esa-sceva/satcom-chunk-collection huggingface dataset:
# Nucleotides as Bacterial Second Messengers
## 4 The cAMP Nucleotide
### Pseudomonas aeruginosa  
_P. aeruginosa_ can express three ACs: CyaA, CyaB, and ExoY, indicating that cAMP plays a role in the pathogenesis of this bacterium. ExoY is an AC toxin that is activated within host cells, and this toxin is exported and introduced into the host cell via a cAMP-regulated T3 SS secretion system [63]. In contrast, the enzymes CyaA and CyaB are not exportable and remain within the cytosol of _P. aeruginosa_; these intracellular ACs increase intracellular cAMP levels to control gene expression of virulence proteins. Ca\({}^{2+}\) is an environmental component for the expression of the T3 SS secretion system; low Ca\({}^{2+}\) concentration triggers cAMP production and, as a result, the expression of secretion system components [64]. Ca\({}^{2+}\) is sensed in the environment by CyaB and detecting a low Ca\({}^{2+}\) concentration activates CyaB to produce high intracellular cAMP concentrations [64]. Furthermore, CyaB is involved in virulence, deletion of the _cyaB_ gene attenuates this process [65]. Low virulence is a result of low expression of T3 SS components. Overexpression of the regulatory factor, ExsA, restores the virulence phenotype because of the reconstitution of the expression of the T3 SS component system [65]. Another cAMP-activated transcription factor is Vrf. Cyclic-AMP binding to Vrf regulates transcriptional upregulation of many virulence-associated genes, such as type IV pili, the T3 SS secretion system, exotoxin A, and the quorum sensing system [66]. On the other hand, cAMP degradation is driven by the CpdA protein to reduce the amount of intracellular cAMP, and these conditions are necessary to overexpress virulence factors.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 5 Fungi
### Other Fungi  
_Lecanicillium lecanii_ (formerly _Verticillium lecanii_) infects several beetle species, however, only a few studies are published on its infectivity in scarabs. [PERSON] and [PERSON] [138] found that _L. lecanii_ produced between 17.4-29.2% mortality in rhinoceros beetle larvae (_Orytotes agamemmon_) in Saudi Arabia, and a _L. lecanii_ soil application was reported to cause 48-53% grub mortality for the sugarcane beetle (_Holotrichea consanguinea_) in India [139]. To our knowledge, _L. lecanii_ has not been tested on any scarab species found in the U.S.  
_Isaria fumososaeus_ (formerly _Pacicolomyces fumososcea_) has been isolated from the New Zealand grass grub [140] and cockchafer (_M. melolontha_) [78] and has been shown to cause larval mortality. However, while it is used for the control of some other beetle species, it has not been used for the biocontrol of any scarab species. Other fungi that have been isolated from scarabs but little-studied are _Entomophthora brahminae_, _Aspergillus flavus_, _Fusarium solani_, and _Rhizopus oryzae_ [84; 141; 142]. Much more research is needed to determine their potential for scarab biocontrol.
## 3 Results and Discussion
### Relationship between Load and Instrumental Signal
In summary, the sensors G1, G3, and G5 showed better performances concerning the linear model between the load and the instrumental signal, for both great and small displacements of the strain gauge when the sensor was embedded between 30% and 45% of the axis of the sensing area, and approximation errors were inferior to 5%. The experimental load-displacement and instrumental signal-displacement curves (Figure 5a,b, respectively) showed a third-order polynomial trend for loads between zero and the maximum load. To obtain a linear relationship between the load and the instrumental signal, it is required that the load-displacement and instrumental signal-displacement curves be collinear; that is to say, the coefficients of the third-order polynomial model need to be perfectly scaled; and thus, both the load-displacement curve and the instrumental signal-displacement curve can measure the same phenomenon proportionally.
## 3 Results  
### Trend of a Typical Test  
In this section, a typical test will be briefly discussed. In Figure 5a, the typical trends of the temperatures in the components are reported for a 90/38/20 (HT/MT/LT) test. They are consistent with the operation of typical sorption storage: during the first half of the cycle, the adsorber is heated, while the condenser is cooled down. Both the temperatures are constant, indicating a good capacity of the testing bench in terms of maintaining the set conditions. During the second part of the cycle, the useful effect is visualized by the reduction in the temperature at the evaporator (the blue line in the figure), while the adsorber is cooled. During the tests, the same temperature was used both for condensation and for adsorption (three temperature levels).  
