esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results
### Experiment Configuration  
#### 5.3.1 Deep Q-Learning Configuration  
Table 2 summarises the key parameters for the DQN algorithm configuration used in this study. These parameters shape the agent's training process and its balance between exploration and exploitation. The number of episodes, set to 150, defines the overall training duration, while the maximum actions per episode, limited to 1000, controls each episode's length. The epsilon parameters, epsilon start, decay, and end, determine the agent's exploration strategy, starting at 0.8 for high exploration, decreasing at 0.99 to gradually reduce exploration, and reaching a minimum of 0.01, which prioritises exploitation. Together, these settings optimise the agent's ability to explore the environment initially and focus on learned actions as training progresses.  
The agent network architecture consists of three layers: the input layer, two hidden layers, and the output layer. The first hidden layer contains 64 neurones, while the second hidden layer has 16 neurons, enabling the network to learn complex representations of the state-action space. The activation function used in the hidden layers is the ReLU (Rectified Linear Unit), which introduces nonlinearity and enhances the learning process. The output layer corresponds to the number of possible actions in the CartPole environment, providing Q-values for each action based on the current state. Key hyperparameters such as \(\gamma\) (discount factor), learning rate, replay buffer size, and target update interval are passed through the implemented GA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline number of episodes & 150 \\ maximum actions per episode & 1000 \\ epsilon start & 0.8 \\ epsilon decay & 0.99 \\ epsilon end & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deep Q-Learning configuration.  
Figure 1: Trend of increase in average reward in subsequent 500 episodes.
## 2 Main Components of the OxPhos Pathway in _M. tb_
### NADH Dehydrogenase  
_M. tb_ possesses three membrane-bound NADH dehydrogenases that are capable of transferring electrons to MK with the oxidation of NADH to NAD+. There is a proton-translocating type I NADH dehydrogenase (NDH-1) and two non-proton-translocating type II NADH dehydrogenases (NDH-2) [28]. NDH-2 has been chemically validated as a drug target in _M. tb_[28; 29; 30]. Unlike NDH-1, it is absent in the mammalian genome, which is an advantage to develop specific inhibitors. NDH-1 is encoded by the nuo operon, NuoA-NuoN, (rv3145-rv3158) and the two NDH-2 are identified as Ndh (encoded by ndh = rv1854c) and NdhA (encoded by ndhA = rv0392c) [17; 28]. High-throughput transposon mutagenesis studies suggested that NDH-1 and NdhA are dispensable for growth, whereas Ndh is essential in vitro and in vivo [31; 32; 33]. However, recent studies have clarified the redundancy and essentiality of the NADH dehydrogenases. Detailed gene-deletion and gene-silencing studies of nuo, ndh, and ndhA performed by [PERSON] et al., established that individually, none of the NADH dehydrogenases are essential in vitro or in vivo [34]. [PERSON] et al. further demonstrated that NDH-2 enzymes are jointly essential for growth in the presence of long-chain fatty acids, but are dispensable for growth on some carbon sources such as glycerol [35]. Moreover, a strain deficient for NDH-2 activity is only mildly attenuated in mice [35]. Both studies agree that while _M. tb_ requires at least one non-proton-pumping NDH-2 enzyme for growth, chemically targeting NDH-2 alone will likely be inefficacious, but is conceivable as part of a rational drug combination.  
Figure 1: Schematic representation of the _Mycobacterium tuberculosis_ (_M. tb_) electron transport chain (ETC) and menaquinone (MK) biosynthesis pathway. Electrons provided by NADH are fuelled to the ETC by the NADH dehydrogenases (mainly NDH-2 in _M. tb_), leading to the reduction of menaquinone (MKH2). MK is the sole carrier of electrons across the ETC and eight main enzymes are involved in its biosynthesis. The succinate dehydrogenase Sdh1 and Sdh2 also contribute to the reduction of the menaquinone pool. Electrons from the menaquinone pool are then transferred to one of the terminal oxidases, the proton-pumping Cyt-bcc-aa3 or the Cyt-bd oxidase, both of which catalyse the reduction of oxygen to water. Along the respiratory chain, protons are pumped across the membrane, which generate the proton motive force (PMF) that is then used by the F\({}_{1}\)F\({}_{0}\) ATP synthase to produce ATP.
