esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 2 Quasi-Floating Gate Transistors
### Application of QFG Transistors to Energy-Efficient Amplifier Design
#### 2.2.5 Class AB Current Mirrors  
Current mirrors are widely used in amplifier design as current followers, i.e., to convey currents from a low-impedance input node to a high-impedance output node. For instance, they are used in current mirror (symmetric) amplifiers to convey (and optionally scale) the differential pair current to the output node. When these current mirrors must process bidirectional signal currents, class AB operation is required to achieve a class AB amplifier [22]. A schematic diagram of a class AB current mirror is shown on the left side of Figure 4e. Again, a floating battery \(V_{AB}\) translates signal variations from node A to B,allowing dynamic currents larger than the quiescent currents. A QFG implementation is shown in Figure 4e [31], which consists, basically, on the replacement of the PMOS current sources on the left side by a two-output adaptive current source, like in Figure 4b. As before, this implementation features accurate quiescent currents not dependent on PVT variations. \(V_{AB}\) is set by the difference between quiescent voltages at nodes A and B and can be either positive or negative. No extra supply voltage or quiescent power is required.
# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 6 Results
The work in [128] applied a deep Gaussian process to train a regression model on a small sample data of the acceleration, braking, and steering angles to capture the most significant autonomous driving features. The paper reports an achievement by using only 0.34% real-time input data and producing the same accurate result as that of Torcs (an open racing car simulator [136]). The work in [107] addresses the relationship between land use and mobility by applying a KNN clustering approach and using two weeks of mobile phone data to group origin/destination (OD) trips. The paper reports an 80% accuracy for OD land use category prediction for weekdays and 67% for weekends. The work in [126] applied reinforcement learning on the three parameters of the cycle, arterial coordination offset, and green split of the multiple intersection signals to optimize timing schemes. The paper concludes that the traffic control method based on reinforcement learning performs better in complex traffic situations but is not applicable to all traffic conditions. Traffic management benefits from automated license plate recognition (ANPR). ANPR depends on an optical character recognition (OCR) technique, while ML algorithms are supreme in OCR. As a result, ANPR has a long history in ML algorithm applications [137]. The work in [56] proposes an ANPR based on a feature extraction model and a backpropagation neural network that is adaptable in weak light and complex backgrounds. The paper reports a 97.7% accuracy with 46.1 milliseconds of processing time. The work in [76] presents a framework for safely approaching an exit ramp by concentrating on the CAV network in a multi-lane road corridor. The framework applies a deep reinforcement learning algorithm that combines a graphic CNN with a deep Q-network as a control algorithm. The CAVs on a highway are presented as a GNN, while messages are passed based on the graph. The work in [119] proposes a rear-end collision detection model by applying a global navigation satellite system with accurate positioning data, a digital compass, and lane information. The model combines a cubature Kalman filter and applies an adaptive neuro-fuzzy inference system to judge the car-following status. A 99.61% prediction accuracy in the field test result is reported.
## 2 Energy Management and Review of Surveys
### Optimization Techniques
Then, the authors classified the AI techniques in monitoring tasks to K means, Fuzzy rules or rule-based approaches, ANN, regression algorithms, support vector regression (SVR), density-based spatial clustering of applications with noise (DBSCAN), random forest (RF), evolutionary algorithms, K-nearest neighbor (KNN), and transfer kernel learning. In addition, the main AI techniques used in analysis tasks are classified as: LSTM, auto-encoder, regression neural networks, suggesting convolutional networks (CNN), extreme learning machine (ELM), regression forest, SVR, RF, radial basis functions network, autoregressive integrated moving average (ARIMA), Seasonal autoregressive integrated moving average (SARIMA), and eXtreme gradient boosting (XGBoost). The used AI techniques in control tasks are deep learning (DL), multi-agents, Fuzzy, ANN, and multi-objective. Different techniques in optimization tasks are mentioned as: multi-objective approaches, evolutionary approaches, Fuzzy models, ELM, multi-agent systems, Z-number, reinforcement learning, and bio-inspired approaches. The most important AI techniques in scheduling tasks are bio-inspired approaches, deep reinforcement learning (DRL), and multi-agent systems. [PERSON] et al. [16] focused on the idea of the advanced energy management system (AEMS) to smooth energy flow in a campus MG. For this purpose, the authors introduced some relevant projects and addressed the optimization techniques including high-reliability distribution system (HRDS), control and management system