esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### EMS Mutagenesis and Mutagenesis Efficiency Assessment  
For the introduction of random mutations, cells from an overnight (O/N) full (LB) medium culture of the transcriptional reporter _M. luteus_ trpE16 _\(\Delta\)comEA/EC:lacZ-kan_ (Figure S1) [26] were used to inoculate LB with and without supplementation of 5 mM ethyl-methanesulfonate (EMS) and the corresponding antibiotic. Starting at an OD\({}_{600\text{nm}}\) of 0.2, the cultures were incubated for 24 h with shaking at 180 rpm and 30 \({}^{\circ}\)C. The treated cells were washed twice with phosphate buffered saline (PBS) by centrifugation. PBS consisted of 137 mM NaCl, 2.7 mM KCl, 10 mM Na\({}_{2}\)HPO\({}_{4}\), 1.8 mM KH\({}_{2}\)PO\({}_{4}\), adjusted at pH = 7.4. For cell recovery and to allow segregation of mutations (single nucleotide polymorphisms, SNPs), samples of 0.05 mL were inoculated in 5 mL of fresh LB with kanamycin (LB\({}_{\text{Kan}}\)) and incubated with aeration O/N at 30 \({}^{\circ}\)C. To assess the efficiency of mutagenesis, mutagen-induced rifampicin resistance was monitored [32]. Samples of EMS-treated cells were plated on full media supplemented with 1 ug/mL of rifampicin, and after growth, the frequency of resistant colonies was determined and compared with the frequency of spontaneous rifampicin-resistant colonies arising from cells not treated with the mutagen. Mutagenized cells were mixed with 25% glycerol (_v/v_; final concentration) and stored at \(-80\)\({}^{\circ}\)C until use.  
Screenings for two phenotypes were conducted with the mutagen-treated cells: (i) _comEA/EC_ promoter-repressing conditions, using LB\({}_{\text{Kan}}\) plates supplemented with 45 \(\upmu\)g/mL of 5-bromo-4-chloro-3-indolyl-\(\upbeta\)-D-galactopyranoside (X-gal), and (ii) _comEA/EC_ promoter-and competence-inducing conditions, using GMM containing antibiotic and 80 \(\upmu\)g/mL of X-gal (Figure 1A). In this way, two groups of mutants were obtained, the M-mutants showing enhanced _comEA/EC_ promoter activity on LB (blue colonies on LB/X-gal plates), a competence-repressing medium [15], and the R-mutants showing no _comEA/EC_ promoter activity when grown on GMM minimal medium (white colonies on GMM/X-gal plates) (Figure 1B). Each mutant with dysregulated _comEA/EC_ promoter activity was labeled accordingly with either M or R followed by the isolate number.
# Improved Road Segment-Based Geographical Routing Protocol for Vehicular Ad-hoc Networks
###### Abstract  
The real-time traffic information dissemination among on-road vehicles has been envisioned via realizing vehicular ad hoc networks (VANETs) as smart service-oriented roadside wireless sensor networks. The network enables various types of real-time traffic applications related to safety and infotainment for drivers and passengers. The information dissemination-centric routing protocols for vehicular networks have to dynamically adopt under the constrained network environment while considering the higher mobility of vehicular nodes and unpredictable physical topologies in the network. The issue needs to be addressed through smart and network-aware routing protocols. Geographical routing protocols have witnessed significant attention for information dissemination under these types of dynamic vehicular network environment. To this end, this paper presents an improved road segment-based geographical routing (ISR) protocol focusing on better head node selection for information dissemination. It divides the forwarding area into a number of road segments and selects a head node on each segment by focusing on traffic-aware information including the location, direction, and link quality-centric score for every vehicle on each road segment. Algorithms were developed for the complete process of head node selection and information dissemination among vehicles on the road segments. The simulation results attested the performance benefits of the proposed routing framework as compared to the state-of-the-art protocols considering dynamic vehicular traffic environment-related metrics.  