## 2 Overview of Measurement Setup and Used Data-Extraction Method
### Case 1: Outdoor Urban Environment at 38 GHz  
In [4], measurement campaign is conducted for measuring different channel parameters in University of Texas at Austin campus for outdoor environment. The study is conducted at mmWave (38 GHz) band. The Tx was placed at different rooftop locations within the University. The antenna to transmit is mounted on a tripod 1.5 m above the roof. At locations of Rx, measurements are obtained by using steerable antennas with a 25 dBi vertically polarized rectangular horn antenna. The separation of Rx from Tx ranges from 75 m to 295 m. Rx is placed at 11 different locations and out of these 11 five were obstructed and six locations had clear line-of-sight (LoS) using the 25 dBi antenna. In selected measurements, three different scenarios are presented. In scenario 1: the distance between Tx and Rx is less than 150 m; scenario 2 covers the cases where the distance between Tx and Rx is greater than 150 m and less than 250 m. Scenario 3 covers all the measurement locations where the distance between Tx and Rx is greater than 250 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measurement Scenario**} & **Propagation Environment** & **Frequency Band** & \multicolumn{2}{c}{**Channel Statistics**} \\ \cline{3-5}  & & **Available** & **Proposed Extension** \\ \hline \multirow{3}{*}{Case 1 [4]} & Outdoor Urban & \multirow{3}{*}{38 GHz} & Path Loss, Root Mean Square (RMS) Delay Spread, AoA & Azimuthal Spread Quantification, LCR, AFD, CD, \\  & & & Spread, AoA & Auto-Correlations \\ \hline \multirow{3}{*}{Case 2 [7]} & Indoor & \multirow{3}{*}{28 and 38 GHz} & Path Loss, RMS Delay Spread, AoA & Azimuthal Spread Quantification, LCR, AFD, CD, \\  & & & AoA & Auto-Correlations \\ \hline \multirow{3}{*}{Case 3 [6]} & \multirow{3}{*}{Indoor Office} & \multirow{3}{*}{28 GHz} & Delay Spread, AoA, Time of Arrival & Azimuthal Spread Quantification, LCR, AFD, CD, Auto-Correlations \\ \cline{1-1} \cline{4-4}  & & & Arrival & AFD, CD, Auto-Correlations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of considered measurement scenarios and the proposed extensions.
## 3 Results and Discussion
### Establishment and Verification of the PLS Model Based on an Optimal Variable Set  
The optimal variable set selected by DWT-BOSS was the independent variable of the tea leaves' moisture content prediction model, and the corresponding tea leaves moisture content was the dependent variable. The tea leaves moisture content prediction model (defined as L(i)-BOSS-PLS model, i = 1-6) was constructed. Due to the generation of random numbers, the model was run 30 times to verify the reliability of the model. In other words, 30 models were obtained in each layer. The optimal variable sets of L1-L6 were modeled, respectively, and the model of full-spectrum L0 was introduced for comparison. The V-PLS model was constructed to explore the model's accuracy and stability. By comparing the L(i)-BOSS-PLS algorithm with the V-PLS algorithm, we concluded that the accuracy and stability of the PLS models were improved. By analyzing the information in Table 1, we found the V-PLS model has the highest accuracy, with an \(R_{c}^{2}\) of 0.9410, RMSEC of 0.2404, \(R_{cv}^{2}\) of 0.9171, RMSECV of 0.2851, \(R_{p}^{2}\) of 0.9513, and RMSEP pf 0.2236. In general, the L(i)-BOSS-PLS model produced a better effect than the L0-PLS model, with higher prediction accuracy and better model stability. The results obtained by running the program 30 times were within a reasonable range. The DWT-BOSS considerably reduces the amount of modeling computation and effectively improves the prediction ability of the model. In the V-PLS model established by the optimal variable set, 55 variables were  
Figure 6: The variables selected by the DWT-BOSS coupling algorithm. Note: L1 refers to the reconstructed spectral average after one-layer wavelet transform, L2 to L6 are analogous to L1. V is the optimal variable set. Different graphs represent different levels of decomposition.  
selected from 3112 variables for modeling, which greatly reduced the modeling time and improved the model accuracy. The method provides a reference for the selection of key bands for the near-infrared spectrum of Yinghong No. 9 tea leaves, providing an inversion of moisture content for other tea leaves.
