esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 4 Experiment and Result Analysis
### Processor Real-Time Testing and Performance Analysis  
This section primarily focuses on the implementation of two RISC-V processors, Rev-riscv and Rev-riscv-II, on an FPGA for subsequent testing.  
To make the verification process more practically relevant, this paper assumes a research background in automotive autonomous driving systems (real-time systems). The relationship between the real-time system and the processors is illustrated in Figure 14.  
The real-time system plans to use three processor models for the autonomous driving system: Rev-riscv, Rev-riscv-II, and Rev-riscv-III (the latter has not been implemented and is included only as a negative reference).  
In this real-time system, the processors primarily execute the following two types of computational tasks:  
1. Conventional performance computations (primarily based on test contents from CoreMark such as matrix operations and list processing) used for routine information processing.
2. High-density multiplication operations used to calculate information such as the current vehicle speed.  
Failure to provide results for these two types of computations within the specified time limits of the real-time system can lead to severe consequences, including car accidents.  
After data analysis by the real-time system software, the following requirements are specified:  
1. The processor must complete Task A (CoreMark tests) within 100 s (denoted as T\({}_{\text{d}}\) in the formula) to meet system requirements and avoid hazardous outcomes.
2. The processor must complete Task B (performing 200 division operations) within 100 microseconds (also denoted as T\({}_{\text{d}}\) in the formula) to meet system requirements and avoid hazardous outcomes.  
Figure 13: Optimized divider module function simulation diagram.  
The workflow relationship between tasks is shown in Figure 14. The metric information for Task A and Task B is provided in Table 9.
## 7 Conclusions  
This paper presents the transmission characteristics of on-chip THz antennas for intra- and inter-chip communication. We have investigated two simple antennas, namely, dipole and zigzag antennas, both resonating in the THz band, and studied them in the presence of densely packed multi-layer flip-chip package environments. Through simulation in Ansys HFSS, we have characterized the transmission, studied the electric field distribution inside a chip and path loss, and provided some basic link budget examples using simulated values. The simulated path loss highlights the improvements of using a polyimide substrate compared with silicon dioxide, and the dipole antenna exhibits more path loss compared with the zigzag antenna. However, according to the bandwidth threshold defined in our paper, a dipole antenna has more bandwidth than a zigzag antenna for intra-chip communications. Similarly, based on the simulations for intra-chip communication, the zigzag antenna demonstrates a more distributed form of electric field radiation reaching up to the receiving antenna compared with the dipole antenna. Overall, the choice of an antenna depends upon the materials used for the substrate, the architecture of the chip, and its placement.  
Furthermore, it is necessary to investigate channel estimation, modulation, demodulation, and bit error rate calculation for full-wave communication within a flip-chip package. This emphasizes the application of compact on-chip THz transceivers integrated into chip packages, making it a viable technology for wireless interconnections for multi-core CPUs in HPC applications.  
Conceptualization, B.P. and X.J.L.; methodology, B.P. and X.J.L.; validation, B.P. and X.J.L.; formal analysis, B.P. and X.J.L.; investigation, B.P. and X.J.L.; writing\(-\)original draft preparation, B.P. and X.J.L.; writing\(-\)review and editing, X.J.L. and B.-C.S.; visualization, B.P. and X.J.L.; supervision, X.J.L. and B.-C.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
Figure 22: Electric field strength (V/m) along the line that separates Antennas A and B (only Antenna A is switched on).
# Ultrasound B-Mode Imaging: Beamforming and Image Formation Techniques
## 2 Ultrasound B-Mode Imaging
### Novel Beamforming Techniques  
High frame-rate imaging techniques [4] have recently gained increased interest for their capability to detect fast dynamic events. However, the improvement of temporal resolution comes at the expense of image quality, thus pushing researchers to recover it by developing smart strategies. Four papers have been published in this Special Issue presenting advanced transmission sequences [7; 8] and beamforming schemes [9; 10] applied to either plane waves [7; 10] or multi-line transmission imaging [8; 9].  
[PERSON] and [PERSON] [7] analyzed the grating lobe artifacts due to the compounding of images obtained from the transmission of steered plane waves with a constant angle interval. Additionally, they showed that the use of non-uniform angle sets is a smart solution to keep the frame rate high, while limiting the level of image artifacts due to grating lobes. [PERSON] et al. [8] studied the effectiveness of orthogonal coded excitations in multi-line transmission imaging in suppressing crosstalk artifacts. They showed that Golay codes enable higher crosstalk rejection (and better contrast) compared to linear chirps.  
