esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved SPGD Algorithm for Optical Phased Array Phase Calibration
###### Abstract  
A chip-level optical beam steerer is an inevitable choice for next-generation light detection and ranging (LiDAR). The research on optical phased array (OPA) is the most intriguing. However, the complexity of control and calibration speed limit the full potential as the number of channels increases. In this paper, an improved stochastic parallel gradient-descent algorithm combined with the Nesterov accelerated gradient method (NSPGD) is presented and applied in a 512-channel OPA. This algorithm can reduce the phase calibration time of large-scale OPA and demonstrates a better convergence performance than traditional SPGD. Compared with the traditional SPGD and hill-climbing (HC) algorithm, optimized convergence performance of NSPGD is shown. The side mode suppression ratio (SMSR) of over 10 dB for 512-channel OPA is obtained with the NSPGD algorithm, and the convergence speed is twice that of traditional SPGD. In addition, a temperature-controlled OPA is also studied to stabilize the whole calibration system.  
optical phased array; LiDAR; phase calibration algorithm; SPGD; temperature controlled OPA Article  
Improved SPGD Algorithm for Optical Phased Array Phase Calibration  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], Y. Improved SPGD Algorithm for Optical Phased Array Phase Calibration. _Appl. Sci._**2022**, 12, 7879. [[https://doi.org/10.3390/app1257879](https://doi.org/10.3390/app1257879)]([https://doi.org/10.3390/app1257879](https://doi.org/10.3390/app1257879))  
Academic Editorwhen used on a large range because PD has to move while the scanning angle of OPA is changing. In addition, traditional SPGD easily falls into local extreme and the convergence speed is slow [17]. In this paper, an improved SPGD combined with Nesterov accelerated gradient (NAG)-NSPGD is proposed. A 512-channel OPA is optimized and calibrated successfully with a traditional InGaAs charge-coupled device (CCD) camera system. In addition, due to the application of a thermo-optic phase shifter, thermal accumulation is difficult to ignore; therefore, we introduce a temperature control structure to maintain stable thermal conditions. Based on our method, we can obtain the optimized OPA emission point, whose side mode suppression ratio (SMSR) is more than 10 dB, and the convergence speed is twice that of traditional SPGD.
# 2D Ultrasonic Antenna System for Imaging in Liquid Sodium
## 2 Imaging Principle of the 2-Antenna System
### Imaging Using Electronic Scanning  
A delay law is applied to each antenna in order to focus its beam at a chosen focusing point in its focusing plane (Figure 2a,b) and, thus, to obtain a very narrow beam in this plane, whereas, due to the convex surface of the elements, the beam is very divergent in its orthogonal plane. Due to its shape, the beam of each antenna is called a \"fan-beam\" [5].  
The intersection of the two fan-beams creates the useful part of the beam in the imaging process, named \"cigar-beam\" because of its shape, and is oriented along an axis located between the E delay law axis and the R delay law axis (Figure 2c). Thus, by applying successive delay laws in order to move the \"cigar-beam\", it is possible to scan and image a 3D zone (Figure 2d).  
Figure 1: Diagram of the antennas’ arrangement and definition of the main terms used in the article: (**a**) Diagram of the two antennas’ arrangement and of the orthogonal section of an element and (**b**) definition of the key terms for one antenna. The central axis is perpendicular to the probe surface at its centre. The delay law axis represents the direction of the deflected beam when delay law is applied. When a delay law is applied, the beam is focused at the focusing point placed at a distance \(d\) as defined in the figure and or deflected by an angle \(\theta\) in the focusing plane of the antenna. The orthogonal plane is perpendicular to the focusing plane.