# Effects of 450 MeV Kr Swift Heavy Ion Irradiation on GaN-Based Terahertz Schottky Barrier Diodes
## 3 Results and Discussion
### The Analysis of Low-Frequency Noise  
To investigate the influence of Kr SHI irradiation on the performance of GaN-based THz SSDs, the LFN spectra of the devices under a forward bias of 0.8 V were measured and are shown in Figure 6a. The observed reduction in LFN following Kr SHI irradiation is attributed to the decreased current. The LFN data at a forward bias of 0.8 V reveal a total noise level comprising superimposed Lorentzian humps and 1/_f_ noise, as described by Equation (4) [35; 36].  
\[S_{I}=\frac{A\tau}{1+\left(2\pi f\tau\right)^{2}}+\frac{B}{f} \tag{4}\]  
where \(S_{I}\) is the LFN power spectral density. \(A\), \(B\) are the amplitudes of Lorentzian hump and 1/_f_ noise, respectively. \(\tau\) is the time constant associated with the trap states.  
The LFN spectra under different irradiation fluences were fitted using Equation (4), and the extracted values of \(A\), \(B\), and \(\tau\) are summarized in Table 2. The trap energy level (\(E_{T}\)) below the conduction band was calculated from the time constant \(\tau\) using Shockley-Read-Hall statistics [37]  
\[\tau=\frac{1}{v_{th}\sigma_{T}N_{C}}exp\left(\frac{E_{T}}{kT}\right) \tag{5}\]  
where \(\sigma_{T}\) = 1 \(\times\) 10\({}^{-14}\) cm\({}^{2}\) is the capture cross-section of the trap states, \(N_{C}\) = 4.3 \(\times\) 10\({}^{14}\) T\({}^{3/2}\) cm\({}^{-3}\) is the effective density of states in the conduction band, and \(v_{th}\) = 2.5 \(\times\) 10\({}^{7}\) cm/s is the average thermal velocity of the carriers [38]. The values of \(E_{T}\) with and without Kr SHI  
Figure 5: (**a**) Trajectories of 450 MeV Kr ions in GaN-based THz SSDs, (**b**) variation in electronic energy loss vs. ions’ path length in Au/Ni/GaN/SiC target, (**c**) distribution of displacement losses and (**d**) vacancy concentration (\(V_{Ga}\) and \(V_{N}\)) in the GaN epitaxial layer for 450 MeV Kr ions in GaN-based THz SSDs.  
irradiation are presented in Table 2, indicating that the trap states in GaN-based THz SBDs are located approximately 0.62 eV below the conduction band. Furthermore, as the fluence of Kr SHI irradiation increases, the \(E_{T}\) becomes shallower.  
Figure 6b displays the contrast of the LFN with current for GaN-based THz SBDs with and without Kr SHI irradiation. Compared to pristine GaN-based THz SBDs, the irradiated devices exhibited relatively lower LFN power. The LFN at low current follows a relationship of \(S_{l}\)\(\propto\)\(l^{0.82}\), whereas it changes to \(S_{l}\)\(\propto\)\(l^{2}\) as the current increases. The LFN of GaN-based THz SBDs under forward bias exhibits a strong dependence on the \(R_{s}\). At low current and high \(R_{s}\) conditions, LFN is dominated by Lorentzian noise, which is primarily attributed to interface defects at the Schottky metal-semiconductor junction. At high current and low \(R_{s}\), changes in barrier height caused by the capture and release of electrons by defect states result in low-frequency 1/f noise [39; 40].
## 3 Proposed FFA-BiGRU Method  
For music emotion recognition, past works have shown that spectral features play an important role in identifying the emotion [27; 28], and a spectrogram is a good representation of the audio clip, and it contains all physical information of the original audio [43]. Therefore, we choose the spectrogram as the model input since it summarizes spectral information in a concise form. Furthermore, the frequency scale of the spectrogram is converted from a linear scale to a mel scale as it resembles the human auditory system. As a result, the log Mel-spectrogram of music audio is used as the model input in this paper. The log Mel-spectrogram of music audio is a pictorial representation and, at the same time, reflects the temporal changes of frequency; that is, the log Mel-spectrogram has both spatial and temporal characteristics. Therefore, we propose a spatial-temporal feature extraction model, called FFA-BiGRU, for music emotion classification.  
