esa-sceva/satcom-chunk-collection huggingface dataset:
# A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios
## 5 Results and Discussion  
### Experimental Results  
The results of Experiment 1 are shown in Table 1; both the stave detection model and the note head position significantly improve the note pitch accuracy. The note head position improves the note pitch accuracy of both the comb filter-based method and the stave detection model, which indicates that the relative position of note heads on the stave is an important factor affecting note pitch accuracy. Moreover, the note pitch accuracy of the stave detection model without note head position is higher than that of the comb filter-based method with note head position. The stave detection model with note head position achieves the highest accuracy of 99.23%. This demonstrates that our stave detection model can effectively detect the position of staff lines and works well in conjunction with the note head position.  
The results of Experiment 2 are shown in Table 2. It is clear that our method performs much better than the CRNN-CTC on recognition in camera-based scenarios when the training set consists of only the PrIMuS dataset. Even when the CPMS dataset, which includes sheet music photos, was added to the training set, our method still comprehensively outperformed the baseline. This indicates that our method is more robust and much better at learning photographic features on the training data than CRNN-CTC.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Note Pitch Accuracy on CPMS(\%)** \\ \hline Comb filter-based method & 85.54 \\ \hline Comb filter-based method + note head position & 91.23 \\ \hline Stave detection model & 95.49 \\ \hline Stave detection model + note head position & 99.23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pitch accuracy comparison between different methods.  
The results of Experiment 3 are shown in Table 3. After training on DeepScoresV2, which does not contain sheet music photos, our method achieves higher pitch accuracy and note accuracy compared to the mixed dataset training of the PrIMuS and CPMS datasets, whereas the type accuracy is slightly lower than the best one. This indicates that our method is able to handle the complexities of sheet music photos in real-world scenarios through training on non-photographic data, demonstrating low data dependency and high robustness.
## 4 Algorithm for Carrier Aircraft Dynamic Scheduling  
In the RCPSP, when the resource constraints and number of projects are small, some exact algorithms (e.g., branch and bound algorithm, linear programming-based algorithm) can be employed to get the scheduling. However, the carrier aircraft support operation problem, which is considered a multi-resource constraint multi-project scheduling problem, involves plenty of carrier aircraft and activities. For this case, a heuristic algorithm has been developed. In this paper, we use the dual population genetic algorithm (DPGA), which has been used for adaptive diversity control [43], and combine the heuristic algorithm with the RH strategy to solve the carrier aircraft dynamic scheduling problem.  
The DPGA is a parallel genetic algorithm that is designed to improve the quality and diversity of the population. Firstly, encoding and decoding processes are set to get population and matching scheduling. The left and right populations proceed simultaneously with crossover, mutation, and local search processes. For the left population, right scheduling is made in the encoding and decoding processes. For the right population, left scheduling is made in the encoding and decoding processes. The original coding is corrected by decoding the end time of the scheduling solution, and the elite population is reserved. The pseudo-code of the DPGA with a local search strategy is described in Algorithm 2 The details are clearly expressed in the next section.  
```
0: The object network data
0: A project scheduling
01: Initialize [pop_left, pop_right];
02: Decoding_left(chromosome[pop_left]), schedule_left([pop_left]);
03: Fitness[pop_left], evaluate(schedule_left[pop_left]);
04: For generation = 1 to GEN do
05: Select_parents(chromosome[pop_left], parents[pop_left]);
06: Crossover(parents[pop_left], children[pop_left]);
07: Mutation (children[pop_left]);
08: Decoding_left(chromosome[pop_left]), schedule_left([pop_left]);
09: Fitness[pop_left], evaluate(schedule_left[pop_left]);
101: Select local optimal individual[pop_left];
11: Local search for the left schedule;
12: Update chromosome[pop_right]. Save elite[pop_left]
13: Select_parents(chromosome[pop_right], parents[pop_right]);
14: Crossover(parents[pop_right],children[pop_right]);
15: Mutation (children[pop_right]);
16: Decoding_right(chromosome[pop_right]), schedule_right([pop_right]);
17: Fitness[pop_right], evaluate(schedule_right[pop_right]);
18: Select local optimal individual[pop_right];
19: Local search for the right schedule;
20: Update chromosome[pop_left] Save elite[pop_right];
21: End for
22: Decoding_left(chromosome[pop_left]), schedule_left([pop_left]);
23: Return a best schedule;
```  
**Algorithm 2** The pseudo-code of the dual population genetic algorithm for carrier aircraft dynamic support scheduling under uncertain conditions.
# Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area
## 3 Design and Implementation
### Testing Scenario  
In this scenario, we verify that the maximum distance and packet delay of the LoRa-based Dragino implementation in agriculture can be reached. The test is conducted by varying the spreading factor and the bandwidth. First, the spreading factor is varied from 7, 9, and 12. Moreover, the bandwidth is set in values of the range of 7800-500,000 Hz. The delay as well as the maximum data are measured to have an insight into the data communication from the client side to the server. The location of clients is spread out in the range of 50-1000 m with an increment of 100 m. Figure 4 shows the test flowchart of LoRa-based Dragino implementation. For the delay test, we take the average value of the delay at each location that is passed for 1 min. The delay measurement was implemented in the Microcontroller used by device nodes. The scheme for calculation is depicted in Figure 5. Please note that the spreading factor is set firstly, then followed by the bandwidth value. This is required to identify the sufficient bandwidth value so that the data is transmitted at the closest distance to the server.  
Figure 4: Testing scenario flowchart.
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
###### Abstract  
Recently, due to the miniaturization of electronic products, printed circuit boards (PCBs) have also become smaller. This trend has led to the need for high-precision electrical test equipment to check PCBs for disconnections and short circuits. The purpose of this study is to improve the position repeatability of the platform unit up to \(\pm\)2.5 \(\upmu\)m in linear stage type test equipment. For this purpose, the causes of the position errors of the platform unit are analyzed. The platform unit holding the PCB is driven by a single-axis linear ball screw drive system offset from its geometric center due to design constraints. The yaw rotation of the platform is found to have a dominant effect on position repeatability. To address this problem, adding balancing weights to the platform unit and adjusting the stiffness of the LM Guides are proposed. These methods reduce the yaw rotation by moving the centers of mass and stiffness closer to the linear ball screw actuator. In the verification tests, the position repeatability was decreased to less than \(\pm\)1.0 \(\upmu\)m.  
precision stage; balancing weight; drive force offset; yaw motion; error prediction; low-cost stage; linear motion guide (LM Guide); ANSYS bushing joint Article
# Extension of the Discrete Electron Transport Capabilities of the Geant4-DNA Toolkit to MeV Energies
## 3 Results and Discussion
### Stopping Power Simulations
In Figure 7, we illustrate the influence of the various corrections implemented into the present model (DNA-Opt4X) (see Figure 1) in the calculation of the SP. Specifically, the percentage difference between the present SP data, calculated with and without the individual corrections, and the ICRU values (which are used for benchmarking), is presented over the energy regime where these corrections are applied, i.e., in accordance with Figure 1, we have split the comparison into the energy regions 1-100 keV (Regime II), 100 keV-1 MeV (Regime III), and 1-10 MeV (Regime IV). Note that the comparison is limited to the energy range from 1 keV to 10 MeV where SP data are provided by ICRU. Evidently, the deviations of the present model from ICRU decrease from \(\sim\)10-15% (without corrections) to \(\sim\)3% (with corrections). In Figure 8, we focus on the influence of the low-energy corrections (i.e., the Mott and the Coulomb corrections) on the SP over the energy range where these corrections are applied, i.e., from 10 eV to 100 keV (see Figure 1). Specifically, we depict the ratio of the SP with the corrections to the SP without the corrections, i.e., \(\frac{\mathrm{SP}_{\mathrm{with\_corrections}}}{\mathrm{SP}_{\mathrm{with\_corrections}}/ \mathrm{out\_corrections}}\). Both the individual and combined effects of the Mott and Coulomb corrections are shown. As expected, the ratio is less than unity at all energies depicted. Specifically, it can be seen that the combined effect of the Mott and Coulomb corrections reduces the SP by up to \(\sim\)40% at 100 eV and gradually vanishes at 100 keV where the ratio approaches unity (\(\geq\)0.99), i.e., the effect on SP falls below the 1% level. Therefore, low-energy corrections are included in DNA-Opt4X only in the energy Regimes I and II (see Figure 1). Figure 7: Percentage difference in the electronic stopping power (SP) calculated by the present model (DNA-Opt4X) with and without the various correction terms from the values of ICRU Report 90 [59]. The notation is as follows: “Mott-Co” denotes the Mott–Coulomb low-energy correction (see Section 2.3), “Asy” refers to the high-energy asymptotic correction (see Section 2.4), “Fermi” refers to the (relativistic) Fermi density correction (see Section 2.4).
