esa-sceva/satcom-chunk-collection huggingface dataset:
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 2 Related Work  
Improving the performance of LA algorithms is a very relevant problem and has spawned increasing interest in research over the past few years. In a recent paper, [PERSON] et al. propose an algorithm that chooses MCSs based on the received channel coefficients and previous packet information [7]. These metrics are translated to SNR values, which are used to derive an MCS with preferably high throughput. They then compare their adaptive preamble-based algorithm to the automatic fallback rate approach as well as the ideal LA scenario. In contrast with [PERSON] et al., who aim for a high data rate, [PERSON] et al. focus on reducing the error rate to improve LA for ultra-reliable communication [8]. To do this, they propose a simple LA scheme that is competitive with optimal LA schemes. In contrast, we focus on achieving a high data rate, accepting the possible negative impact on other transmission characteristics.  
[PERSON] et al. [1] extend Thompson Sampling for the LA use case. Their Latent Thompson Sampling (LTS) method is based on a Bayesian multi-armed bandit approach and observes a low-dimensional latent state model in the form of SINR, while ACK and NACK are used to give feedback on the previous transmission. [PERSON] et al. [9] present Training of Outer Loop Link Adaptation (TROLL) to enhance the already simple and robust OLLA implemented in current wireless systems. The approach relies on the baseline OLLA but adds minor modifications, such as gradient detaching to improve the initialization and a channel quality indicator (CQI) correction term (CCT) to correct SINR estimates. Compared to other proposed LA approaches, TROLL is simple to implement and only slightly increases complexity. Our DRLLA approach takes the next step and introduces enhancements and advantages, such as a continuous learning characteristic enabled by RL, which which leads to only a moderate increase in complexity.  
Unlike the approaches by [PERSON] et al. and [PERSON] et al., the CQI prediction approach using a hidden Markov model (HMM) by [PERSON] et al. [10] does not require ACK/NACK feedback. They use an HMM to predict the next CQI through an estimated probability density function (PDF) for the SINR. This approach helps deal with highly dynamic channels, one of the main challenges faced by LA. In contrast, we focus on a combination of SINR and PER for our feedback system during the training procedure.  
In [11], [PERSON] et al. explore using RL for LA, considering the received signal strength indicator (RSSI) as the observed state. A middle layer was created to enable simpler interactions between the signal processing toolkit GNU Radio and their RL models, allowing the Gym framework to interact with a prebuilt flowgraph. In this paper, we build on the open-source GrGym framework, which emerged from the work in [11].
# Fermentation Strategies for Production of Pharmaceutical Ternenoids in Engineered Yeast
## 6 Modes of Fermentation Process
### Continuous Mode  
Continuous processes (or chemostat cultivations) have been employed in industrial bioprocesses (e.g., insulin production) and have several advantages over batch and fed-batch, such as decreased costs of bioreactor constructions, minimized plant maintenance and operation costs, convenient bioprocess control, requirement of less downtime for tank cleaning and maintenance, and higher productivities [83; 103]. One important advantage of this process is to provide a precise comparison of productivities of selected engineered strains under constant conditions and to investigate the effect of the growth rate independently of the other parameters [61]. However, only some studies have applied chemostat cultivation for terpenoid production. Ref. [61] employed continuous cultivation and achieved \(\alpha\)-santalene productivity of 0.036 Cmmol/(g biomass)/h at the dilution rate of 0.05 h\({}^{-1}\). In another study, squalene accumulation of 30 mg/g (product/dry cell weight) was obtained at low dilution rates between 0.05 h\({}^{-1}\) and 0.20 h\({}^{-1}\) in glucose limited chemostat cultivation [120]. Despite demonstrated applicability in terpenoids production, continuous fermentation has been poorly explored, mainly because fed-batch fermentation has been shown to be the most effective way for obtaining meaningful terpene titers for industry, combining the advantages of batch and continuous mode at once.