Instead, in Figure 5b, the power and energy measured at the phase changer are reported. In particular, the power in the negative part of the chart is the condensation power, while the one in the positive part is the evaporation one. Energy (which is represented on the secondary axis on the right) has been instead calculated only for the evaporation, representing the useful effect on the cycle in the present configuration. The curve, in particular, represents the cumulative energy recovered, which, for the examined cycle, amounts to 580 Wh.  
Figure 5: (**a**) Typical temperature trends for a reference test; (**b**) power and energy for a reference test. HT (high temperature) = 90 °C; MT (medium temperature) = 38 °C; LT (low temperature) = 20 °C.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Charge/discharge time (s) & 600, 750, 900, 1200 \\ High temperature inlet (°C) & 85, 90 \\ Medium temperature inlet (°C) & 28, 30, 33, 35, 38 \\ Low temperature inlet (°C) & 5, 10, 12, 15, 18, 20 \\ Adsorber Flow rate (kg/min) & 8, 12, 15 \\ Phase changer flow rate (kg/min) & 5, 10, 15, 18 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Testing conditions for the first characterization tests of the composite adsorber based on an aluminum, porous structure and a SAPO-34 coating.
## 3 Results and Discussion
### Chloroplast Genome Assembly of A. remotilora  
For the assembly construction of the chloroplast genome, 1.5 Gb sequences were re-extracted from the trimmed 188.2 Gb sequence dataset. The assembled genome presented a circular-shaped structure with 174,455 bp. The circular structure had four conserved regions consisting of LSC (108,423 bp), SSC (10,444 bp), and a pair of I  
Figure 1: Genome size estimation of _A. remotilora_ (**a**) Whole-genome size with optimal _k_-mer frequency values were measured using GenomeScope profile plots. (**b**) Whole-genome sizes were measured using jellyfish. 27,794 bp). A total of 131 genes were functionally predicted including 23 multicopy genes (Table 1). The 23 duplicated genes were excluded from the 131 gene dataset, resulting in 108 unique genes being annotated (Table 1, Figure 2). This genome has 19 intron-containing genes consisting of 11 protein-coding genes and 8 transfer RNA genes (_trnG-UCC_, _trnK-UUU_, _trnL-UAAA_, _trnV-UAC_, _trnA-UGC_ (\(\times\)2), and _trnI-GAU_ (\(\times\)2)). Among these genes, three genes (_rps12_ (\(\times\)2) and _ycf3_) contain two introns, and sixteen genes have one intron. In particular, three genes (_trnA-UGC_, _trnI-GAU_ and _rps12_) have two copies. In the RNA splicing, we identified nine splicing genes consisting of eight cis-splicing and one trans-splicing (_rps12_) gene (Figure S1). The _ycf3_ cis-splicing gene has two introns, which is important in photosynthesis [9]. Chloroplast genome genes were categorized into three functional groups: photosynthesis, self-replication, and \"other genes\" [24]. The annotated 108 genes of _A. remotiflora_ were categorized into 44 genes in the photosynthesis metabolism, 58 expression genes in the self-replication, and 6 genes in the \"other genes\" category. Among the classified genes, the photosynthesis category was functionally divided into six groups (Table 1). This category had two dominant gene families, such as five genes of the photosystem I group and sixteen genes of the photosystem II group. The self-replication category comprised five groups, including 34 encoded genes from ribosomal and transfer RNAs. The \"other genes\" category was divided into four groups, and three genes (_ycf1_, _ycf2_, and _ycf4_) as open reading frames, whose gene functions are unclear, were identified.
# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 2 Methods
### Description of Local Features  
Following feature point extraction from the point cloud and computation of dynamic neighborhood radii, a feature descriptor is generated by constructing a cumulative histogram of projection distances between each feature point and its neighbors within the dynamically sized neighborhood.  