operation, MILP, MICP, PSO, TLBO, multi-agent system (MAS)-based, two-stage stochastic programming, MINLP, and NSGA. [PERSON] et al. [13] addressed some of the optimization methods: AHP, cuckoo search/bat algorithm, DP, genetic optimization/generating sets search algorithm, GWO, hybrid robust/stochastic, imperialist competitive/MCS, multi-objective GA, NSGA II/Fuzzy clustering, decentralized control/multi-agent, MINLP, PSO/Q-learning, stochastic programming, two-step method, and weighted majority algorithm. [PERSON] et al. [1] addressed different mathematical-based methods suggested for the OPF problems: distributed and parallel OPF (DPOPF), multiphase OPF (MOPF), OPF approach based on linearization and approximation, OPF approach based on considering storage devices, unbalanced three-phase OPF (TOPF), alternating direction method of multipliers (ADMM), OPF based on the simultaneous formulation of the post-contingency flows, and uncertainty-based OPF models. These approaches can be handled in terms of system type (three phases versus single phase), system balance (balanced versus unbalanced), operational state (islanded versus grid-connected mode), network topology (distribution versus transmission), programming model (dynamic versus static), control strategy (centralized versus decentralized control), multi-agent versus central agent, solution algorithm (mathematical approach versus heuristic algorithm), and realistic description (deterministic versus uncertainty).
## 3 Traffic Management Model
### Infrastructure Unit Workflow  
The Infrastructure Unit (Figure 3) collects control information such as the position, location, speed, and time signature of the vehicle. The information is iteratively collected to ensure that the information is accurate. The data are gathered by the BAs and not directly transmitted to infrastructure units.  
The information from IoT-BAs is sent to the DNN, and the routing path is processed based on the routing information collected from BAs (routing process is the same as in [64]). The BAs route BAs with a code execution unit and set the route to prevent the congestion of the VANETs. All operations are carried out to prevent the greatest challenge of congestion.
# Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
## 3 Matrix Propagation Model for Optical Fibers
### Time Skew between Polarizations and Modes  
Another parameter that can affect the clock recovery performance is the time skew between signal components [22]. So, apart from the DGD, the signal components can be time mismatched due to propagation differences in the electrical cables both at the transmitter and the receiver. We can model the time skew between signal components, \(\mathbf{W}(\Omega)\), by:  
\[\mathbf{W}(\Omega)=\text{diag}\left[\exp\{-j\Omega\tau_{1}\},\exp\{-j\Omega \tau_{2}\},\cdots,\exp\{-j\Omega\tau_{Z}\}\right]. \tag{19}\]  
Therefore, regarding the time skews, the received signal is related to the input signal by,  
\[\mathbf{E}_{out}(\Omega)=\mathbf{W}_{Rx}(\Omega)\mathbf{M}(\Omega)\mathbf{W}_ {Tx}(\Omega)\mathbf{E}_{in}(\Omega). \tag{20}\]  
In the next sections, we will analyze the clock recovery performance evaluating the clock tone amplitude for the QPSK modulation format. Higher-order QAM modulation formats are expected to have similar performance as the spectrum shape of these formats are invariant to the modulation order [30].
# Respiratory Muscle Strength as a Predictor of VO2 max and Aerobic Endurance in Competitive Athletes
## 2 Materials and Methods
### Data Collection Tools
#### 2.3.3 Assessment of Aerobic Capacity (VO\({}_{2}\)max)  
In this study, maximal oxygen uptake (VO2 max) was evaluated using a portable cardiopulmonary exercise test system (Cosmed K5, Italy, Serial No: 2019030706), renowned for its precision in automatically analyzing expiratory gases. The system was calibrated with a known gas mixture (5.0% CO\({}_{2}\) and 16.0% O\({}_{2}\)) to ensure accuracy in measurements. The VO2 max test protocol started with a 2-min warm-up phase at a constant speed of 5.0 km/h on the treadmill. After this initial phase, the treadmill speed increased incrementally by 0.016 km/h every second, providing a gradual rise in intensity. This specific speed increment was selected to ensure a smooth transition between workloads while pushing the participants toward their maximal aerobic capacity. Athletes were monitored throughout the test, with adjustments continuing until they met at least three of the following termination criteria: a rating of perceived exertion (RPE) of 17 or higher on the Borg scale, verbal indication of exhaustion, no further increase in oxygen consumption despite rising workload, a respiratory quotient (RQ) of 1.15 or above, achieving 85% or more of their maximal heart rate, or a plateau in heart rate despite increasing effort. The average values recorded during the final 30 s of the test were used to calculate the participants' maximal oxygen consumption, normalized to their body weight [25]. The measurements included minute ventilation (VE), oxygen uptake (VO\({}_{2}\)), and carbon dioxide production (VCO\({}_{2}\)), all captured in real time by the gas analysis system. To avoid bias, different researchers conducted the test administration and data analysis, ensuring that the study's blinding technique was effectively implemented.