## 3 Low-Strain Integrity Testing
### Acoustic Emission (AE)  
AE testing is a real-time and NDT technique. The schematic diagram of AE testing shows the overall working principle of the AE monitoring system, as shown in Figure 8[82]. The principle underlying AE is based on the generation of transient elastic waves due to the rapid release of internal energy within the material during the stress process [83]. AE testing necessitates two primary components: the material deformation that generates transient elastic waves and the transducers that detect these stress waves. Elastic vibration signals are generated using transducers positioned on the surface to capture localized dynamic changes in the material. The transducer converts the mechanical wave signals into electrical signals and determines the acoustic source properties. Owing to its non-destructive nature, high sensitivity, real-time monitoring capability, and wide monitoring range, this method has been widely used in bridge health monitoring [84], fiber-reinforced composite damage identification [85], and concrete beam damage monitoring [86].  
[PERSON] et al. [87] utilized AE techniques and shaking table tests to assess the structural integrity of the foundation during earthquake simulation. Through analysis of AE parameters obtained from the shaking table tests, they established optimal filtering criteria with thresholds of 48 dB amplitude, 18 AE counts, and 1000 aJ absolute energy for accurate damage localization. The study concludes that the AE monitoring system can be used to assess the damage to foundation structures during seismic events and applied to the real-time monitoring of substructures of bridges or piles of ultra-high-rise buildings.
## 3 _Drosophila_ CMT Models  
Current studies employing next-generation sequencing identified more than 80 genes associating with CMT [31]. As _Drosophila_ lacks myelin sheaths and Schwann cells surrounding the axon of neurons, it is only suitable as axonal-type CMT models [3]. More than 35 _Drosophila_ homologues of causative genes of CMT in humans have been identified. Many of them may be causative genes specific for the formation of abnormal axonal phenotypes observed in axonal-type CMT [3].  
Various _Drosophila_ models of CMT targeting different genes have been established and recently reviewed in detail: genes encoding proteins involved in mitochondrial dynamics, such as Mitofusin 2 (MFN2), Ganglioside-induced differentiation associated protein 1 (GDAP1), and solute carrier family 25 member 46 (SLC25A46); genes encoding proteins involved in lysosome and endosome functions such as Factor-induced gene 4 (FIG4) and RAB7; genes encoding aminoacyl-tRNA synthetases; genes encoding proteins involved in axonal transport such as Kinesin family member 1A (KIF1A) [32]. Many of these _Drosophila_ CMT models exhibit common phenotypes such as larval crawling defect, adult climbing defect, and aberrant morphology of synapses at the NMJ [3; 32]. A genetic link between _Drosophila_ _FIG4_ (_dFIG4_) and _lpo_ was also reported, suggesting that _lpo_ is involved in a common pathway in the pathogenesis of CMT and ALS [33].
# Implementation Studies of Bacteriophage \(\lambda\) O
## 3 Discussion
### O:P Interaction Effects  
The functional cooperation of O and P in \(\lambda\) replication initiation was suggested by genetic studies [49]. The N-terminal region of O was suggested to contain a DNA binding domain and the COOH-terminal region to contain a P-binding domain [40; 50; 51], with the domains separated by a flexible linker region [52]. [PERSON] and [PERSON] [5] showed that O protein binds to each ITN as a dimer, thus _ori_\(\lambda\) should bind four dimers, with higher order binding suggested [53] to form an O-some that produces torsional stress on the adjacent AT rich region causing the double-stranded DNA to become slightly destabilized and partially unwound [54]. The N-terminal portion of P was assumed to contain an O-binding domain [55], while its COOH-terminal domain was suggested to interact with the host DnaB replicative helicase [55; 56]. It has been suggested that a complex between O and P is formed that can be independent of DnaB [57; 51].  