Keywords:road; segment; geographical; routing; communication; protocols; VANET +
Footnote †: journal:
## Abbreviations  
* [1] AWGN additive white Gaussian noise BER bit error rate CFRE cyclic frequency relative error CP cyclic prefix CSD cyclic spectrum density CSIN cyclostationary impulsive noise DBPSK differential binary phase shift keying ESR error-to-signal power ratio FEC forward error correction FEQ frequency-domain equalization FFT fast Fourier transform FIR finite impulse response FRESH frequency-shift FS Fourier series GINR Gaussian-to-impulsive-noise ratio IFFT inverse fast Fourier transform LPTV linear periodically time-variant LTI linear time-invariant MSE mean square error MUX multiplexer NB-PLC narrowband powerline communication OFDM orthogonal frequency division multiplexing PSCGM piecewise spectral cyclostationary Gaussian model RLS recursive least-squares SINR signal-to-interference-plus-noise ratio SNR signal-to-noise ratio TA-LMS time-averaged least mean squared
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers
## 9 Conclusions  
This paper discusses the rest-to-rest and non-rest-to-rest control of an undamped, planar, two-body spring-mass model using the four-impulse optimized ZVDD input shaping method. The proposed method is useful to guarantee the final rest condition. For the rest-to-rest control problem, the traditional ZVDD input shaper can bring the final state to rest, but it does not mitigate vibrations during the motion. The proposed optimized  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters at Final Time** & **Bang-Bang (Figure 6)** & **ZVDD (Figure 7)** & **Proposed Method (Figure 12)** \\ \hline \(\theta_{1}\) (deg) & \(-9.6504\) & \(-0.7629\) & \(3\times 10^{-03}\) \\ \(\theta_{1}\) (deg/s) & \(-2.0158\) & \(3.8336\) & \(-1\times 10^{-03}\) \\ \(\theta_{2}\) (deg) & \(4.1951\) & \(-8.0039\) & \(-9\times 10^{-03}\) \\ \(\theta_{2}\) (deg/s) & \(4.0799\) & \(-7.7594\) & \(3\times 10^{-04}\) \\ \hline Final Time & \(33.5823\) & \(40.8789\) & \(47.4278\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of various test results obtained using different methods for non-rest-to-rest condition.  
Figure 12: Non-rest-to-rest case with optimized convoluted torque. The plot units are degree (\(\theta_{1}(t)\) and \(\theta_{2}(t)\)), degree/second (\(\dot{\theta}_{1}(t)\) and \(\dot{\theta}_{2}(t)\)) and N.m (\(u(t)\)).  
input shaper not only brings the final state to rest but also reduces the vibrations for the rest-to-rest problem.  
The non-rest-to-rest problem is an extremely complicated problem to solve, as the minimum number of switches depends on a number of different factors, including the initial state values. This article shows that the traditional ZVDD shaper results in significant non-zero values for the final state. However, the proposed shaper is able to solve the non-rest-to-rest control problem regardless of the initial non-rest values, so the proposed method is useful regardless of the minimum number of switches.  
Conceptualization, S.B. and J.H.; methodology, S.B.; software, S.B.; validation, S.B., J.J.K. and J.H.; formal analysis, S.B.; investigation, S.B. and J.J.K.; resources, S.B.; data curation, S.B.; writing--original draft preparation, S.B.; writing--review and editing, S.B., J.J.K. and J.H.; visualization, S.B.; supervision, J.H. and J.J.K.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
The project or effort undertaken was or is sponsored by the Department of the Navy, Office of Naval Research, Consortium for Robotics Unmanned Systems Education and Research at the Naval Postgraduate School.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 3 Experiment
### Linewidths Measurement Results  
Multiple measurements using Focus & TSOM-CNN are made for the gold lines target with the linewidths range of 247-1010 nm. The measuring performance of Focus & TSOM-CNN and other two deep-learning TSOM methods (DenseNet121 model and ResNet50 model in regression) on the test set is shown in Figure 11, where the abscissa of the scatterplot is for measuring linewidth, the vertical represents the absolute value of the error of the prediction results (\(|y_{i}-\hat{y}_{i}|\), the absolute value of the difference between the measured value and true value), and each point in the figure represents a measurement result. The DenseNet121 model and ResNet50 model are trained with the same hyperparameters reported in the Reference [19].  