# Biosynthesis of Rubbellins in _Ramularia collo-cysni_--Genetic Basis and Pathway Proposition
## 2 Results  
### Phylogenetic Analysis of R. collo-cygni Nonreducing-Polyketide Synthases  
A previous study based on phylogenetic analysis and domain organisation of all _R. collo-cygni_ PKSs showed that the genome of _R. collo-cygni_ encodes three NR-PKSs that have been named Pks1, Pks2, and Pks3 [26]. To assess the putative role of the NR-PKSs identified in _R. collo-cygni_, a new phylogenetic study of the proteins RecPks1, RecPks2, and RecPks3 encoded by _Pks1, Pks2_, and _Pks3_, respectively, was carried out (Figure 2). RecPks3 was recovered on the same clade as MpaC and PksAC, the NR-PKSs responsible for the biosynthesis of the backbone of mycophenolic acid and aschochitine in _Penicillium brevicompactum_ and _Aschochytta fabae_, respectively [31, 32]. Two NR-PKSs involved in the biosynthesis of terpenoids, also located in the same clade as Trt4 and AndM, are involved in the biosynthesis of terretonin and andatomin in _Aspergillus terreus_ and _A. stellatus_, respectively [33, 34]. RecPks2 was recovered in a clade that contains enzymes involved in the biosynthesis of perylenequinones such as Pks1 in _Elsinoefavceti_ and CpPks1 in _Cladosporium phlei_ that are responsible for the production of elsinochrome and phleichrome pigments, respectively [35, 36]. RecPks2 is also located on the same clade as two NR-PKSs of unknown function in the closely related fungi _Zymsceptoria tritici_ and _Pseudocerospora fijiensis_. RecPks1 was recovered on the same clade as ClaG and EncA, the enzymes responsible for the production of the anthraquinone-derived SMs cladofulvin and endocrocin in _C. fulvum_ and _Aspergillus fumigatus_, respectively [23, 37]. The NR-PKSs AgnPks1, Acas and MdpG that are involved in the biosynthesis of the anthraquinone SMs agnestin, atrochrysone and monodictyphenone, in _Paeciomyces diuariatus_, _Lasiodioplodia thebromae_ and _A. nidulans_, respectively, were also located on the same clade as RccPks1 [25, 38, 39].
## 4 Experimental Results and Analysis
### Energy Dissipation  
The energy dissipation capacity is a very important factor for assessing seismic performance and evaluating the ability of a structure to absorb applied seismic energy. Cumulative energy dissipation values for each test specimen are shown in Table 5 and Figure 13. Both the NRC and WCFST specimens displayed a significant increase in cumulative energy dissipation at a drift ratio of 0.75%. Such a finding indicates that the amount of seismic energy dissipated by the structures increased rapidly because of the presence of cracks at a drift ratio of 1%. In the case of the NRC specimens, the cumulative energy dissipation was calculated up to a drift ratio of 2%, as failure of the test specimen occurred as the load was applied in a positive direction at a drift ratio of 2.5%. When retrofitted with the WCFST system, the cumulative energy dissipation at a drift ratio of 0.25% was 1.8 times higher than that of the existing reinforced concrete frame. The cumulative energy dissipation gradually increased with a rise in the drift ratio, and dissipation from the WCFST specimen was about 2.5 times higher than that from the NRC specimen at a drift ratio of 2%. From the obtained results, it was confirmed that retrofitting specimens with the WCFST system was an effective strategy for improving the seismic performance by facilitating significant seismic energy dissipation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Drift Ratio (\%)**} & \multicolumn{2}{c}{**Effective Stiffness (kN/mm)**} & \multirow{2}{*}{**Effective Stiffness Ratio of WCFST to NRC**} \\ \cline{2-3}  & **NRC** & **WCFST** & \\ \hline
0.1 & 22.31 & 43.24 & 1.94 \\
0.25 & 18.07 & 38.47 & 2.13 \\
0.5 & 13.67 & 32.11 & 2.35 \\
0.75 & 11.23 & 27.87 & 2.48 \\
1.0 & 9.62 & 24.78 & 2.58 \\
1.5 & 7.40 & 20.43 & 2.76 \\
2.0 & 5.67 & 16.89 & 2.98 \\
2.5 & - & 13.71 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of effective stiffness according to the drift ratio.  
Figure 12: Effective stiffness reduction ratio when compared to the initial stiffness.
## 3 Results
### Application to Clinical Samples  
Plasma levels of Fingolimod and Fingolimod-P have been measured in 15 patients, of which the 33% (5/15) were naive and 10/15 switched from a first-line therapy due to inefficacy. No safety issues were recorded, and all MS patient completed the 24-month period of treatment and follow-up. At month 6, one patient experienced mild radiological activity and another one experienced an increase of the EDSS score (diagnosed as a secondary progressive, SP, patient after 24 months of treatment); all the other patients were classified as NEDA (No Evidence of Disease Activity--no relapse, no radiological activity, no increase in EDSS). After 12 and 24 months of treatment, all patients achieved the NEDA status, except for the SP one at month 24.  