For each feature point, the Euclidean distance to every point \(P_{i}\) within its dynamic neighborhood \(L\) is computed. The resulting distance vector is then projected onto the feature point's principal eigenvector \(\mathbf{v}_{1}\), corresponding to the largest eigenvalue, yielding a projection length \(d_{p}\), as shown in Equation (18):  
\[d_{p}=(P_{i}-P_{c})\cdot\mathbf{v}_{1} \tag{18}\]  
Upon obtaining the projected distances for all points within the neighborhood, a cumulative projected distance histogram is constructed for each feature point. The histogram's range is defined by the minimum projected distance, \(d_{p}min\), and the maximum projected distance, \(d_{p}max\), which serve as the lower and upper bounds, respectively. This range is then partitioned into a set of equally sized bins, and the frequency of projected distances falling within each bin is accumulated. The resulting histogram represents the distribution of projected distances with respect to the orientation defined by \(\mathbf{v}_{1}\) and is shown in the following equation:  
\[H(i)=count\big{(}d\in\big{[}d_{p},d_{p+1}\big{]}\big{)} \tag{19}\]  
where \(H(i)\) represents the number of points within the i-th interval [\(d_{p}\), \(d_{p+1}\)].  
The cumulative projected distance histogram provides a local feature descriptor for each feature point.
## 2 Materials and Methods
### Statistical Analysis  
The three jumps performed with each device were averaged to provide a representative value for each variable. Means and standard deviations (SDs) were calculated after verifying the normality of distributions using Kolmogorov-Smirnov statistics. Paired-sample \(t\)-tests were used to compare the mean differences between methods and between time phases (takeoff and landing). One-sample \(t\)-tests were used to examine CONT\({}_{k}\) against zero. The magnitude of the difference was also assessed using [PERSON]'s \(d\), where \(d>0.8\) is a large effect, \(0.5\leq d\leq 0.8\) is a moderate effect, \(0.2\leq d\leq 0.5\) is a small effect, and \(d<0.2\) is a trivial effect [18]. The intra-participant reliability of the variables of the three jumps was examined by the intraclass correlation coefficient, one-way random-effects model (ICC\({}_{1,1}\)). Acceptable reliability was defined as an ICC \(>0.70\)[19]. The analysis of the fixed bias with its upper and lower limits of agreement (LOA) between the jump heights for all 81 trials obtained from the two calculations was performed by using a Bland-Altman plot [20]. Heteroscedasticity of error (proportional bias) was defined as a coefficient of determination (\(r^{2}\)) \(>0.1\)[21]. Statistical significance was determined by a probability level of \(p<0.05\). All calculations were performed using IBM SPSS Statistics version 19 (IBM Co., Chicago, IL, USA).
## 3 Methodology
### Spatiotemporal Sequences Modeling  
Every individual exhibits a distinct gait, speed, and acceleration while walking. Since _LSTM_ is particularly effective at capturing temporal correlations in sequence data, and pedestrians' trajectories are influenced by previous behavioral decisions over time, the _LSTM_ is used to encode pedestrian trajectories, which helps to extract the temporal dynamics from their past movements. Initially, we transform the absolute positions from the pedestrian trajectory data into relative positions. These relative locations are then embedded and input into an _LSTM_ for encoding, enhancing the model's ability to detect and understand temporal correlations in pedestrian trajectories.  
\[u_{i}^{t}=\{(x_{i}^{t}-x_{i}^{t-1}),(y_{i}^{t}-y_{i}^{t-1})\} \tag{3}\]  
\[s_{i}^{t}=LSTM(s_{i}^{t-1},\phi(u_{i}^{t};W_{u});W_{s}) \tag{4}\]  
\(W_{u}\) represents the embedding weights, while \(\phi(\cdot)\) is an embedding function. The weights of the temporal _LSTM_ cell are \(W_{s}\), and the hidden state of the _LSTM_ at time t is represented by \(s_{i}^{t}\).  