As can be perceived from Figure 11, the measuring error of Focus & TSOM-CNN is generally lower than that of the other two regression models, and most of the test errors are less than 15 nm with only a few exceptions, which is potentially related to the image noise from data collection.  
Figure 12 shows a couple of examples of the experiment in order to evaluate the results more intuitively: the multiple measurements of gold lines with 247 nm, 357 nm, 625 nm and 768 nm linewidths. It could be concluded from these figures that the measurement output values are clustered to the true values. The experiment shows that the two-input deep-learning TSOM method proposed in this paper is accurate in nanoscale measurement and has good repeatability.  
Figure 11: The measuring performance of three different TSOM methods on the test set. (**a**) Focus & TSOM-CNN; (**b**) ResNet50; (**c**) DenseNet121.  
Table 1 presents the MSE and MAE of the measuring results of 247-1010 nm gold lines of the three regression models on the test set. According to the data in Table 1, the MSE of our two-input deep-learning TSOM method is 5.18 nm\({}^{2}\) and the MAE is 1.67 nm, which are both far less than other two regressive deep-learning TSOM models. This suggests that the Focus & TSOM-CNN shows the highest accuracy in nanoscale measurement.  
The two-input deep-learning TSOM method is not limited by the feature extraction algorithm and the classification categories for extracting the features automatically and using the regression model. Moreover, this method is not needed to carry out complex simulation modeling process such as the library-matching TSOM method. Therefore, the method has good performance in precision and convenience of measurement.
## 2 Related Work
### Summarization  
Summarization is an NLP task whose aim is to generate a descriptive summary either for a single text or for a corpus. Advancements in ML and Deep Learning have led to three categories of approaches, namely extractive, abstractive, and hybrid ones. Extractive summarization techniques employ ranking algorithms that divide the documents at a predefined level (i.e., usually in sentences) and measure the importance and relevance of the tokens to the overall document. The top-k tokens are then combined to produce a concise summary. Based on the ranking algorithm utilized, we can further divide extractive summarization techniques into three distinctive categories: (i) statistic-based approaches [15; 16] that rank the tokens by word or sentence frequency; (ii) semantic-based approaches [17] that use similarity metrics to create a co-occurrence matrix for the tokens; additionally, they employ dimensionality reduction techniques (such as _Latent Semantic Analysis_, _Principal Component Analysis_, etc.) to extract the most significant tokens; (iii) graph-based approaches that create a graph of sentences (tokens); the produced graph is employed for extracting and ranking the most significant measures via a series of graph-based metrics. Some of the most prominent approaches falling into this category are _TextRank_[18] and _LexRank_[19].  
On the other hand, abstractive summarization techniques operate by utilizing Deep Learning technologies to obtain vector representations (i.e., embeddings) of the sentences. The embeddings are utilized by a language model to generate an abstractive summary (i.e., summaries that may include different text compared to the original document). The recent appeal of Large Language Models, due to the impressive results they have demonstrated as far as the task of text generation is concerned, has led to the development of Transformer-based abstractive approaches (such as _BERT_[20], _BART_[21], _T5_[22], etc.). Hybrid approaches employ a combination of extractive and abstractive summarization techniques for generating a representative summary.