In essence, the idea was advanced that O bound to _ori_\(\lambda\) is a display platform that is recognized by P:DnaB. However, we show that the co-expression of \(O\)-\(P\) results in several phenotypic effects which suggest that this idea is too simplistic. The co-expression of \(O\)-\(P\) nullifies the inhibitory effect of \(P\) expression on cell growth, for over four hours, and it reduces cell killing caused by prolonged expression of P (i.e., when expressed at 39 \({}^{\circ}\)C). In contrast, the co-expression of \(O\)-\(P\) nullifies the ability of O to complement. These opposed activities suggest that O and P physically interact without having O bound to _ori_\(\lambda\). Combining O expression with the possibility for expression of the N-terminal portion of P eliminates O complementation, suggesting that O binding to the N-terminal portion of P prevents its useful binding to _ori_\(\lambda\), which is presumably a requirement for O complementation activity. However, the co-expression \(O\)-\(P\) does not temper the ability of P to cause plasmid loss. Thus, while the expression of \(P\), or N-terminal portions of \(P\), can obviate O complementation, coordinate \(O\) expression does not fully nullify all the P-lethality phenotypes.
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 3 Methodology
### Evaluation Metrics: BLEU  
Bilingual Evaluation Understudy (BLEU) [12] is a widely used metric in machine translation. Given a sentence predicted by the model and the reference translation (usually generated by humans). The BLEU score represents the similarity between them. To compute BLEU, the first step is, for \(n=1\) to \(N\), counting \(c_{n,total}\), the total n-grams in the prediction, and \(c_{n,match}\), the n-grams in the prediction that are also found in the reference translation. We stop counting some n-grams in \(c_{n,match}\) when they appear more frequently in the prediction than in the reference translation. Then we obtain \(P_{n}=c_{n,match}/c_{n,total}\) and a brevity penalty as Equation (17):  
\[BP=\min\{\exp(1-\frac{length_{ref}}{length_{pred}}),1\} \tag{17}\]  
which is introduced to reduce the score for short prediction. Finally, the BLEU is calculated as Equation (18):  
\[BLEU=BP\cdot\exp(\sum_{n=1}^{N}W_{n}\ln P_{n}) \tag{18}\]  
In practice, we set \(N=4\) and all weights \(W_{n}=0.25\), as in the original literature. We multiply 100 by the original BLEU value to make it a score in a 0 to 100 interval by convention.
## 4 Simulation and Analysis  
The algorithm proposed in this chapter is simulated using Simulink, and two scenarios are considered for simulation verification. Scenario 1: In order to fully verify the control effect of the anti-saturation auxiliary controller, the proposed finite-time anti-saturation control method (FACM) and nonlinear dynamic inversion (NDI) are applied. It is assumed that the disturbance is known during the simulation process, and the UAV needs to perform a large angle maneuver in a short period at this time, which causes the control output of the flight system controller to reach the saturation state. Figure 3: Compare observations with study [31]. The desired attitude is as follows:  
\[roll_{d}=0.32\sin(1.3t+0.5\pi)+0.3,\ pitch_{d}=0.32\sin(1.3t)+0.5,\ yaw_{d}=0\]  
The initial angle of the drone is \(\mathbf{\Theta}_{0}=[0;0;0.3]^{T}\) rad. The moment saturation intervals for the roll and pitch channels are \([-0.03,0.03]\mathrm{N}\cdot\mathrm{m}\). The moment saturation interval of the yaw channel is \([-0.005,0.005]\mathrm{N}\cdot\mathrm{m}\). The moment of inertia matrix is \(\mathbf{G}=\mathrm{diag}(0.004021,0.004021,0.006564)\mathrm{kg}\cdot\mathrm{m}^ {2}\). The controller parameters are shown below:  
\[\mathbf{C}_{1}= \begin{bmatrix}3.75&0&0\\ 0&3.75&0\\ 0&0&3.75\end{bmatrix} ,\ \mathbf{C}_{3}= \begin{bmatrix}15&0&0\\ 0&15&0\\ 0&0&15\end{bmatrix} ,\ \mathbf{C}_{2}=\mathbf{C}_{4}=\mathbf{I}_{3\times 3}\]  