Subsequently, we amalgamate trajectory sequence data, scene features, and intention features of pedestrian behavior, as depicted in Figure 1. We used the multi-head attention mechanism and the fully connected layer to fuse these features. The multi-head attention mechanism enhances the understanding of complex crowd dynamics by focusing on different subspaces of the input data at the same time and by computing multiple attention heads in parallel, enabling the model to extract dimensional information from different perspectives and features. The inputs for the attention mechanism consist of three components: the query and key (with each key having a dimension of dk), and the value (having a dimension of dv). During computation, the query is dot multiplied with all keys. To address the vanishing gradient problem, each product is normalized using the square root of \(d_{k}\). Afterwards, the softmax function computes the weights for each value. In our model, the query, key, and value matrices \(Q^{i}\), \(K^{i}\), and \(V^{i}\) are derived from a vector \(n_{i}\), which combines the trajectory feature vector, environment interaction feature vector, and intention feature vector of pedestrian behavior.  
\[\overline{v}_{i}=\delta_{1}(v_{i}^{T_{\text{obs}}}) \tag{5}\]  
\[\overline{r}_{i}=\delta_{2}(r_{i}^{T_{\text{obs}}}) \tag{6}\]  
\[m_{i}=\overline{v}_{i}\parallel\overline{r}_{i} \tag{7}\]  
\[n_{i}=m_{i}\parallel s_{i} \tag{8}\]  
\[Q^{i}=K^{i}=V^{i}=n_{i} \tag{9}\]  
\[Attention(Q^{i},K^{i},V^{i})=softmax\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}V \tag{10}\]  
Here, \(v\) stands for the scene's features and r indicates the pedestrian's intention features. \(\delta_{1}(\cdot)\) and \(\delta_{2}(\cdot)\) are two different multilayer perceptrons.
## 1 Introduction
They found that changing the distance between the magnets resulted in an optimal resistance variation of about 25%. A magnetically coupled electromagnetic energy harvester consisting of a pair of spring-connected magnets, coils, and a free-moving magnet was proposed in [25]. A multi-harvester consisting of a pair of spring-connected magnets, coils, and a free-moving magnet was proposed in [26]. Two configurations of a multi-generator were fabricated. In the first model, four generators were positioned side by side, while in the second model, the generators were positioned one above the other. The second design was clearly advantageous in terms of power density. The influence of scaling on a 2 DOF velocity-amplified electromagnetic generator was presented in [27]. It has been demonstrated that the electromagnetic coupling coefficient degrades more rapidly with scale. The optimization of the transduction mechanism in the harvester, consisting of two masses oscillating one inside the other between four sets of magnetic springs, including the collision effect in [28], was analyzed. The 2 DOF nonlinear electromagnetic energy harvester for ultra-low frequency excitations, as described in [29], was also analyzed. It was realized by simply magnetically suspending a 1 DOF harvester. The proposed design improved voltage and power outputs, expanded the operating bandwidth, and allowed for easy tuning of the operating frequency band. In this study, we focus on the development of an electromagnetic energy harvester with a wider operational frequency applicable for low-frequency vibrations. The rest of the paper is organized as follows. Section 2 is devoted to the modeling and prototyping of an electromagnetic harvester with a single coil and two levitating magnets. In this section, a new model of the magnetic suspension is proposed. Results and discussion are presented in Section 3, and conclusions are presented in Section 4.
## 3 Results and Discussion
### LC-MS Characterization of Phenolic Compounds from Culinary Spices and Herbs
#### 3.5.1 Flavonoids
Furthermore, neoeriocitrin has been suggested to have neuroprotective effects, potentially reducing the risk of cognitive decline and neurodegenerative diseases [59; 60]. A precursor ion of compound **8** (\(m/z\) 593.1872 and 593.1876) was identified in basil, fennel and mint leaves as didymin in negative mode [61]. A flavonoid compound called didymin is present in a variety of citrus fruits, herbs, and spices that offer unique health benefits. This compound has neuroprotective effects and antioxidant, anti-inflammatory, and anti-diabetic properties [62]. Additionally, didymin has been shown to have advantageous effects on blood sugar levels, making it a potential natural remedy for managing diabetes. Studies have also suggested that didymin may benefit skin health, because it has been demonstrated to shield against UV-induced harm [63]. 6-geranylnaringenin (compound **9**) was characterized in coriander, basil, and fennel leaves at \(m/z\) 407.1878 and 407.1864. Previously, lemon and mint were said to have significant antioxidant potential.