Figure 2 shows the distribution of Fingolimod and Fingolimod-P concentrations in the plasma of 15 patients given the daily oral dose of Fingolimod (0.5 mg). Fingolimod levels measured after 6, 12, 24 months of treatment were comparable (\(p>0.05\)): median with [Q1-Q3] of the plasma levels were, respectively, 0.547 ng/mL [0.316-0.722], 0.400 ng/mL [0.306-0.511] and 0.720 ng/mL [0.391-1.031]. Similarly, comparable levels (\(p>0.05\)) of Fingolimod-P were found, showing a median with [Q1-Q3] of 1.230 ng/mL [1.040-1.460], 0.960 ng/mL [0.730-1.350] and 1.390 ng/mL [0.780-1.718] after 6, 12 and 24 months, respectively. Fingolimod-P-to-Fingolimod ratio ranged from 1.281 to 4.244 (mean \(\pm\) SD 2.2 \(\pm\) 0.7, \(n=31\)) with no differences between the three time-points (\(p>0.05\)).
## 5 Results and Discussion  
### Results  
This work proposes a validated two-step parametrization method, and it demonstrates the method by parametrizing the presented model using the measurement data obtained from the FC test bench. The proceeding is almost identical to the one described in Section 3.4. The differences are that the measured outputs \(\mathbf{y}^{*}\) now serve as reference data, the optimizer's population size is 1000, and the model gets parametrized only once. Therefore all conclusions stated there still hold. Confidentiality agreements do not allow the publication of the numerical values of the optimized parameters. Figure 4a-d show the parametrization results. Simulating the model with the optimized parameter vector \(\theta_{\text{opt}}\) and using the measured inputs \(\mathbf{u}\) described in Section 4 yields the simulated model outputs \(\mathbf{y}\). The figures additionally depict the measured outputs and four model inputs as reference: the mass flow of air \(\mu_{\text{Ca,in}}\), the FC temperature \(T\), the anode supply manifold pressure \(p_{\text{an,sm}}\), and the current \(I\). The parameterized model achieves an \(R^{2}\) value of 93% on average. For validation data, the model reaches an \(R^{2}\) value of 91% on average (see Figure 4e-h), providing the required accuracy for control-oriented applications.
## 3 Methodology
### [PERSON]'s Approach to Yield Estimation
#### 3.1.2 Crop Coefficient  
The Kappa coefficient for crops (Kc) is a specific coefficient for a particular plant crop. Typically, the Kc is calculated experimentally on fields. This value represents multiple changes in the effects of the soil, crop, climate conditions, and crop-management techniques. Every crop field has a set of specific crop coefficients based on the climate and the chlorophyll rates. This method is based on research on wheat fields in Egypt [23]. In that exact research, it had been determined that there is a correlation between the NDVI index and Kc.  
\[\text{Kc}=\frac{\text{K}_{\text{cmax}}(\text{NDVI}-\text{K}_{\text{cmin}})}{ \text{NDVI}_{\text{max}}-\text{NDVI}_{\text{min}}} \tag{4}\]  
According to the experimental data, Kcmax usually is equal to 1.2, while Kcmin is 0.1 [23].  
The final approach that we are taking into consideration is the Kc calculation method based on Leaf Area Index (LAI). The research was applied to two vineyards located in Israel, using optical and SAR RS imagery [24]. As a result, the quadratic correlation between Kc and the LAI in that research was confirmed.  
\[\text{Kc}=-0.0283*\text{LAI}^{2}+0.3547*\text{LAI}+0.0775 \tag{5}\]  
LAI--as one of the most significant biophysical variables used to track plant development and production, is an index that is defined as leaf area per unit of ground area. For large agricultural areas, determining LAI by field surveys is time-consuming and challenging. Since they can offer reliable, up-to-date, and consistent spatial and temporal coverage and resolution, as well as usability and ease of use, indirect approaches, such as the utilization of geographic data gathered by RS, have grown popular. Furthermore, LAIcannot be measured over a vast area. Because of this, it is thought that using satellite data to calculate LAI values quickly and precisely is the most appropriate approach [25].  
\[\text{LAI}=\frac{\text{Leaf area}}{\text{Ground cover}} \tag{6}\]
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 6 AI-Based Malware Detection  
### Signature-Based Malware Detection  
It involves the generation of unique signatures for each known malware to be compiled into malware behaviour libraries. Experts can manually identify these signatures, which may also be produced using automatic approaches and may contain a range of data, such as filenames, string values, or bytes.  