Two papers focus on the so-called coherence-based beamforming methods. Spatial coherence of ultrasound backscattered echoes is affected by contributions coming from off-axis regions, noise, and interferences. [PERSON] and [PERSON] (Guest Editors) presented a new formulation of the Filtered Delay Multiply and Sum (F-DMAS) beamforming, namely Short-Lag F-DMAS [9]. They provided new insights into the relation between the performance of the F-DMAS algorithm and the coherence of backscattered signals in multi-line transmission imaging. [PERSON] et al. presented a generalized and extended formulation of the F-DMAS beamformer, referred to as p-DAS [10]. They applied the proposed method to plane wave imaging and showed the achieved improvements in terms of lateral resolution and artifacts rejection.
## 3 Results and Discussion
### Antibacterial Activity  
The antibacterial activity of _L. phillipiana_ essential oil was evaluated using _S. aureus_ and _K. pneumoniae_ strains. Figure 1 shows the experimental results of the antibacterial evaluation of the essential oil compared to the mineral oil. The latter was used as a control for traditionally lipophilic extracts to obtain essential oils, which could influence the antibacterial activity of the extract. Figure 1A shows a decrease in _S. aureus_ viability over 90% and 15-70% of the essential oil and mineral oil, respectively. Data presented a statistically significant difference in all concentrations evaluated (0.1-0.5 Oil/medium ratio). On the other hand, _K. pneumoniae_ strain viability (Figure 1B) reported decreasing viability values between 80-90% for the essential oil and between 25-70% for the mineral oil. In this case, only statistically significant differences were obtained in the concentrations 0.1, 0.2, and 0.3 Oil/medium ratio. Antimicrobial testing demonstrated higher susceptibility in Gram-positive bacteria compared to Gram-negative bacteria. A similar behavior was previously reported for _L. philippiana_ essential oil against Gram-negative (_E. aerogenes_ and _E. coli_) and Gram-positive (_S. aureus_ and _S. epidermidis_) strains [10], which was associated with small hydrophilic compounds such as oxygenated monoterenes (linalool and eucalypt) presented in the _L. philippiana_ essential oil, which could be translated through the external membrane due to porin proteins which provide hydrophilic transmembrane channels [10]. Thus, the different susceptibility in Gram-positive and Gram-negative bacteria could be due to the complex outer rigid membrane of the latter, which is rich in lipopolysaccharide and limits the diffusion of the metabolites. In contrast, Gram-positive bacteria lack an extra complex membrane, increasing their susceptibility to small antimicrobial molecules. In turn, the infiltration of hydrophobic compounds is favored due to the lipophilic terminations present in the cell membrane, such as lipoteichoic acid [28]. Linalool may act as a protein-denaturing agent [28]. In addition, other compounds of _L. philippiana_ essential oil can also contribute to antibacterial activity, including isosafrole, a Safrole isomer that has been reported to act as a precursor to Spathulenol, and methyleugenol, which has demonstrated antifungal effects against the phytopathogens _Alternaria_ spp. and _Penicillium chrysogenum_[29; 30]. It is also worth noting that the antimicrobial effect of _L. philippiana_ essential oil remained unaffected while stored at 4 \({}^{\circ}\)C. The antimicrobial effect of the three-year-old extract stored at 4 \({}^{\circ}\)C is reported in the Supplementary Information (Figure S1). No significant differences were observed with respect to the antimicrobial effects of fresh oil.