###### Abstract
Unsupervised ML algorithms are commonly less effective in classification but can provide other benefits, for instance, when working with imbalanced data [20]. Additionally, unsupervised methods do not require data pre-labelling and tend to be more explainable [21]. The ultimate goal of our work was to propose a decision support system (DSS) for epilepsy diagnostics as explainability is highly important in medicine. Thus, we decided to use unsupervised ML methods specifically designed to work with imbalanced data and perform anomaly detection. A literature search showed that some unsupervised methods rely on complex pre-processing [22] or invasive techniques [23], neither of which are acceptable for rapid clinical diagnostics in human patients. In this research, we decided to use EEG data with minimal pre-processing to devise a system capable of operating in a clinical setting. The main goal of our work was to estimate the performance of unsupervised anomaly detection models as a system for automatic pre-labelling of epileptic seizures on human EEGs. Recently, we showed that EEG data with epileptic seizures are not only imbalanced but also demonstrate features of extreme event behaviour [24; 25]. We used this knowledge to apply anomaly detection techniques to label epileptic EEG data. These techniques belong to unsupervised ML algorithms that operate without training data. We tested a one-class support-vector machine (SVM), a popular outlier detection algorithm, on the data of 83 patients and reported 77% sensitivity and 12% precision [26]. Here, we further investigate the ability of outlier detection algorithms to detect seizures. We start by introducing popular algorithms for outlier detection, including SVM, ensemble method, and distance-based methods, and adjust their hyperparameters to ensure the best score. Then, we trained these algorithms using various features of EEG signals, including two frequency ranges and three types of spectral power: full spectrum, mean, and principal components (PCs). With the obtained results, we tested two hypotheses: (i) the algorithm type and features impact performance; (ii) predicted performance based on prior knowledge about the algorithm type and features. The remainder of this paper is organized as follows. Section 2 presents the materials and methods, describing the used dataset, details on the following data processing, and feature engineering procedures. Section 2 also includes information on the proposed methods:Block diagram, details on the different ML algorithms tested, and a description of processes for evaluation and hyperparameter optimization. Section 3 presents the results and discussion, including the results of hyperparameter optimization and an evaluation of the models with optimal hyperparameters. Section 3 also includes the results of statistical comparisons of different algorithms as well as different features. Section 4 presents the conclusion.
## 3 Results and Discussion
### Time Consumption  
The time complexity of the tasks in this approach, in descending order, is as follows:  
* Creating a 3D model,
* Software Tools Setup,
* Heat map rendering time.  