## 5 Discussion
### Along-Arc and Inter-Arc Volcanic Gas CO\({}_{2}\)/S\({}_{\rm T}\) Variations
Further, seismic reflection data indicate that most of the sediments are indeed subducted [60]. In fact, the same authors argue that, in the region, the addition of a significant subducted sediment component has variably modified the mantle wedge over time. Within the less-surveyed northern Vanuatu region, distinct geochemical patterns can be observed. For instance, lavas from Gaua have lower \({}^{143}\)Nd/\({}^{144}\)Nd than lavas from Merelava, despite similar Sr- and Pb-isotope compositions. This can be explained in terms of a common Indian-MORB-like mantle source but would indeed require a higher contribution from subducted sediments to Gaua lavas [60]. Ultimately, the mismatch between the high C input (sediments) and the low C output (volcanic gases) may simply reflect a limited C recycling by fluids delivered by the slab, leading to sedimentary C transport deep in the mantle [7]. We caution, however, that our discussion is based on one single, brief campaign at Gaua, while more continuous and prolonged gas records would be required to fully capture the volcano's time-averageddegassing behavior and gas composition. Indeed, gas compositions exhibit both long-term changes in response to changes in volcano behavior (e.g., as persistent degassing activity transitions into unrest and finally eruption) [19], and also more subtle short-term changes related to rapid events of gas segregation and explosive burst. At Yasur volcano, for example, the bulk composition appears to have remained relatively stable since the 2000s, yet with CO\({}_{2}\)/\({}_{\rm ST}\) ratios spanning between 1.6 and 2.2 [30; 31; 32]. In addition, short-term (timescales of seconds to minutes) fluctuations in gas compositions between 'passive' (CO\({}_{2}\)/S\({}_{\rm T}\)-1.9) and 'active' (CO\({}_{2}\)/S\({}_{\rm T}\)-2.9; during strombolian explosions) degassing regimes have also been observed [30; 32]. Ultimately, additional gas observations at Gaua and other Vanuatu volcanoes is required for a more robust assessment of C recycling efficiency along the arc.
# Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques
## 3 Methodology
### Multivariate Adaptive Regression Splines (MARS)
#### 3.2.1 Forward Phase  
Forward selection phase is the same as the case in piecewise linear regression; it just differs in the point that it uses the A set functions (including all of the functions of \(h_{1i}\), \(h_{2i}\), \(i=1,\ldots,n\) and product of two or more of these functions) instead of explanatory variables. This phase is started by considering y-intercept term \(a0\) (average of response values), then the basis functions are repeatedly added as pairs, and corresponding \(\beta\) coefficients are estimated in Equation (7) by minimizing the SSE. In each step of this phase, MARS finds a pair of basis functions that have the highest reduction in SSE. At the end of the phase, a large model is formed with a large number of terms having unsuitable performance in response to the prediction for new data. In other words, this model causes data over-fitting; therefore, it will need a backward deletion phase to achieve the best number of terms [86].  
Figure 4: Basis functions diagram (**a**) h1 and (**b**) h2 values.