# HVdc Circuit Breakers: Prospects and Challenges
## 7 Available HVdc Breakers from Tech Giants
### General Electric's Superfast Medium Voltage HVdc Breaker/Switch  
Existing dc circuit breakers developed for point-point applications are generally costly, too big, and too slow for use in a medium-voltage dc (MVdc) distribution grid. The energy department of Advanced Research Projects Agency-Energy (ARPA-E), United States recently awarded a research grant of $5.8 M to a team of engineers at General Electric (GE) to develop a superfast MVdc breaker [140,141]. The GE research team proposes to use gas plasma to construct a superfast switch. [PERSON], the chief investigator on the ARPA-E grant, says a typical HVdc breaker requires an air-conditioned building, measures 10 \(\times\) 10 \(\times\) 25 feet, and costs between $5 M to $10 M. A dc breaker made of power semiconductor switches commutates the current in a few milliseconds, which gives time to operate a main mechanical breaker. Such a topology would be very much expensive to set up an MVdc grid.  
The GE research team is designing a breaker that uses plasma or charged gas to operate between the two electrodes. A small current flowing through a mesh wire employed within the two electrodes is used to operate the switch. Their goal is to build a dc breaker of rating 100 kV in the next three years, which is reflected as a boundary between high and medium dc voltage.  
Plasma switches have the benefit of a smaller size. Semiconductor switches can also operate rapidly, but the cooling requirement demands more space to apart the switches  
Figure 11: Control strategy for fault current interruption of hybrid HVdc Breaker, proposed by Alstom grid [137].  
to stay cool. Hence, the equipment size increases. As plasma switches can work at very high temperatures, they can operate close together and adjusted in confined space of smaller footprints.  
A comparison is provided in Table 6 of anticipating commercial HVdc breakers from Tech giants.
## 3 Flexible Substrate Antenna Design
### Antenna Fabrication  
Following the excellent results obtained in the simulation process using CST microwave studio suite 2019, the antenna design was ready for fabrication. For this next step, to fabricate the antenna on the PET substrate, copper was used as the conducting material and the ground plane. An RP-SMA female miniature connector with the dimensions 1.7 \(\times\) 4.1 mm, 8 mm was used for the microstrip feed line. The thickness of the ground plane and substrate was 0.105 mm and the total thickness with the copper patch was 0.14 mm. A Keysight Vector Network Analyzer (VNA) series E5063A was used to perform the measurements; see Figure 6.  
Operating at the 2.45 GHz frequency, and following the same procedures and fabrication steps to corroborate the bending effects of radiation on other polymer substrates, the antenna design was then reproduced on both the Teflon and PVC substrates with 100 and 110 \(\upmu\)m thicknesses, respectively, as shown in Figure 7.  
Figure 5: Structural diagram of E-shaped microstrip antenna.  
Figure 6: E-Shaped flexible microstrip patch antenna on PET substrate: (**a**) thickness measurement, (**b**) fabricated antenna connected to VNA.
## 2 Methods
### DAIDA Framework  
#### 2.2.1 Preprocessing  
The sEMG signals are stochastic non-stationary signals and therefore need to be preprocessed before they can be used for subsequent classification. The preprocessing process of the data in this study consisted of filtering and noise reduction, normalization and active segment extraction.  
According to the spectral energy distribution of the sEMG signal, the motion artifacts were removed using 4 th-order 20-500 Hz Butterworth bandpass filtering, and the industrial frequency noise was removed using 50 Hz notch filtering. Because different subjects' anatomical tissues, physiological states and other factors make the multichannel sEMG signals show large individual differences [25], in order to reduce the impact of individual differences on pattern recognition classification, it is necessary to standardize them by transforming sEMG signals from different subjects. The standardization method used in this article is Z-Score standardization. The transformation formula is \((x-\mu)/\sigma\)[26]. In this formula, \(\mu\) is the mean and \(\sigma\) is the standard deviation. It converts data of different orders of magnitude into unitless values [27].  
In order to obtain effective motion information, the determination of active segments is essential [28; 29]. Therefore, in this study, an adaptive dual-thresholding method was used for the detection of active segments of sEMG signals [27]. According to studies, the optimal window length for sEMG-based pattern recognition (PR) falls within the range of 100 ms to 250 ms [30]. To ensure classification accuracy while taking into account Noraxon's sampling rate, a window of 300 sample points in length was used, sliding over the data in incremental lengths of 150 sample points.  
Eventually, the sEMG signals were converted into a suitable data format for input to the network, i.e., batch size \(\times\) 1 \(\times\) number of channels \(\times\) window length.