The time taken to create a 3D model from a floor plan is strongly dependent on the complexity of the floor plan. There is only one room in this simulation and the time required to create the model is 3 min. It takes 3 min to set up the software. The rendering time of the heat map is strongly dependent on the complexity of the scene, the number of EM emitters, i.e., light sources, and the desired resolution of the heat map. The results for different heat map resolution configurations and different numbers of sources in the scene can be seen in Table 6. The difference in simulation time between antennas 1 and 2 are lower units of seconds, and this difference can be considered negligible. The simulation times given in Table 6 are for antenna 1. In the case of two active emitters, both antennas are active in the heat map. The simulation times for one and two active emitters differ by lower units of seconds and increase with increasing resolution of the heat map. In this simulation, the computation is performed in only one closed corridor. In the case of multiple simulation rooms, a larger difference in time between one and two active emitters can be expected as the computational complexity of propagating reflections and refractions increases.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & **Univariate Logistic Model** & **Multivariate Logistic Model** \\ \hline \multirow{2}{*}{**Skeletal Parameter**} & **Not Severe** & **Severe** & \multirow{2}{*}{_p-Value_} & **Odd Ratio** & **Confidence** & **Odd Ratio** & **Confidence** \\  & **Prevalence (\%)** & **Prevalence (\%)** & & **(OR)** & **Interval** & **(OR)**\({}^{\circ}\) & **Interval** \\ \hline \multicolumn{6}{l}{Ponticulus posticus,} \\  & (\%) & & & & & \\ Grade 1 1  & 38.5 & 42.9 & 0.803 & 1 & 1 & \\ Grade 2 & 35.9 & 25.0 & 0.428 & 0.62 & 1.92–2.04 & 0.62 & 0.17–2.20 \\ Grade 3 & 12.8 & 17.9 & 0.731 & 1.25 & 0.29–5.34 & 1.37 & 0.31–6.19 \\ Grade 4 & 12.8 & 14.3 & 1 & 1 & 0.22–4.56 & 0.85 & 0.16–4.50 \\ \hline \multicolumn{6}{l}{Sella Bridging, (\%)} \\  & Grade 1 1  & 10.3 & 17.9 & 0.474 & 1 & 1 & \\ Grade 2 & 51.3 & 64.3 & 0.326 & 0.72 & 0.16–3.10 & 0.77 & 0.17–3.55 \\ Grade 3 & 38.5 & 17.9 & **<0.001 1 ** & 0.26 & 0.05–1.40 & 0.29 & 0.05–1.58 \\ \hline \multicolumn{6}{l}{Atlas posterior arch} \\  & deficiency, (\%) & & & & \\ Improbable 1  & 84.6 & 75.0 & 1 & 1 & \\ Possible & 15.4 & 25.0 & 1.83 & 0.54–6.02 & 2.20 & 0.58–8.03 \\ \hline \end{tabular}
\end{table} 
Table 6: Descriptive statistics and results of univariate and multivariate logistic analysis with not severe/severe maxillary canine impaction (MCI) for the following skeletal parameters: PP, SB and APAD. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Skeletal Parameter**} & **Male (\%)** & **Female (\%)** & \(p\)**-Value** \\ \hline \multicolumn{3}{c}{Ponticulus posticus, (\%)} \\  & Grade 1  & 54.0 & 66.7 & 0.198 \\  & Grade 2 & 28.0 & 15.5 & 0.118 \\  & Grade 3 & 12.0 & 7.1 & 0.363 \\  & Grade 4 & 6.0 & 10.7 & 0.534 \\ \hline \multicolumn{3}{c}{Sella Bridging, (\%)} \\  & Grade 1  & 26.0 & 25.0 & 0.999 \\  & Grade 2 & 54.0 & 52.4 & 0.999 \\  & Grade 3 & 20.0 & 22.6 & 0.830 \\ \hline \multicolumn{3}{c}{Atlas posterior arch deficiency, (\%)} \\  & Improbable 1  & 86.0 & 89.3 \\  & Possible & 14.0 & 10.7 & \\ \hline \multicolumn{3}{c}{Shape of pterygopalatine fissure} \\  & (length/width), (mean \(\pm\) SD) & 4.16 \(\pm\) 0.82 & 4.28 \(\pm\) 0.99 & 0.804 \\ \hline \multicolumn{3}{c}{\({}^{\ast}\)\(p<0.05\); \({}^{\rm a}\): reference category.} \\ \end{tabular}
\end{table} 
Table 5: Prevalence of skeletal measurements in males and in females.
## 2 Materials and Methods
### Mix Design  
In order to perform the study, AC 156 Surf 35/50 S (AC16S) bituminous mixes were fabricated according to Spanish norms. Table 4 shows the aggregate gradation of the mixes fabricated with the control aggregate (AC-PA), with well cuttings (AC-WC), and with mine tailings (AC-MT), along with the aggregate grading curve established for bituminous mixes according to Spanish specifications.  