## 3 Critical Technical Approach
### Stray Light Suppression Analysis and Structural Design Based on BRDF Modeling  
In this system, the primary source of stray light is not external environmental radiation but rather internal scattering processes induced by the solar simulation module. Since the solar background light and the signal beam are combined into a shared optical path, spatially merged via a beamsplitter and transmitted through a common optical aperture, this co-path configuration results in strong spectral and spatial overlap between internal stray radiation and the communication signal. Consequently, conventional stray light test techniques such as _VGI_ and _PST_ methods fail to effectively separate the two [29]. Therefore, high-precision theoretical modeling and ray-tracing simulation are required to analyze the dominant stray light paths, surface scattering mechanisms, and suppression structure performance [30]. These models form the theoretical foundation for subsequent stray energy ratio assessments and (_SNR_) optimization.
## 5 Discussion
### Machine Learning  
The results obtained from question RQ-ML1 establish that supervised ML algorithms are hegemonic. A considerable number of articles incorporated multilayer neural networks and SVM algorithms. This was mainly substantiated by the upsurge in libraries supported by large software companies, such as Tensorflow, Keras, and others, in addition to their start-up in a wide-reaching and versatile language, such as Python, which facilitates the mining of code repositories.  
The lack of studies that reported experiences with algorithms that used reinforcement learning underscored the lack of environments and libraries that directly support such algorithms. In many cases, the existing algorithms do not work in specific situations, and their adjustment can be complex or even require ad hoc development.  
With respect to the use of previously trained models (RQ-ML2), the poor availability of the models used is surprising. It could be argued that the reasons for this finding are supported by intellectual property or the licensing of certain knowledge. In the same way, the main dataset used was PROMISE. This dataset has characteristics that make it suitable for use because, for example, the structure, documentation, and nature of the data that come from industrial projects make this dataset suitable for laboratory situations.  
In relation to the characteristics of the hardware and software (RQ-ML4, RQ-ML5), the few details of the configurations of both the hardware and software stand out. A total of 93% of the articles did not provide the details of their hardware configurations, whereas 60% did not do so with respect to the software. There was also no repository or anything similar provided so that the studies could be replicated or the configurations could be investigated. These results are supported by the criticisms of reproducibility and replicability in deep learning (DL) and ML proposed in [61], which described innumerable articles that did not make the evidence that could ensure these characteristics clear. The replicability of studies that obtain results from various sources is fundamental in software engineering. Accordingly, including details related to the hardware and software characteristics used to reach results in articles facilitates the replicability of thereof.
## 2 Mathematical Formulations
### Multi-Objective Function Formulation
#### 2.1.2 Constraints  
The multi-objective function formulated in (3) is subject to the following set of constraints:  
With DG integration, there is the possibility of active/reactive mismatch, which can result in a voltage stability problem at the distribution level. Therefore,  
\[\left|P_{ij}^{Line}\right|\ \leq P_{ijmax}^{line}\leq P_{DG,max} \tag{7}\]  
Apart from active and reactive power mismatches, the quantity of DG in the network and the place of location are factors that can cause over or low voltage profiles in the network system. For these reasons, the voltage magnitude is constrained as  
\[V_{min}\ \leq V_{i}\leq V_{max} \tag{8}\]  
For the sake of this study, a 5% variable voltage was used as the allowance for voltage variation against the optimal voltage of 1.00 pu, and 0.95 pu and 1.05 pu were the minimum and maximum voltage limits, respectively.  
The constraint associated with the line power loss is given as  
\[\sum_{i=1}^{ND}P_{DGi}+P_{Grid}=\sum_{i=1}^{ND}P_{Di}+P_{L} \tag{9}\]  
where \(ND\) is the total number of \(DG\)s installed in the network during each test case, \(P_{L}\) is the real power loss that occurs in the system, \(P_{Di}\) is the power demand at the buses at the time of the test, \(P_{Grid}\) is the power from the source and \(P_{DGi}\) is the total DG at the time of the test.
