esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 5 Antenna Performance Verification under an NLOS Environment
In this measurement, the distance (\(d\)) between the Tx and Rx was set as 2 m when the Rx antenna was at position 0. The input power was set at 15 dBm. In all measurements, the position of the Tx antenna was fixed, and only the Rx antenna position was varied from LOS to NLOS. The measured results are plotted for LOS (at positions 0 and 1) and NLOS (at the other positions) for both pairs of antennas in Figure 18. The results for the case of frequencies of 2.0 and 2.5 GHz are plotted in Figure 18a, and those for the case of frequencies of 3.0 and 3.5 GHz are plotted in Figure 18b. Figure 16: Received power measurement setup. Figure 17: Experiment setup at the university building corridor. (**a**) LOS. (**b**) NLOS. Figure 18: Measurement results of received power for the different operating frequencies: (**a**) 2.0 and 2.5 GHz and (**b**) 3.0 and 3.5 GHz. In the measurement results, the solid lines represent the omni-directional antenna (Tx) and the proposed quasi-isotropic antenna (Rx) pair, whereas the dotted line represents the reference with the omni-directional antenna pair. For positions 0 to 1, the received power shows little difference for both pairs. For positions 2 to 7, however, the received antenna power is clearly higher when the proposed quasi-isotropic antenna is used. There are some points where the proposed antenna shows slightly less power than the omni-directional antenna pair, and this is because the peak gain of the proposed antenna is lower than that of the omni-directional antenna at some frequencies, as in Table 3. Although the overall performance verifies, the quasi-isotropic antenna generally receives better results than the omni-directional antennas. This is due to the merit of the field uniformness in quasi-isotropic antennas that have the capability of receiving more power irrespective of their position.
## 5 Reward and State Definitions
Data availability for reward and state: The reward defined in this study relies on monitoring the number of vehicles crossing stop lines. The data can be easily collected using conventional traffic detection devices, such as underground inductive loop detectors. The defined state requires periodic measurement of vehicle locations and speeds near an intersection. These data can be captured through advanced video or radar detection systems, as well as emerging internet-of-vehicle or vehicle-to-infrastructure technologies.
## 2 Methodology
### Bayesian Optimization Methods
#### 2.2.2 Adding New Observation Points  
Additional observation points had to be added at suitable locations to estimate the distribution of the objective function more accurately. The PI method (Probability of Improvement) and the EI method (Expected Improvement) are commonly used to search observation points [34; 35]. We used the EI method to identify additional observation points as it has a wider search domain. New sampling points were determined via the EI method by comparing the mathematical expectation of the loss function of the current sampling point and other sampling points. Assuming that \(p^{+}\) is the optimal hydraulic conductivity vector inside the existing observation points, and \(y^{*}\) is the threshold value larger than \(L(p^{+})\), the mathematical expectation \(E(p_{s})\) can be expressed by Equation (2):  
\[\left\{\begin{array}{l}E(p_{s})=\int_{-\infty}^{y^{*}}(y^{*}-y)P(y|p_{s})\, dy=\frac{\ell(p_{s})(\gamma y^{*}-\int_{\infty}^{y^{*}}P(p_{s})dy)}{\gamma \ell(p_{s})+(1-\gamma)g(p_{s})}\\ P(p_{s}|y)=\left\{\begin{array}{ll}\ell(p_{s})&y\leq y^{*}\\ g(p_{s})&y>y^{*}\end{array}\right.\end{array}\right. \tag{2}\]  
where: \(P(y|p_{s})\) is the probability density function; \(\gamma\) is the distribution weight; \(\ell(p_{s})\) is the distribution function when \(y\leq y^{*}\); and \(g(p_{s})\) is the distribution function when \(y>y^{*}\). The vector of the hydraulic conductivity \(p_{best}\) that gives the optimal mathematical solution as shown in Equation (3), was selected as the new sampling point.  
\[p_{best}=\text{argmax}E(p_{s}) \tag{3}\]  
where: \(\text{argmax}E(p_{s})\) represents the \(p_{s}\) that provides the optimal \(E(p_{s})\). These newly selected sampling points allow for a more accurate estimate of the distribution of the loss function.
# Implementation of Tools for Lessening the Influence of Artifacts in EEG Signal Analysis
## 4 Implementation
### Partial Removal of Independent Components: fa_ica_partially_removed_components_method  
This function removes independent components from EEG signals with a high correlation with EOG signals, but only where artifacts were detected. For this, the correlation coefficients between ICA components and EOG data are used, and the impact of the components selected is removed at the samples marked as artifacts by the MSF.  