## 1 Introduction
[43] examined the damage potential of MS-AS sequences by relating MS and AS intensities to the accumulative damage of reinforcement concrete (RC) frame structures under MS-AS sequences, respectively. Yet, only one RC frame structure was considered in both studies, leading to the limitation of the results to specific structural property. Moreover, only MS [42] or AS [43] IMs are considered to represent MS-AS sequences. This is unfavorable to reflect the real damage potential of a MS-AS sequence since the MS-AS-induced accumulative damage of structures is, to a large extent, dependent on the intensities of both MS and AS. Therefore, there is an urgent need to make a comprehensive investigation on the prediction of damage potential for MS-AS sequences. The main purpose of this study is to identify a set of optimal IMs of MS-AS sequences which exhibit a high correlation to structural damages. To cover a wide range of general structural properties, several representative SDOF systems with varying hysteretic behaviors are taken as the study cases. The considered SDOF systems are then subjected to a suite of 662 sequential earthquake inputs selected from 13 earthquake events. The structural accumulative damage caused by the MS-AS sequences is obtained using the damage index proposed by [PERSON] et al. [44] and modified by [PERSON] et al. [45]. Based on the obtained results, a comprehensive correlation analysis is conducted to search for the optimal IMs for predicting damage potential of MS-AS sequences.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Model Comparisons and Performances
Additionally, the Spearman correlation analysis ruled out any extreme multicollinearity among predictors, thereby affirming the model's stability and interpretability. Furthermore, to ensure that our models were not overly complex or specifically tailored to our dataset, we implemented strategies to act as pruning techniques for tree-based models, regularization parameters in linear models, and early stopping in gradient boosting. These measures help prevent overfitting while maintaining model accuracy and generalizability. The results confirm that the stacking regressor, chosen for its high accuracy, meets the essential assumptions for reliable regression analysis and is, thus, well-suited for further deployment in predictive tasks. In summary, our comprehensive evaluation across multiple train-test splits underscores the comparative robustness and superior performance of advanced predictive models over their linear counterparts. The linear models, while straightforward and inter \begin{table}
\begin{tabular}{c c c} \hline \hline
**Configuration** & **R-Squared** & **MSE** \\ \hline Base Model (3 Dense Layers) & 0.87 & 0.022 \\ Optimized Model (85–15 split) & 0.93 & 0.007 \\ CNN Architecture & 0.74 & 0.031 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Neural network performance. Figure 9: Variations in root mean square error (RMSE) across the different train–test split ratios. pretable, consistently demonstrated lower predictive capabilities, affirming the presence of significant nonlinear complexities within our dataset that these models fail to address. Conversely, tree-based ensembles and gradient boosting methods have not only achieved marked improvements in accuracy--surpassing linear models by a margin of 0.25 to 0.40 in R\({}^{2}\) scores--but have also maintained low variance in these metrics across various splits, highlighting their strong generalizability. In particular, the stacking ensemble has distinguished itself as the most effective model in terms of raw accuracy. This model's success is attributed not only to its high performance but also to its consistent results across different dataset partitions, which speaks to its robustness against overfitting--a potential concern for complex models. This robustness was ensured through rigorous cross-validation techniques that effectively identified and corrected those models that were overly fitted to specific segments of data. The application of these models to contemporary crime datasets has proven highly promising, setting a solid foundation for future predictive tasks. This aligns with our study's goals of utilizing advanced analytical techniques to enhance crime forecasting and prevention strategies. As we move forward, the stacking model, in particular, will serve as a cornerstone for ongoing research and application in this field, promising not only theoretical insights but also practical benefits in terms of public safety and policy formulation.
## 3 Results
### Evaluation of Volatile Compounds by HS-SPME-GC/MS Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample/Elements** & **Raw** & **Raw** & **GTW** & **GSW** \\
**GTW** & **GSW** & **Cooked in Unsalted** & **Cooked in Unsalted** \\  & & & **Water** & **Water** \\ \hline Aldehydes & & & & \\
2-methylbutanal & 0.54 \(\pm\) 0.02 & 1.09 \(\pm\) 0.02 & 1.17 \(\pm\) 0.02 & 0.21 \(\pm\) 0.01 \\