\[\mathbf{K}_{1}= \begin{bmatrix}5&0&0\\ 0&5&0\\ 0&0&5\end{bmatrix} ,\ \mathbf{K}_{2}=\mathbf{I}_{3\times 3},\ \mathbf{K}_{3}= \begin{bmatrix}2&0&0\\ 0&2&0\\ 0&0&2\end{bmatrix} ,\ \mathbf{K}_{4}= \begin{bmatrix}0.1&0&0\\ 0&0.1&0\\ 0&0&0.1\end{bmatrix} ,\ \mathbf{b}_{1}= \begin{bmatrix}0.05&0&0\\ 0&0.05&0\\ 0&0&0.05\end{bmatrix} \]  
\[m=99,\ n=103,\ \lambda_{1,0}= \begin{bmatrix}0&0&0\end{bmatrix} ^{T},\ \lambda_{2,0}= \begin{bmatrix}0&0&0\end{bmatrix} ^{T}\]  
The simulation results are shown in Figures 4-6. Figure 4 shows the moments applied to the quadrotor UAV. It is obvious from the figure that, under both control algorithms, the controller output saturation occurs. At the initial moment, compared to the algorithm without an anti-saturation auxiliary controller, the FACM algorithm is in the saturated state for less time, although the saturation of the output moment also occurs. Therefore, the FACM algorithm proposed in this paper minimizes the duration of control moment saturation. Figure 5 shows the attitude tracking results of the quadrotor UAV. In general, the attitude tracking error tends to become zero around 2.5 s with or without the anti-saturation auxiliary system in the case of known disturbance, so both control methods achieve good attitude tracking results. At the beginning of the simulation, the errors of both control methods are larger before 2.5 s because the initial attitude angle is not consistent with the desired attitude.
## 2 Reactive Routing Protocols and Application of Jitter
### Basic Operations of Reactive Routing Protocols  
In a reactive routing protocol, routes are computed on demand, _i.e._, a router seeks to construct and maintain paths to a destination, only when it has data to deliver to that destination. Consequently, control traffic is generated in response to data traffic only; absent data traffic, no control traffic is generated. Furthermore, as long as data traffic flows along an already established path and no errors are incurred, no control traffic is generated for maintaining the path. Finally, each router is required to maintain the state only for the active paths, on which it is an intermediary, as opposed to, e.g., a complete topology map, as in a classic link state protocol. Unsurprisingly, the main mechanisms in a reactive routing protocol are denoted route discovery and route maintenance.  
Reactive routing protocols have been explored in academic literature, as well as have been published as experimental protocols by the IETF: the Ad hoc On-Demand Distance Vector (AODV) protocol, as RFC 3561 [18] in 2003, and the Dynamic Source Routing (DSR) protocol, as RFC 4728 [19], in 2007. Reactive protocols typically find use in wireless _ad hoc_ and sensor networks, due to the limited amount of states required in each router. For example, LOADng (Light-weight On-demand Ad hoc Distance-vector routing-Next Generation)--a simplified and extended version of AODV [20]--is the routing protocol component of the G3-PLC (Power Line Communication) ITU-T (International Telecommunication Union Telecommunication Standardization Sector) standard for communication in the \"smart grid\" [21].
# Design of an In-Process Quality Monitoring Strategy for FDM-Type 3D Printer Using Deep Learning
## 2 Development of In-Process Monitoring System
### Residual Blocks  
In the proposed architecture, a stacked dilated convolution inspired by [21] has a receptive field \(r_{f}\) that defines the number of neurons in the input that influences the predicted values produced on the final layer. To ensure that the predicted values solely based on the historical values of the series \(x\), a causal convolution is used. Each of the neurons are subjected to a filter size of 256 and a \(d=[2^{0},\cdots,2^{l-1}]\) increment the dilation factor of each layer \(l\). About on the individual layer, a residual block is implemented instead of directly applying the architecture presented in [22]. The residual block applied in this research was composed of batch normalization, dilated convolution, and non-linearity. These displayed blocks efficiently support the network stabilization and training.