The syntax to use with this function is as follows:  
_cleaned_data = ffa_ica_partially_removed_components_method(data_eeg, MSF, fig)_. This function partially removes ICs of EEG data using the MSF and correlation information, and draws (if _fig_ = 1) a figure with the correlation between EEG and EOG signals before and after the procedure.  
The steps for this method are as follows:  
1. Obtain the VEOG and HEOG channels.
2. Obtain ICs of all the EEG channels.
3. Calculate the correlation coefficients between the ICs and the EOG signals, as in the previous method.
4. Sum all the correction coefficients between the EOG channels and each of the ICs.  
Partial elimination of ICs related to ocular artifacts using the function _fa_channels_from_ic_partial_reject_components_.  
This function makes use of the following:  
* _ft_selectdata_ to select EOG data.
* _ft_componentanalysis_ to obtain the ICs of all the EEG channels.
* _fta_corroeff_, which calculates correlation coefficients.
* _fta_channels_from_ica_partial_reject_components_, to partially reject components for reconstruction, where MSF = 1.  
The inputs to this function are as follows:  
1. _data_egeg_: EEG data to process.
2. _MSF_: Logical sequence with Fieldtrip structure compatible with data_eeg, where artifacts are marked as 1 and other samples as 0, as created by _fta_create_msf_fieldtrip_ (see Section 4.5).
3. _fig_: Logical value: 1 or 0 to plot, or not, the sum of the correlation coefficients before and after applying the method.  
The output of this function is as follows:  
1. _cleaned_data_: Fieldtrip-structured data with reduced influence of ocular artifacts.  
In the examples section, a brief illustrative script of Matlab code will be shown.
## 1 Introduction  
Bone fractures are one of the most common injuries nowadays. Every year, 2.7 million fractures occur across the EU6 nations, France, Germany, Italy, Spain, Sweden, and the UK [1]; an incredible number of people suffers from this disorder and the implications of an untreated fracture may lead to permanent damage or even death. A great responsibility for this lies with the doctors, who have to evaluate tens of X-ray images a day. The technology utilized for first diagnosis is mostly X-ray, which is a modality that has been used for more than one hundred years and is still frequently used. It is challenging for doctors to evaluate X-ray images: firstly, X-ray could hide certain particularities of the bone; secondly, a lot of experience is needed to correctly classify different types of fractures; thirdly, doctors have often to act in emergency situations and may be constrained by fatigue. Actually, it has been shown that the performance of radiologists in the interpretation of musculoskeletal radiographs decrease in fracture detection by the end of the work day compared to the beginning of the work day [2]. In addition, radiographic interpretation often takes place in environments without the availability of qualified colleagues for second opinions [3]. The success of the treatment and prognosis strongly depends on an accurate classification of the fracture among standard types, such as those defined by the Arbeitsgemeinschaft fur Osteosynthesfragen (AO foundation). In that context, a computer-aided diagnosis (CAD) system able to help doctors might have a direct impact in the outcome of the patients. In this paper, we reviewed some selected papers concerning this topic, starting from basic approaches to the main advanced solutions. Initial prior works on the detection and classification of fractures [4; 5; 6] have focused on conventional machine learning processes consisting of pre-processing, feature extraction and classification steps. Recently, impressive results have been obtained using deep learning [7] methods. A total of 232 records were identified through database searching and other sources. At total of 107 records were screened and 65 of them excluded, resulting in 42 full-text articles assessed for eligibility. Among these papers, we selected 11 records for analysis. This whole process is shown in Figure 1 with a PRISMA flow diagram [8]. The majority of them pursue the classification between fractured and not fractured bones, while just two of them tried to classify the different types of fractures. We have chosen papers which, in our personal opinion, contain the strengths given by a deep learning approach that should be used in order to develop a generic tool able to classify every type of fracture in each bone of the human body.
## 2 Materials and Methods
### Direct Laser Interference Patterning  
Different two-beam interference setups were utilized to produce the different hierarchical geometries on all investigated materials. In the case of metals, the DLIP experiments were carried out using a IR (1064 nm) picosecond laser (solid-state Q-switched Innoslab Nd:YVO4, pulse duration 10 ps, repetition rate of 1 kHz) combined with a DLIP optical head (Fraunhofer IWS) which allow to split the main laser beam into two sub-beams which are later recombined by an aspherical lens on the materials surface producing the line-like pattern at the interference area.  
