of eluates from low-expression clones rarely identifies the target protein, but eluates from highly expressed clones allow quick identification of the target protein, as well as information on its integrity, possible PTM, and homogeneity (5 in Figure 1). Bands of low-expressing clones can be identified by MS/MS of tryptic digests of the gel bands (4 in Figure 1). All this information is integrated into decisions on prioritization of correct clones for large-scale expression, for elimination or re-cloning of incorrect clones, and (when partial proteolysis is evident), the design of new constructs. Large scale (1-24 L) expression followed by multi-step purification allows to recover proteins from low and high-expression clones. In a structural genomics pipeline, there are often no specific assays for the activity of the target protein, so the purification intermediates are monitored through purification by presence of gel bands of the correct size. When purifying low-expressing or membrane proteins there is often uncertainty about the identity of gel bands; tryptic digest and MS/MS (6 in Figure 1) are used to provide definitive identification. Intact MS analysis is also used during purification to monitor enzymatic treatments (tag removal, dephosphorylation) and to differentiate fractions with minute differences (e.g., phosphorylation states) in high-resolution chromatography. Finally, intact MS is used for quality control and characterization of all purified protein batches (7 in Figure 1), as well as MS/MS to map PTMs, when necessary.
## 3 Results and Discussion
### Adsorption Kinetics  
The adsorption kinetic study was essential to determine the adsorption efficiency for practical applications [33]. As shown in Table 3 and Figure 9, the kinetic data were better fit by the pseudo-second-order model with a higher value of \(R^{2}\) (\(>\)0.99). Besides, The calculated \(q_{e}\) values (i.e., 1.216 and 1.787 mg\(\cdot\)g\({}^{-1}\)) by Equations (5) and (6) were close to the experimental values (i.e., 1.221 and 1.792 mg\(\cdot\)g\({}^{-1}\)), respectively, indicating the chemisorption involved covalent interaction due to the sharing and exchange of electrons between Cr(VI) ions; the chemical sorption process was the rate-limiting step of the adsorption of Cr(VI) onto FeBC [34].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature/K**} & \multicolumn{3}{c}{**Langmuir Model**} & \multicolumn{3}{c}{**Freundlich Model**} \\ \cline{2-7}  & \(q_{e}\)**/mg\(\cdot\)g\({}^{-1}\)** & \(K_{\text{L}}\)**/L**\(\cdot\)mg\({}^{-1}\)** & \(R^{2}\) & \(K_{\text{F}}\) & \(n\) & \(R^{2}\) \\ \hline
298 & 2.7840 & 0.6001 & 0.9950 & 1.4606 & 5.8720 & 0.9310 \\
308 & 3.0921 & 0.8398 & 0.9972 & 1.8098 & 6.8917 & 0.9304 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Langmuir and Freundlich model parameters for the Cr(VI) adsorption onto FeBC.  
Figure 8: [PERSON] isotherm (dash line) and Freundlich isotherm (solid line) for the adsorption of Cr(VI) onto FeBC.