## 3 Results of Global-Local Analysis
### Results  
#### 3.3.1 Convergence of the Different Cases  
In all the cases studied, the method converges to very small values after four iterations (see Figure 4). For case I and case II with a mesh size of 1 mm and case III with a mesh size of 0.5 mm, the error goes directly to 0, while for the other cases, the error tends to \(10^{-15}\) or lower, showing that the methodology has converged.
## 2 Semi-Analytical Solution for the Coupled Track-Ground-Tree Grid System  
This section presents a semi-analytical approach for computing the dynamic response of the coupled track-ground-tree system subjected to train-induced vibrations, with the aim of analyzing the attenuation performance of tree grids against railway-induced ground vibrations. A three-dimensional model is applied, as shown in Figure 1. Firstly, the fundamental solutions for the layered ground are derived using the thin-layer method (TLM).  
These solutions are then employed to establish the coupling between the railway track and the ground by enforcing displacement compatibility and stress continuity at their interface. Forest trees are represented as an array of vertically oscillating rods interacting with the underlying soil. By integrating the train model, tree array, and track-ground system, the ground vibrations generated by a moving train are evaluated, with the vibration attenuation effect of the trees explicitly considered.  
Figure 1: Schematic diagram of a tree grid coupled to a stratified half-space with track structures.
## 4 Conclusions  
This paper proposes an innovative thin-film bulk acoustic wave resonator design tool powered by an upgradable library. The decision algorithm is based on exploiting the mass-loading effect. Unlike the available simulation, the only information needed is the calibration curve expressing the trend of the phase velocity versus the top-electrode thickness.  
The calibration curves are reusable and arranged into a modular library that can be easily enriched with new materials, even starting from experimental data. Moreover, the algorithm includes the possibility of considering fabrication tolerances and bridging the gap between the simulations and the experimental devices.  
Furthermore, the algorithm is tailored to be shared and expanded with new data refining the potential, and arbitrary values of fabrication tolerances [19].  
The effectiveness of the tool is demonstrated through the design and fabrication of an innovative air-gap FBAR based on an organic sacrificial layer and with a flexible membrane. The tool is successfully taken to account for the fabrication tolerances, and the working frequency of the resonator of 2.545 GHz is within the 95% confidence interval estimated by the design tool. The proposed fabrication protocol is demonstrated to be effective and straightforward as the device has an optimal electromechanical coupling coefficient equal to 4.7% and Q-factors of 1426 and 1467 for the resonance and antiresonance frequencies, respectively. We strongly believe that this new approach represents a strategic step forward in developing high-performance FBARs and can be applied to the design and fabrication of resonators or filters.  
Conceptualization, G.N. and I.M., methodology, G.N., I.M., F.R. and M.G.; software, G.N.; validation, G.N. and I.M.; resources, A.D. and M.D.V.; writing--original draft preparation, G.N., I.M. and F.R.; writing--review and editing, G.N., I.M., F.R., A.D., M.G. and M.D.V.; supervision, M.G. and M.D.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Design Technique** & **Computational** & **Relative Error** & **Scalability** & **Fabrication** & \(Q\) & \(k_{eff}^{2}\) & _F.O.M._ \\  & & **Cost** & **[\%]** & **Tolerances** & \(Q\) & \(k_{eff}^{2}\) & _Q.\(k_{eff}^{2}\)_ \\ \hline
[13] & FEM & HIGH & N.A. & LOW & N.A. & 1548 & 1 & 1548 \\ \hline
[14] & FEM and ECA & VERY HIGH & N.A. & LOW & N.A. & 2507 & 2.12 & 5314 \\ \hline
[15] &
\begin{tabular}{c} FEM and \\ deep learning \\ \end{tabular} & HIGH & 0.2–0.3 & HIGH & N.A. & N.A. & N.A. & N.A. \\ \hline
[16] & FEM & VERY HIGH & 0.2 & MEDIUM & N.A. & 59.8 & 4.1 & 245.18 \\ \hline T.W. & Calibration Curves & LOW & 0.2 & HIGH & Yes & 1467 & 4.7 & 6895 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between this work (T.W.) and the state of the art.