For the polycarbonate material, a nanosecond UV (263 nm) laser system was used. In this case, a Q-switched diode-pumped solid-state laser (Laser Export, TECH-263 Advanced) was used, with a pulse duration of 3 ns, a repetition rate of 1 kHz and a maximal pulse energy of 50 \(\upmu\)J. The experiments were conducted also using a self-developed DLIP system (DLIP-\(\upmu\)FAB, Fraunhofer IWS), producing pixels containing the line-like interference pattern with diameters of \(\sim\)20 \(\upmu\)m. Taking into consideration the optical loses, irradiances up to \(\sim\)3.10\({}^{9}\) W/cm\({}^{2}\) can be reached. To cover larger areas, the substrate was translated in X and Y directions and a specific distance between the pixels were defined.  
In the case of the resist materials, a standard DLIP beam splitter configuration was used [26]. Before irradiation, the samples were pre-baked on a hotplate at 95 \({}^{\circ}\)C for 2 min. Following, the substrate was exposed using an 8 ns pulsed Nd:YAG laser (Quanta Ray Pro 290-10) with the 3 rd harmonic wavelength (355 nm) and a repetition rate of 10 Hz. The beam splitter set-up was used to overlap both beams with an angle of \(\theta\) = 4.07\({}^{\circ}\) and \(\theta\) = 41.59\({}^{\circ}\) to reach spatial periods of \(\Lambda\) = 5.0 \(\upmu\)m and \(\Lambda\) = 0.5 \(\upmu\)m, respectively. The samples were irradiated with two single pulses with a 60\({}^{\circ}\) rotation in between to obtain a hole-like pattern with a hexagonal arrangement. The laser fluence was set to 4 mJ/cm\({}^{2}\) and 7.5 mJ/cm\({}^{2}\) for fabricating 0.5 \(\upmu\)m and 5 \(\upmu\)m periodic patterns, respectively. After the exposure, the samples were post-baked at 95 \({}^{\circ}\)C for 2 min. The development of the SU-8 was carried out by dipping the samples into the mr-dev 600 (Microchem Corp.) developer solution for 1 min. Subsequently, the samples were rinsed in isopropanol in the same way for 1 min and were hard baked at 95 \({}^{\circ}\)C for 5 min.
## 5 Discussion
### Hydrodynamic Changes
#### 5.1.2 Alteration in Bar Submergence Frequency  
The variation pattern of the annual discharge process in typical years is relatively consistent, with the phenomenon of the discharge process flattening occurring in all instances. Nevertheless, to assess the influence of flow on the interaction between channel and bars, we select frequency changes of overbank flows and flood discharge at stations along the MYR in different time series. This approach helps to minimize any cognitive bias that may arise due to the typical selection of years in the previous section.  
Figure 7 illustrates the alterations in bar submergence frequency and flood occurrence probability for six hydrometric stations along the MYR during pre- and post-TGD periods. The findings reveal that both the bar submergence frequency and flood occurrence probability at the stations in the MYR have declined following the TGD operation.  
Overall, from period 1 to period 3 (Table 1), the bar submergence frequency at hydro-metric stations in the MYR declined by 49-62%, while the flood occurrence probability dropped by 69-76%. Bar submergence frequency and flood occurrence probability decreased as follows: at Station 1, by 51% and 69%; at Station 2, by 55% and 74%; at Station 4, by 54% and 74%; at Station 6, by 49% and 73%; at Station 8, by 61% and 76%; at Station 9, by 62% and 74%, respectively.  
Generally, when the water depth reaches approximately 1.5 m, it is unfavorable for the growth of bar vegetation [57; 58; 59]. Here, based on the relationship between water level and discharge, the corresponding water depth of flood discharge chosen by each hydrometric station is roughly 2 m higher than that of floodplain discharge (approximately 0.5 m higher than the water depth threshold suitable for vegetation growth). As illustrated in [9] (with Yichang Station and Luoshan Station as examples), we believe that the occurrence of flood flow is detrimental to vegetation growth. Following the TGD operation, the frequency of floods decreases at all stations along the MYR (with a concurrent reduction in bar submergence frequency), which will provide more favorable conditions for the growth of vegetation in elevated bar areas.  
Figure 7: Alterations in bar submergence frequency and flood discharge frequency for different hydrometric stations in the pre- and post-TGD periods. The blue (red) bar is the bar submerged frequency (flood discharge frequency) of each hydrometric station and the color gradient from lighter to darker signifies the progression of time, ranging from far to near (period 1 through period 3).