3-methylbutanal & 1.10 \(\pm\) 0.07 & 2.11 \(\pm\) 0.28 & 1.71 \(\pm\) 0.02 & 1.96 \(\pm\) 0.08 \\ Penanal & 2.69 \(\pm\) 0.14 & Nd & 2.93 \(\pm\) 0.38 & 1.55 \(\pm\) 0.06 \\ Hexanal & 56.31 \(\pm\) 0.31 & 14.28 \(\pm\) 0.03 & 66.36 \(\pm\) 0.50 & 39.88 \(\pm\) 1.53 \\
2-heptenal & 0.31 \(\pm\) 0.01 & 1.12 \(\pm\) 0.00 & Nd & 3.64 \(\pm\) 0.16 \\ Nonanal & Nd & Nd & Nd & 3.34 \(\pm\) 0.08 \\
2-octanal & 2.33 \(\pm\) 0.04 & 5.11 \(\pm\) 0.15 & 3.51 \(\pm\) 0.09 & 6.51 \(\pm\) 0.18 \\ decanal & Nd & Nd & Nd & 0.94 \(\pm\) 0.0 \\ Benzaldehyde & Nd & Nd & Nd & 2.13 \(\pm\) 0.01 \\ Tot & 1.98 \(\pm\) 0.07 & 5.07 \(\pm\) 0.09 & 3.51 \(\pm\) 0.02 & 5.87 \(\pm\) 0.28 \\ \hline Ketones & & & & \\
2-Propanone & 3.52 \(\pm\) 0.07 & 2.63 \(\pm\) 0.22 & 2.23 \(\pm\) 0.09 & 5.85 \(\pm\) 0.42 \\
2-3-butanedione & Nd & 1.33 \(\pm\) 0.01 & Nd & Nd \\
2-heptanone & Nd & 2.45 \(\pm\) 0.10 & Nd & 3.02 \(\pm\) 0.03 \\
1-hepten-3-one & Nd & Nd & 0.47 \(\pm\) 0.02 & Nd \\ Acetoin & Nd & 1.69 \(\pm\) 0.00 & 0.84 \(\pm\) 0.02 & 2.05 \(\pm\) 0.08 \\
2-3-octanediene & 0.17 \(\pm\) 0.00 & Nd & 0.34 \(\pm\) 0.03 & Nd \\
6-methyl-5-hepten-2-one & 0.22 \(\pm\) 0.00 & 0.49 \(\pm\) 0.02 & 0.27 \(\pm\) 0.04 & 0.77 \(\pm\) 0.04 \\
3,5-octaedien-2-one & 0.61 \(\pm\) 0.01 & 2.04 \(\pm\) 0.10 & 1.07 \(\pm\) 0.03 & 2.54 \(\pm\) 0.08 \\
3-octan-2-one & Nd & Nd & 0.55 \(\pm\) 0.02 & Nd \\ Tot & 4.52 \(\pm\) 0.07 & 10.62 \(\pm\) 0.23 & 5.77 \(\pm\) 0.09 & 14.23 \(\pm\) 0.63 \\ \hline Alcohols & & & & \\ ethanol & 0.18 \(\pm\) 0.01 & 0.17 \(\pm\) 0.00 & Nd & Nd \\
1-Pentanol & 3.92 \(\pm\) 0.13 & 4.68 \(\pm\) 0.14 & 3.05 \(\pm\) 0.18 & 4.00 \(\pm\) 0.15 \\
1-Hexanol & 3.56 \(\pm\) 0.00 & 6.42 \(\pm\) 0.14 & 2.43 \(\pm\) 0.01 & 5.77 \(\pm\) 0.15 \\
3,5-octaedien-2-ol & 0.36 \(\pm\) 0.01 & Nd & 0.23 \(\pm\) 0.01 & Nd \\
1-octen-3-ol & 1.20 \(\pm\) 0.05 & 1.73 \(\pm\) 0.02 & 1.36 \(\pm\) 0.10 & 1.49 \(\pm\) 0.03 \\ Benzenemethanol & Nd & 0.58 \(\pm\) 0.01 & Nd & Nd \\ Tot & 9.22 \(\pm\) 0.15 & 13.58 \(\pm\) 0.26 & 7.07 \(\pm\) 0.06 & 11.26 \(\pm\) 0.02 \\ \hline Furans & & & & \\
2-methylfuran & 0.52 \(\pm\) 0.00 & 4.90 \(\pm\) 0.08 & Nd & Nd \\
2-ethylfuran & 1.49 \(\pm\) 0.02 & 3.93 \(\pm\) 0.14 & 1.33 \(\pm\) 0.05 & 2.09 \(\pm\) 0.02 \\
2-pentylfuran & 4.70 \(\pm\) 0.21 & 8.73 \(\pm\) 0.42 & 4.91 \(\pm\) 0.12 & 3.34 \(\pm\) 0.02 \\ Tot & 6.70 \(\pm\) 0.22 & 17.57 \(\pm\) 0.20 & 6.24 \(\pm\) 0.16 & 5.43 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Volatile organic compounds (VOCs) identified by SPME-GC/MS in _Gnocchi_ samples.
## 3 Results and Discussion
### Methodology Analysis  
This section covers the results of the methodology following the steps depicted in Figure 1. To present these results in an ordered way, it has been split into three different subsections: the first covers the construction and validation of the reference model and uses it as a baseline to test its performance over the target dataset; the second presents the results obtained by using the methodology, preceded by a comparison of the dissimilarity filter proposed in this study with the rest of approaches exposed in Section 2.3; and the third section covers the comparison of the presented methodology with current transfer learning alternatives.
## 5 Experimental Setup
### Case Study
#### 5.1.1 Pilot Case  
The proposed solution has been tested on a production line used for the production of metal filters by SIFIM SrI (Jesi, Ancona, Italy), an Italian company leader which specializes in the production of this type of component for the household appliances industry and community kitchen industry (restaurant, hotels, canteens, etc.). SIFIM's production line can be divided into three functional blocks associated with the three production phases (See Figure 8):  
* Loading station: This is where the iron, steel, or aluminum sheet coil is loaded onto a decoiler.