# Gut Microbiota as an Endocrine Organ: Unveiling Its Role in Human Physiology and Health
## 3 Neurohormones and Their Role in Microbial Effects
### Neurohormones: Bridging the Gap between the Brain and Microbial Effects
#### 3.2.1 Gut Microbiota Modulation of Serotonin Dynamics
For instance, antibiotic-treated mice exhibit a decrease in colonic serotonin levels, highlighting the role of microbiota in regulating serotonin-driven gut motility [58]. Additionally, the impact of gut microbiota on serotonin synthesis by ECs and the role of bacterial enzymes in supporting luminal serotonin levels add layers of complexity to the microbiota-gut motility axis [59]. The gut microbiota's role in the development of conditions such as autism spectrum disorders (ASDs), neurological disorders, and functional gastrointestinal disorders highlights its significance beyond merely regulating serotonin [60]. The dysbiosis in the gut microbiota correlates with altered tryptophan levels, shifts in serotonin pathway metabolites, and the manifestation of gastrointestinal symptoms. Examples include the increase in mucosa-associated _Clostridiales_ and alterations in serotonin pathway metabolites observed in children with ASD and functional gastrointestinal disorders [52]. Understanding these correlations sheds light on the intricate relationships between the gut microbiota, serotonin, and the manifestation of neurological and gastrointestinal disorders. In conclusion, the dynamic tapestry woven by the gut microbiota in regulating serotonin across systemic organs illuminates its significance in maintaining physiological equilibrium. Unravelling these complex interactions enhances our comprehension of the gut-brain axis and unveils potential therapeutic avenues for addressing disorders linked to serotonin dysregulation. This holistic perspective underscores the need for further exploration to harness the therapeutic potential of modulating the gut microbiota in influencing serotonin pathways.
# The Synthesis and Biological Evaluation of 2-(1H-Indol-3-yl)quinazolin-4(3H)-One Derivatives
## 2 Results and Discussion
### In Silico Characterization of Substituted Indoles Binding to Long RSH Proteins  
Searching for new chemical compounds that are targeted to non-essential bacterial adaptation pathways, such as stringent response, is an attractive approach for developing antimicrobials preventing bacterial persistence, biofilm formation, quorum sensing, and virulence [56]. Previously, researchers have identified indole derivatives as a chemotype capable of binding the RelSeq (p)ppGpp synthetase/hydrolase synthetic domain via virtual screening and thermal shift assays [26]. Based on these data, we selected a set of substituted indoles from our library of compounds to assess the possibility of their binding to RelMtb or RelSeq proteins in synthetic domain (SYN) active sites by using molecular docking methods. The RelMtb crystal structure unbound to the substrate [57] was used to analyze the ligand binding to an inactive state synthetase, while the RelSeq structure complexed with a GDP (guanosine diphosphate) substrate [58] was used to investigate the binding to (p)ppGpp synthetase in a catalytically competent state. The reference ligand indole-5-carboxylic acid demonstrated a RelSeq active site binding energy of \(-6.42\) kcal/mol (Table 4), consistent with the result of \(-6.41\) kcal/mol calculated in the previous research (in which ligand mentioned as I2) [26].  
The docked ligands interacted with the Arg241 and His312 residues (Figure 4b,c), which are involved in GDP substrate binding [58]. IndolyIquiniazoliones and their derivatives demonstrated weaker binding in the RelSeq active site than the reference ligand, with the most negative docking score of \(-4.76\) kcal/mol (Table 4). This might have been due to the planar structure of the analyzed compounds, lacking the flexibility to optimally fit the tight binding pocket in the substrate-bound state (Figure 4b,c). However, eight analyzedcompounds demonstrated stronger binding energies (from \(-7.61\) to \(-6.56\) kcal/mol) compared to the reference ligand (\(-6.55\) kcal/mol) in case of the substrate-unbound RelMtb structure SYN active site (Table 4).  
Moreover, the indole derivatives interacted with the Tyr309 residue (Figure 5a-c), which has been shown to be essential for RelMtb (p)ppGpp synthesis [59]. Indole derivatives are therefore promising candidates for developing inhibitors of (p)ppGpp synthetases.  
Figure 4: Best energy interactions between ligands and RelSeq protein synthetic domain active site. (**a**) Reference ligand indole-5-carboxylic acid; (**b**) **3o** compound; and (**c**) **3 ab** compound. Protein chains are shown in grey thin sticks, ligands are shown in blue thick sticks, interactions are shown in dashed lines: yellow hydrogen bonds, blue pi–pi stacking and green pi-cation interactions, purple salt bridges.