Cylindrical samples, plates, and slabs with different bitumen contents were prepared for this study. The bitumen content for the AC-PA and AC-WC was set at 3% in the samples with the lowest content and was incremented by 0.5% to a maximum of 5.5% in the samples with the highest bitumen content. The bitumen content for AC-MT samples ranged from 3.5% to 6% in 0.5% increments, except for the samples used for Marshall tests, which ranged from 3% to 6.5%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Units** & **35/50** \\ \hline Penetration (25 \({}^{\circ}\)C) & mm/10 & 40.0 \\ Softening point & \({}^{\circ}\)C & 53.8 \\ Penetration index & - & \(-\)0.80 \\ Specific weight & kg/m\({}^{3}\) & 1030 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the 35/50 bitumen.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Aggregate Properties** & \multicolumn{2}{c}{**Standard Test**} & \multirow{2}{*}{**Limit**} & \multicolumn{3}{c}{**Coarse Aggregate**} & \multicolumn{2}{c}{**Fine**} & \multirow{2}{*}{**Filler**} \\ \cline{3-4} \cline{6-7}  & & **Method** & & **PA** & **WC** & **MT** & **Aggregate** \\ \hline Apparent particle density (kg/m\({}^{3}\)) & UNE EN 1097-6 & - & 2937 & 2750 & 2837 & 2719 & - \\ Dry particle density (kg/m\({}^{3}\)) & UNE EN 1097-6 & - & 2869 & 2641 & 2617 & 2641 & - \\ Saturated surface dry density (kg/m\({}^{3}\)) & UNE EN 1097-6 & - & 2892 & 2681 & 2695 & 2669 & - \\ Water absorption after 24 h (\%) & UNE EN 1097-6 & - & 0.8 & 1.5 & 2.96 & 1.1 & - \\ Flakiness index (\%) & UNE EN 933-3 & \(\leq\)20-30 & 13.5 & 28.5 & 15.5 & - & - \\ Los Angeles Abrasion loss (\%) & UNE EN 1097-2 & \(\leq\)20-25 & 10 & 19 & 21 & - & - \\ Polished Stone value & UNE EN 1097-8 & \(\geq\)56-44 & 54 & 67.3 & 81.5 & - & - \\ Bulk density (kg/m\({}^{3}\)) & UNE EN 1097-3 & 500–800 & - & - & - & - & 620 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results for the aggregate types used in the mixes.
## 1 Introduction
Many factors significantly impact the extraction process, including solvent concentration, solid-to-liquid ratio, temperature, and extraction time [62]. Therefore, optimizing extraction conditions is a significant research challenge [63]. Response surface methodology (RSM) is a popular statistical and mathematical technique [39; 64; 65; 66] used to optimize the range of variables in diverse experimental processes to decrease the number of experimental runs, time, and costs [58] required to evaluate the effects of multiple factors and their interactions on one or more responses [63; 67]. It has been successfully applied to optimizing ultrasound-assisted extraction processes [68]. Several studies have investigated the extraction of phenolic compounds from medicinal plants of the Algerian Sahara. Nevertheless, to our knowledge, research is still necessary to optimize the extraction conditions of plants of the Chenopodiaceae family from the El-Oued region. This study is a novel approach aiming to investigate the influences of different extraction parameters (solid-to-liquid ratio, extraction temperature, and extraction time)on three responses: extraction yield, total phenolic content, and total flavonoid content of the plant _Cornulaca monacantha_ Del. using the Box-Behnken Design of response surface methodology. The results identified the optimal extraction conditions, which maximized the studied responses, and this optimized protocol was applied to another plant of the same family (_Anabasis articulata_ (Forssk.) Moq.). Additionally the antioxidant activities of these plant extracts were evaluated.
## 3 Results and Discussion
### TDS-1 Data Analysis at Regional Scale
#### 3.2.1 CEMADEM-Brazil  
The area of Brazil analyzed spans from 5\({}^{\circ}\) to 25\({}^{\circ}\) South, 55\({}^{\circ}\) to 25\({}^{\circ}\) West. Following the same approach as in Section 3.1, the sensitivity of TDS-1 derived reflection coefficients with respect to SMOS L3 SM data have been derived. Figure 13a shows the main results, when all corrections are applied, and for different ranges of incidence angles. See figure caption for details. Results are discussed at the end.  
SMOS L4 SM Sensitivity  
Similarly, Figure 13b shows the main results, when all corrections are applied, and for different ranges of incidence angles for SMOS L4 SM (1 km resolution). See figure caption for details. The results will be discussed at the end.