The proposed model is shown in Figure 1, which consists of three modules: a multi-level spatial feature learning module, a temporal feature learning module, and an emotion prediction module. Specifically, the spatial feature learning module is an attention-based convolutional residual network called FFA [32], which mainly consists of three group architecture (GA) blocks to learn multi-scale spatial features. Each group architecture is a global residual block that combines 19 basic blocks with local residual learning, and each basic block combines residual learning with a channel-spatial attention module. To further capture the temporal features of music audio, the BiGRU module is introduced after the spatial feature learning module to capture the sequential features. Then, the output feature maps of FFA and those of the BiGRU are concatenated in the channel direction. Finally, the fully connected (FC) layer is used to provide the final emotion prediction result. In the following subsections, we will provide the network details of each module in the proposed model.  
Figure 1: An overview of the proposed FFA-BiGRU model. The model consists of a spatial feature learning module (FFA), a temporal feature learning module (BiGRU), and an emotion prediction module.
## 2 Site Description and Techniques for Data Analyses
### Numerical Modelling  
To quantify the impact of ship emissions, two different models have been employed: BOLCHEM [31; 32] and ADMS-Urban [33]. BOLCHEM simulations domain covered the Apulia region, allowing to evaluate the spatial distribution of the ship emissions impact in the area surrounding the port. ADMS-Urban, using background concentration simulated by BOLCHEM and modelling explicitly the ship emissions, allowed to evaluate the detailed spatial distribution of pollutants at a local level. Unlike BOLCHEM, ADMS-Urban uses a simplified chemical module for NO\({}_{\text{x}}\)-VOC-O\({}_{3}\) chemistry and the formation of secondary aerosols is not included in the model.
# Design and Experimental Study of the General Mechanical Pneumatic Combined Seed Metering Device
## 4 Seed Performance Test
### Single-Factor Test
#### 4.3.2 Airflow Velocity of Cleaning Seed  
The unloading air velocity of soybeans and corn was set to 15 m/s, the rotating speed of the drum was set to 15 r/min, and the cone angle of the hole was set to 35\({}^{\circ}\). The air nozzle was taken out, and the anemometer was placed at the distance corresponding to the hole to measure the airflow speed. The air flow regulating valve was used to adjust the air speed. The cleaning air speed was set to 21 m/s, 18 m/s, 15 m/s, 12 m/s, 9 m/s, and 6 m/s for the experiment. Since the rapeseed was lighter, the seed unloading air velocity was set to 9 m/s during the seeding, and the cleaning air velocity was set to 9 m/s, 7.5 m/s, 6 m/s, 4.5 m/s, 3 m/s, and 1.5 m/s for the test. Three sets of each level test were repeated. The test results are presented in Figure 7.  
According to the single-factor experiment, the qualified seeding index of the three crops first increased and then decreased with the increase in the cleaning air velocity; the missed seeding index first decreased and then increased. Too small cleaning airflow made it difficult for seeds to be cleaned out, thereby resulting in an increase in the replay seeding index. When there were many seeds in the hole not cleaned, and the uppermost seed part of the hole was exposed, as the feeding drum rotated, it might produce the seed knocking, cutting function of the protective seed plate cause the top seed to be damaged, and the bottom seed to be compacted under the extrusion of surrounding seeds. The unloading airflow struggled to unload the lowest seed, and continued to rotate with the feeding roller in the hole and affect the subsequent seeding operation. Excessive cleaning airflow would caused seeds in the hole to be cleaned out, thereby resulting in a phenomenon of missed seeding, which demonstrated that a proper cleaning airflow speed could effectively improve the seeding performance of the metering device.