The signature of unfamiliar software can be subjected to a comparison with the library of malware behaviour to ascertain whether any corresponding signatures can be identified. This detection method is the most commonplace and is utilized with a swift detection rate and a low rate of false alarms. Signature-based malware detection is rendered ineffective for newly emerging malware, just as misuse intrusion detection is. The malware library must be kept up-to-date and regularly managed. The starting point of the detection or prevention of malicious activity necessitates that a victim first reports the incident. When the initial victim is of great significance, the outcomes may be intolerable. The 2015 U.S. Office of Personnel Management vulnerability in critical infrastructure could potentially lead to several repercussions that could extend for decades.  
Different machine learning techniques have been utilized for malware detection. These ML techniques opt to decompose the malware to extract pertinent information from the software, thus facilitating the models to detect malicious software. The selection of suitable input data is critical to the successful deployment of Artificial Intelligence (AI)-based malware detection.  
We conduct an analysis of various exemplar ML malware detection schemes and a comparison of many alternative software information options chosen by researchers.
## 6 Conclusions  
In this paper, a new model-based battery monitoring method was proposed. Firstly, the battery internal characteristic was fully used for the theoretical observability analysis. Namely, the linear segment characteristics of the battery OCV-SOC led to a model decomposition based on the superposition principle. Thus, the original battery ECM was decomposed into two sub-models. Each sub-model contained different battery states and parameters; hence, each of them was extended in order to be analyzed. Therefore, after the nonlinear observability analysis, the necessary observability conditions were clearly listed for the battery ECM parameters and battery capacity.  
Answers to the four questions raised in the Introduction could be given as follows. Firstly, the battery capacity estimation based on the extended ECM depended on \(\left(\frac{\mathrm{d}V_{\mathrm{CC}}}{\mathrm{d}osc}\right)^{2}\), which induced the inherent weak observability. Secondly, the smaller initial value of the battery capacity would guarantee a better local observability condition, which would consequently lead to a rapid convergence speed. Thirdly, the battery state and parameter estimation based on the extended ECM depended on the battery current, namely both the current amplitude and its time varying behavior would affect the observability. Last, but not least, according to the theoretical analysis, a new cascaded framework that separated the battery ECM parameters and battery capacity estimation was proposed. The effectiveness of the analysis results and the proposed framework was validated through numerical simulations.  
[PERSON] has proposed the original idea, carried out the simulations and written the original draft paper. [PERSON] and [PERSON] have supervised the research, analyzed the results and reviewed and edited the paper. [PERSON] has reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NO. 61673260).  
The authors declare no conflict of interest.
# Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies
## 6 Simulations
### Experimental Setup  
We consider a set of \(K=5\) arms, each modeled as a three-state Markov chain. The transition probabilities for each arm's Markov chain, as well as the rewards associated with each state, are randomly generated at the start of every simulation run. This randomized setup ensures that the results represent average-case performance over a wide variety of synthetic conditions rather than tuning to any particular fixed scenario.  
Specifically, for each arm \(i\in\{1,\ldots,5\}\), we construct its state transition probability matrix \(P^{(i)}\) and reward vector \(\
u^{(i)}\) as follows:  
1. State Transition Probabilities: We draw each nonzero transition probability \(p^{(i)}_{uv}\) from a Beta distribution (to ensure values between 0 and 1) and then normalize each row so that they form a valid probability distribution. For example, for each row \(u\), we sample three preliminary values from \(\text{Beta}(\alpha,\beta)\) with parameters \((\alpha,\beta)\) fixed with \((\alpha,\beta)=(2,2)\) for a moderate spread, and then normalize the row so that \(\sum_{v}p^{(i)}_{uv}=1\). Each run of the simulation independently re-samples these probabilities. This ensures diverse state transition dynamics for each arm across runs.  
2. Reward Distributions: Each state of each arm is assigned a reward distribution centered around a mean value drawn uniformly from \([0,1]\). Specifically, for arm \(i\) and state \(u\), we let  
\[\mu^{(i)}_{u}\sim\text{Uniform}(0,1).\]  
We then model the reward at each round from that state as  
\[r^{(i)}_{t,\mu}\sim\bar{\mathcal{N}}(\mu^{(i)}_{u},\sigma^{2}),\]  
where the value of \(\sigma\) is the standard deviation for all states and arms and \(\bar{\mathcal{N}}(\mu^{(i)}_{u},\sigma^{2})\) is the truncated normal distribution. Truncation ensures that rewards remain within \([0,1]\). By re-sampling these mean rewards and their underlying realizations in every run, we capture a broad spectrum of synthetic arm behaviors.  