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 4 Results and Discussion  
### Experimental Environment  
To verify the performance of the proposed algorithm, we conduct a number of experiments in our personal computer. The presented approach is implemented in Matlab2012b. We use MIT-BIH arrhythmia database [24] as a reference for real signals. Each sample length is 650,000. The sampling rate is 360 Hz and the resolution is 11 bps. For simulated electrocardiograms, we used the introduced dynamic model to generate synthetic ECG signals [25; 26].  
In the qualitative evaluation, we select the signal numbered 114 for direct de-noising observation because the record contains more noise. In addition, since there is an ectopic complexes in 119 record, we add noise to record 119 and compared the effect of de-noising with several traditional methods. Finally, In order to observe the de-noising effect of this method under different types of noise, we add power line interference, Gaussian noise and EMG noise to records 106, 113 and 115, respectively.  
In the quantitative evaluation, we consider ECG signals such as 100, 103, 105, 113, 115, 117, 119, 122, 200, 215, 230, which contain time-varying QRS morphology,normal and abnormal ECG beats. In each set of experiments, these signals are tested 100 times and averaged. In addition, the record 115 is quantitatively evaluated by adding different types of noise.  
We simulated power line interference, EMG noise and synthetic interference. In order to evaluate the performance of the proposed method for power line interference denoising, the additional functions of these interference are as follows:  
\[N\left(t\right)=A\times\sin\left(2\pi ft\right) \tag{20}\]  
where \(A=0.15\ mv\) and \(f=50\) HZ. The signal was added to the original electrocardiogram signal to simulate power line interference. Random noise is added to ecg signals [27] to simulate EMG interference (\(\mu=0,\sigma=0.15\)). In the case of synthetic interference, white gaussian noise (WGN) is used to simulate these noises.
## 6 Geometries Used in Additively Manufactured Crash Boxes
### Origami-Inspired Structures  
Origami-inspired structures, also known as pre-folded structures, are innovative designs that incorporate intricate folding patterns to enhance the energy absorption capacity of crash boxes, drawing inspiration from the ancient Japanese art of paper folding. These structures offer several advantages over traditional designs, including reduced _PCF_ values, high energy absorption efficiencies, and stable deformation behaviors [21]. Origami-inspired structures, especially multi-cell origami designs, are challenging to produce due to their intricate geometries and complex folding patterns, which are difficult to replicate using traditional manufacturing methods. AM provides a viable solution that accommodates the complex shapes and fine details required in origami-inspired designs, enabling the precise fabrication of such detailed structures. Several investigations have focused on origami-inspired structures developed through AM [51; 52; 53].  
[PERSON] et al. [54] proposed an origami crash box (OCB), shown in Figure 9a, fabricated using the FDM technique with carbon-fiber-reinforced nylon (PATH-CF15) material. Various geometrical configurations are generated using the Latin Hypercube Sampling (LHS) method to train an Artificial Neural Network (ANN) model designed to forecast the performance of the OCB. The results revealed that the highest difference in \(PCF\) between ANN predictions and experimental findings is 19.9%, while the highest difference in \(E_{\text{T}}\) is 27.5%.  
Because of their high energy absorption capacity and lightweight nature, multi-cell structures are frequently utilized in energy-absorbing applications. However, these structures can generate high \(PCF\), posing risks to passenger safety during crashes. To address the issue, [PERSON] et al. [55] proposed origami-inspired multi-cell tubes fabricated from 316L stainless steel using SLM, with the printed specimens shown in Figure 9b. Their study demonstrates that web-to-web (W2W) designs achieved the highest \(SEA\), while web-to-corner (W2C) designs offered superior deformation modes and lower \(PCF\). Building on the multi-cell concepts, [PERSON] et al. [56] introduced double-layer biomimetic multi-cell tubes with pre-folded external walls, shown in Figure 9c, fabricated using SLM with stainless steel 316L. By varying parameters such as the number of cell elements, wall thickness, folded segments, folded edges, and cell patterns, they found that structures with eight edges and four-fold segments significantly enhanced both \(SEA\) and \(PCF\) performance.