## 1 Introduction
In [9], on the basis of experimental geophysical data, it is indicated that the upper layer of bottom sediments on the Arctic shelf comprises, as a rule, unconsolidated or weakly consolidated sediments, which are characterized by the presence of various structures. These are layered series of deposits with a length up to tens of kilometers, narrow vertical channels related to natural gas emissions with craters at the water-bottom interface. In the composition of the material of these structures, there is an alternation of areas of frozen, thawed and gas-saturated sediments [11], with a significantdispersion of the values of the speed of sound \(c_{1}\) and density \(\rho_{1}\). Thus, the upper part of the sedimentary layer of the Arctic shelf bottom is a medium with a substantially inhomogeneous spatial distribution of the main acoustic parameters \(c_{1}\) and \(\rho_{1}\), which describe the impedance of the lower boundary of the waveguide. For the most adequate description, this indicates the need to consider the bottom impedance as a random spatial function and studying the statistical problem of sound propagation in a homogeneous waveguide of a shallow sea with horizontal fluctuations of the bottom impedance along the path. This paper presents the results of a statistical analysis of such a model problem for the characteristic values of the waveguide and bottom parameters, corresponding to the known 3D seismic data [9], obtained in the water area of the Kara Sea shelf. A statistical analysis of this problem, which assumes coverage of a wide range of possible values of the speed of sound in the upper layer of bottom sediments, makes it possible to draw fairly representative conclusions about the average characteristics of the acoustic field not only for the Kara Sea but also for the propagation of sound in the shallow waters of the Russian Arctic shelf as a whole. Note that the statistical problem of the influence of the fluctuating bottom impedance on the propagation of a sound signal in a 2D inhomogeneous shallow-water waveguide is apparently formulated and solved for the first time. From a fundamental point of view, of interest are the regularities of the behavior of the average intensity, which describe the transmission losses of the signal in a random medium waveguide bottom sediments, and its fluctuations revealed in the work. The applied significance of the results of this work lies in the quantitative estimates obtained by the authors of the degree of influence of random bottom parameters in typical Arctic regions, which can be used to predict transmission loss of low and medium-frequency signals in these regions and areas with similar conditions. It is also important to note that the statistical analysis performed in this work is useful for predicting the presence of bottom regions with gas-saturated sediments. This is of interest, on the one hand, for the exploration of minerals in the regions of the Arctic shelf, and on the other hand, for the indication of possible places of emission of greenhouse gases into the atmosphere.
## 3 Antimicrobial Peptides (AMPs)
### Mechanisms of Action
#### 3.1.3 Immunomodulatory AMPs  
Except for inherent antimicrobial activity, certain AMPs (also termed as host-defense peptides (HDPs)) are found to modulate the host immune system in vivo and in vitro by manipulating immune and pro-inflammatory/anti-inflammatory responses, such as chemo-attraction and programmed cell death, contributing to the clearance of pathogens, as well as their persisters. Moreover, evidence shows that the alterations of HDPs levels are related to diverse (multiple) infection events and inflammation. This phenomenon can potentially be used as a biomarker for such diseases [137]. In terms of foodborne pathogens causing diarrhea and infectious colitis (such as _E. coli_, _Salmonella_ and _Listeria_), DHPs produced by colonic epithelial cells and leukocytes are crucial parts of the innate immune response in the colon against these enteropathogenic bacteria [138].  
In humans, cathelcidin and \(\beta\)-structured defensins are two primary families of HDPs, related to both innate and adaptive immunity [139]. There is only one human cathelcidin--hCAP18, secreted by a range of cells including mucosal epithelial cells, mast cells, and various immune cells [140]. LL-37 is cleaved from the C-terminal of hCAP18 by serine proteases [137], and is one of the best studies human AMPs. As an immunomodulatory AMP, LL-37 cannot only rapidly kill a wide range of pathogens by creating transmembrane pores, but also work as an \"alarmin\" that modulates immune mechanisms [141]. Specifically, LL-37 interacts with more than 16 protein receptors and, consequently, putatively modulates expression of hundreds of genes and thousands of second receptor proteins. As a result, it modulates various pro-inflammatory and anti-inflammatory activities depending on the cell type and inflammatory stimuli [139].  
Similar to cathelcidin, defensins possess both effective bactericidal function and relatively modest regulatory activity in inflammation and immunity. Human defensins are cysteine-rich peptides, including two subgroups: \(\alpha\)- and \(\beta\)-defensins. \(\alpha\)-Defensins, also known as human neutrophil peptides (HNPs), are produced by azurophilic neutrophil granules (HNP1-4) or by Paneth cells (HD5-6) in intestinal mucosa. In comparison, \(\beta\)-defensins (hBDs) are longer peptides with more lysines, mainly released by epithelial cells and immune cells such as macrophages. For now, at least 48 human hBDs gene are found and six hBDs have been characterized (hBD1-6) [142], regulating the immune system by inducing chemotaxis secretion and promoting the maturation and activation of leukocytes [143].