3. Multiple Simulation Runs: To assess performance stability, we run each experiment for \(N_{\text{runs}}=10^{4}\) independent runs (which goes beyond any reasonable confidence level value). Each run involves simulating \(T=10^{4}\) time steps, allowing sufficient duration for the algorithms to settle into steady behaviors. Due to this extensive repetition, we approximate the long-run expected cumulative rewards and regret for each algorithm, mitigating the variance from any particular random draw.  
This highly synthetic and randomized environment aims to stress-test the MC-UCB policy under different Markovian conditions to demonstrate how our theory-based approach scales to a few arms and stochastic transitions.
## 3 Results and Discussion
### XPS and AFM Characterization  
The chemical structure of the coating was identified by FTIR and XPS, as shown in Figure 3. The peak position at 780 cm\({}^{-1}\) was caused by the stretching vibration of Al-O-Al, while the peak of Si-O-Si was located at 1086 cm\({}^{-1}\) (Figure 3a). Figure 3b shows the XPS result of the uncoated and coated fiber. According to different photoelectron binding energy corresponding peak positions, Al 2p, Si 2p, Al 2s, Si 2s, C 1s, N 1s, O 1s were detected to be present for the coated quartz fiber (Figure 3b). The characteristic binding energy of 73.7 eV corresponded to -Al-O- bond (Figure 3b), consistent with the FTIR result in Figure 3a. Both FTIR and XPS results evidenced the characteristic peak of -Al-O-, corresponding to Al\({}_{2}\)O\({}_{3}\), consistent with the EDS analysis in Figure 2c,d.  
The fine microstructure of the Al\({}_{2}\)O\({}_{3}\) coating was further characterized by AFM, as shown in Figure 4. A relatively smooth surface was observed on the bare quartz fiber (Figure 4a,b), while the deposition of the Al\({}_{2}\)O\({}_{3}\) coating increased the surface roughness, evidenced by the formation of the nano-sized Al\({}_{2}\)O\({}_{3}\) nodules as shown in Figure 4c,d. After the Al\({}_{2}\)O\({}_{3}\) coating was deposited onto the  
Figure 2: SEM morphologies (**a**,**b**), energy disperse spectroscopic spectra (**c**,**d**) and mapping results (**e**) of the bare (**a**,**c**) and coated quartz fibers (**b**,**d**,**e**).  
surface of the quartz fiber, the root-meansquared roughness of the coated carbon fiber increased from 68 to 136 nm. Therefore, the coating is beneficial to improve the surface roughness of the quartz fiber, and enhance the mechanical interlocking and interface adhesion between the fiber and the matrix in the composite material.
## 3 Results and Discussion
### Nutritional Requirements
#### 3.3.2 What Are the Protein Requirements of the COPD Patient?  
**Recommendation 13**.: _GARIN suggests an intake of 1 g protein/kg body weight/day in stable patients and 1.2-1.5 g/kg for malonurished patients in advanced stages and during exacerbations._  
Grade of recommendation B, (96%) Strong consensus.  
Comment:  
There is very limited evidence on specific protein requirements in COPD patients. The study by [PERSON] [85] described, using the isotope tracer technique, an increase in protein metabolism that correlated with REE in COPD patients. Protein catabolism was not significantly different between COPD subjects and controls. On the other hand, [PERSON] et al. [90] found that threshold and anabolic capacity are preserved in clinically and weight-stable COPD patients and, therefore, suggest that there is no disease-related anabolic resistance and/or increased protein requirements [90]. Results obtained from studies on protein metabolism in vivo are inconclusive, and other studies report an increase in both synthesis and catabolism, suggesting an overall increase in protein metabolism compared with healthy subjects, in which the lower leucine concentrations were associated with low FFM in the COPD group [91].  
On the other hand, there is some evidence in the reviewed literature on the benefits achieved in terms of increased muscle mass and strength in those patients who consume more protein [14; 92; 93; 94].  
Although the current study has not found any specific recommendations for COPD patients, the members of GARIN, based on the literature reviewed, consider it important to advocate a higher protein intake in the diet of these patients.
## 3 Design of Data Collaboration Mode Based on Improved Framework
### Data Collaboration Method Based on ITS
#### 3.1.4 Collaboration between GPS Information and UAV  
Considering the high resolution and real-time characteristics of UAV, this paper adopted an image processing algorithm to conduct traffic flow statistics based on real-time data collected by UAV, and comprehensively used GPS macro data and position micro-data collected by UAV to detect flow rate and extract features. Finally, the road index is measured and the road network is constructed based on the data of the traffic simulator.  
The UAV video data can also be used to detect weather and air quality at the naked eye level for collaborative analysis.