## 3 Discussion  
Pullulan, which is a versatile and valuable EPS produced by _Aureobasidium_ strains, has diverse applications that benefit both health and the environment [4]. Some wild-type _Aureobasidium_ strains can achieve relatively high pullulan levels (>100 g/L) [1], while the majority of them are limited to low levels of pullulan production (\(\leq\)78 g/L) [3; 8; 12]. Various approaches, including strain screening, mutagenesis, and genetic modification, have been explored to enhance pullulan production [1; 9; 14]. However, these methods come with challenges, such as genetic instability and strain degeneration due to genomic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Genes**} & \multirow{2}{*}{**Contents/Functions**} & \multicolumn{2}{c}{**Relative Gene Expressions of Strain ZH27 (\%)**} & \multicolumn{2}{c}{**Changes in Gene**} \\ \cline{3-5}  & & **IC** & **OC** & **Expressions under the OC** \\ \hline FF2 & Fructosyltransferase & 100 & 231.8 \(\pm\) 56.7 \(\ast\) & Upregulation \\ FF3 & Fructosyltransferase & 100 & 2152.2 \(\pm\) 37.9 \(\ast\) & Upregulation \\ FTR1 & Transcriptional factor of & 100 & 333.1 \(\pm\) 86.7 \(\ast\) & Upregulation \\ \(P\)FK26 & Phosphofructo-2-kinase & 100 & 257.8 \(\pm\) 25.5 \(\ast\) & Upregulation \\ \(F\)BA1 & Fructose-bisphosphate atolase1 & 100 & 524.7 \(\pm\) 60.5 \(\ast\) & Upregulation \\ \(G\)3P & Glyceraldehyde-3-phosphate & 100 & 523 \(\pm\) 102.6 \(\ast\) & Upregulation \\  & dehydrogenase & & & \\ \(PGK\) & Phosphoglycerate kinase & 100 & 484.1 \(\pm\) 18.7 \(\ast\) & Upregulation \\ PYK & pyruvate kinase & 100 & 203.6 \(\pm\) 28.9 \(\ast\) & Upregulation \\ \(PYD\) & Pyrruvate decarboxylase & 100 & 245.1 \(\pm\) 64 \(\ast\) & Upregulation \\ _AreA_ & Transcriptional activator of & 100 & 135.7 \(\pm\) 20 \(\ast\) & Upregulation \\  & nitrogen & & & \\ _AreB_ & Transcriptional repressor of & 100 & 85.3 \(\pm\) 11 \(\ast\) & Downregulation \\  & nitrogen & & & \\ \hline \hline \end{tabular}  
* Notes: \(\ast\ast\ast\) represents the probability value below 0.05, and \(\ast\ast\ast\ast\) means the probability value below 0.01.  
\end{table}
Table 1: \(Cont.\)rearrangements [24]. The genetic instability of mutants [24] and their limited increase in pullulan titers [1; 25] have posed challenges for industrial pullulan production, emphasizing the urgent demand for wild-type strains with high pullulan-producing abilities. In this study, we identified and investigated promising target strains with the potential for high pullulan production, confirming the EPS produced as pullulan. Subsequently, we delved into changes in cell morphology and gene expressions during pullulan synthesis after optimizing conditions.