## 5 Results and Discussion
### Evaluation of the Explanation Methods
The steep slope of the MAZ curve around zero indicates that the explanations of the corresponding method are sparse. We note that the explanation methods, particularly the LIME, that captured the dominant contribution features in Figure 9 show sparsity between these features, as shown from the steep slopes of the MAZ curves in Figure 10. This observation can be clear by finding the area under the MAZ in Table 14. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Method** & **Dominant Contribution Features** \\ \hline
**Benign** & LIME & 5, 31, 26, 83, 72, 55, 51, 36, 18, 35 \\ \hline
**GoldenEye** & LIME & 54, 19, 30, 16, 27, 31, 45, 47, 22, 7 \\ \hline
**Hulk** & LIME & 49, 58, 28, 46, 54 \\ \hline
**Slowhttp** & LIME & 33, 55, 50, 58, 51 \\
**Slowloris** & LIME & 50, 34, 51, 36 \\ \hline
**HOIC** & LORE & 72, 22 \\ \hline
**LOIChttp** & LIME & 54, 19 \\ \hline
**DNS** & LIME & 44, 58, 20, 57, 59, 6, 35, 72, 46 \\ \hline
**LDAP** & LORE & 12, 13, 59, 44, 14 \\ \hline
**MSSQL** & LIME & 4, 20 \\ \hline
**NTP** & LIME & 15, 47, 55, 6, 42 \\ \hline
**NetBIOS** & LORE & 45, 44, 59, 13, 14 \\ \hline
**SNMP** & LORE & 58, 13, 78, 44, 29 \\ \hline
**SSDP** & SHAP & 4, 42 \\ \hline
**SYN** & LIME & 54, 5, 4, 55, 7 \\ \hline
**TFTP** & Anchor & 59, 4 \\ \hline
**UDP** & SHAP & 4, 12, 47, 43, 33 \\ \hline
**UDP-Lag** & LORE & 54, 55, 12, 23, 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Strong contribution features for each class of the CIC datasets. Figure 10: Sparsity measured as mass around zero (MAZ) score on each class of CIC datasets. AUMAZ columns in Table 14 list the value of the area under the MAZ curve of the explanation methods for each dataset class. The MAZ curve that has a steep slope around zero returns a larger AUMAZ value that indicates a sparse explanation. The bold red values in the table are the largest AUMAZ. We also observe that the explanation methods, particularly the Anchor and SHAP, that capture the dense features in Figure 9 do not show the largest values of AUMAZ (bold red values) in Table 14, except for three classes. These classes are SNMP, SSDP, and UDP.
# A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer
## 3 Theory and Method
### Feature Embedded Transformer
#### 3.3.1 Input Data and Data Coding  
There are two kinds of input data in this model: one is feature data, and the other is behavioral data. Feature data include users' gender, age, credit record, and other credit data. The users' behavioral data mainly include the users' online operation behavioral data, such as click behavior and input behavior. After the feature data are processed, they are used as the input of the model.  
Figure 2: Network architecture of the FE-Transformer.  
For the behavioral data, inspired by NLP, each kind of behavior event can be regarded as a word. The behavioral data of each user are composed of a series of events, which constitute a sequence of events and can be regarded as a sentence. We process the raw online operation behavior record data and convert these behaviors into event sequences in chronological order. Then, we encode the input behavioral data via embedding and position encoding.  
An event is the basic unit of model processing. First, the input event needs to be converted into a vector through a word embedding algorithm. In order to understand a sequence of events, the model needs to know the position of the event in the sentence in addition to understanding the meaning of the event. Since the calculation of the transformer abandons the recursion and convolution of the cyclic structure, it cannot simulate the positional information of the events in the sequence, so it is necessary to obtain the positional vectors of the events through positional encoding. The position vector is then added to the event vector to obtain the input to the model. We take the sine function to generate the position vector for each event:  
\[PE_{(pos,2i)}=\sin\left(pos/10000^{2i/d_{model}}\right) \tag{1}\]  
\[PE_{(pos,2i+1)}=\cos\left(pos/10000^{2i/d_{model}}\right) \tag{2}\]  
where _pos_ is the position of the event in the behavior sequence of events, \(d_{model}\) is the dimension of positional encoding, \(2i\) is the even dimension, and \(2i+1\) is the odd dimension (\(2i\leq d_{model},2i+1\leq d_{model}\)). After data coding, the data are used as the input of the transformer layer.