* Work area: This is composed of a punching press that creates the weave of the metal mesh and a straightening area in which the mesh, passing through opposing rollers, is stretched and flattened.
* Unloading station: Here, a winding reel collects the coil of metal mesh produced.  
The first and the third module are equipped with a 3 kw and 3.5 kw three-phase asynchronous motor, respectively, while the second module uses four different types of motors: two three-phase asynchronous motors (one of 11.5 kw for the main activity and the other of 0.7 kw to manage the back-and-forth movement of the press in relation to the desired settings) and two brushless motors (2 kw and 0.7 kw) used to control the oscillation and lateral displacement of the press head. The production line overall has an average consumption ranging from 5 to 8 amps depending on the type of material used to produce the filters and the production velocity (aluminum, stainless steel, etc.). In fact, even though the nominal power of the production line is over 25 kw, the actuators work only for a few milliseconds in a time range of one second.
# Analysing Mouse Skin Cell Behaviour under a Non-Thermal kHz Plasma Jet
## 3 Results
### Plasma-Liquid Chemistry  
To fully understand the plasma jet impact on cells in a liquid culture medium, the potential plasma-induced modifications in liquid chemistry need to be evaluated in detail. Firstly, as the liquid culture medium's pH is one of the most important factors that significantly influence cell viability, the pH of MEM was measured immediately after exposure to plasma. The results demonstrated no significant discrepancies from the non-treated sample's values or control (Figure 4). Thus, it was concluded that the pH level was not a contributing factor. Along with pH, conductivity was measured. As in the results obtained from pH measurements, conductivity exhibited similar stability. Data are presented in the supplementary file.  
In contrast to the findings obtained for pH and conductivity measurements, the plasma treatments induced a slight increase in the concentrations of NO2- and H2O2. Figure 5a displays the concentrations of NO2- obtained immediately after treatment. As demonstrated from the results, the concentrations gradually increased with shorter distances between the plasma ignition point and MEM surface and longer exposure times. The maximum NO2- value (i.e., 10.5 uM) was measured at a distance of 10 mm after 60 s of exposure. Long distances led to negligible increases in NO2- concentrations. Similar trends were also observed for results obtained 24 h and 72 h after treatments (Figure 5c,e), where, generally, a small increase in NO2- concentration was noted. The shortest treatment distance and longest exposure time also led to the formation of the highest H2O2 concentration directly after plasma treatment (Figure 5b). A 60 s exposure of MEM to plasma at 10 mm distance resulted in the generation of 25.9 uM of H2O2. In contrast, concentrations of 19.5 uM and 6.3 uM were recorded at treatment distances of 15 mm and 20 mm, respectively. Unlike NO2-, H2O2 concentrations significantly shrank or completely disappeared after 24 h and 72 h (Figure 5d,f).  
Figure 4: pH of plasma treated MEM measured right after treatment. Results are presented as mean values \(\pm\) SEM (two-way ANOVA, Dunnett’s post-tests; n = 3).
# Frequency-Dependent Schroeder Allpass Filters
## 3 Proposed Frequency-Dependent Schroeder Allpass  
In this section, we propose an improved frequency-dependent Schroeder allpass filter. First, we introduce the filter structure. Secondly, we study the pole-zero locations depending on the gain filter. Thirdly, we demonstrate the proposed filter with a numerical example.
## 4 Implementation and Experimental Results  
The implementation included using CHB-MIT Scalp EEG database [55; 56], which contains EEG recordings with intractable seizures gathered at Children's Hospital Boston. After the cessation of anti-seizure medication, subjects were observed for up to several days to define their seizures and determine their suitability for surgical intervention. These recordings were made using the International 10-20 method of EEG electrode physical locations and names, as shown in Figure 2. In this method, the electrode's name points to its placement on the brain; C refers to the center of the head, F: to the front, T is temporary, P is a rear part, and O means occipital. In addition, the name also contains a number that refers to the side of the cerebral; for the right side, even numbers are used, and odd numbers are used on the left [57; 58].  
EEG signals of the CHB-MIT dataset were gathered from 24 cases (23 human subjects). Each case contains 9-42 continuous data files from a single subject. The sampling rate for the signals was 256 samples per second with 16-bit resolution. In this work, 20 files from 10 patients were chosen randomly to generate a stream containing 110,644 s and included normal and seizures signals. The total number of extracted rows was 28,324,864 rows with 23 columns and the class label.