# Study on the Effect of Vibrating Process on the Compactness of Slipform Concrete
###### Abstract  
Compaction directly affects concrete's strength and durability. In this paper, the relationship between vibration parameters and compactness is modeled by using colored aggregates and image methods to analyze the compactness of concrete characterized by pore structure. Experiments were conducted to investigate the effects of vibrating frequency and duration on the aggregate distribution coefficient, segregation rate, and porosity of slipform concrete. The test results showed that the smaller the aggregate size under high-frequency bounding, the better the concrete compactness. In addition, the aggregate segregation rate and concrete strength increased and then decreased with the bounding length, and the greater the bounding frequency, the more pronounced the trend. Lastly, the concrete's internal porosity increased and then decreased with the bounding length. This study obtained the control range of aggregate segregation rate and porosity through analysis, and established equations for the optimal vibration parameters and paving speed of sliding form concrete to guide the reasonable control of actual sliding form paving and vibration construction processes.  
vibrating process; sliding form concrete; intensity of the concrete; hole structure +
Footnote †: journal: Applied Sciences
# A MILP Model for a Byzantine Fault Tolerant Blockchain Consensus
## 4 Computational Experiments
### Exploring Minimization and Maximization Optimization Directions  
An interesting feature of the proposed model is its flexibility in being Minimized or Maximized. Table 2 summarizes some combinations of weights and optimization directions used to exemplify the extreme scenarios highlighted in this paper. Weights \(\omega_{1}\), \(\omega_{2}\) and \(\omega_{3}\) are described at Section 3.3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline _Instance_ & \(N\) & \(t^{max}\) & **Vars** & **Binaries** & **Integers** & **Const.** & **Equa.** & **Ineq.** & **Nzs** \\ \hline dbft-2-4-5 & 4 & 5 & 1722 & 1700 & 22 & 2838 & 611 & 2227 & 11,708 \\ dbft-2-4-10 & 4 & 10 & 3402 & 3380 & 22 & 5238 & 931 & 4307 & 36,233 \\ dbft-2-4-15 & 4 & 15 & 5082 & 5060 & 22 & 7638 & 1251 & 6387 & 72,758 \\ dbft-2-7-5 & 7 & 5 & 8196 & 8141 & 55 & 12,745 & 2453 & 10,292 & 55,871 \\ dbft-2-7-10 & 7 & 10 & 16,281 & 16,226 & 55 & 23,700 & 3433 & 20,267 & 177,571 \\ dbft-2-7-15 & 7 & 15 & 24,366 & 24,311 & 55 & 34,655 & 4413 & 30,242 & 361,396 \\ dbft-2-10-5 & 10 & 5 & 22,716 & 22,610 & 106 & 34,358 & 6203 & 28,155 & 154,982 \\ dbft-2-10-15 & 10 & 15 & 67,716 & 67,610 & 106 & 93,958 & 10,203 & 83,755 & 1,021,962 \\ dbft-2-10-25 & 10 & 25 & 112,716 & 112,610 & 106 & 153,558 & 14,203 & 139,355 & 2,604,942 \\ dbft-2-16-5 & 16 & 5 & 88,854 & 88,592 & 262 & 130,846 & 22,019 & 108,827 & 606,716 \\ dbft-2-16-25 & 16 & 25 & 442,134 & 441,872 & 262 & 589,086 & 42,499 & 546,587 & 10,450,216 \\ dbft-2-19-25 & 19 & 25 & 731,772 & 731,405 & 367 & 969,093 & 64,261 & 904,832 & 17,405,223 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Generated instances with distinct sizes \(N\) and \(t^{max}\).  
As described on Table 2, Scenarios P1 and P2 are maximization problems with \(\omega_{1}\) set to 1000, favoring a high value of \(B^{\prime}\), with different values for \(\omega_{2}\) (100 and \(-100\)) regarding objective \(V^{\prime}\) (that can be considered time-related, as it counts the number of successive views in the system). This allowed the experimentation of interesting scenarios, where Byzantine actors behave differently depending on the _cost/benefit_ from the combination of weights. Due to \(\omega_{3}\), Objective \(C^{\prime}\) is only regarded in P4. Table 2 scenarios were built from practical experimentation regarding PBFT and dBFT theory, when exploring (optimizing) the instances in Table 1.
# Multi-GeV Laser Wakefield Electron Acceleration with PW Lasers
## 3 Multi-GeV LWFA with PW-Class Lasers
### LWFA with Capillary Discharge Plasmas at LBNL
\begin{table}
\begin{tabular}{c c c c} \hline \hline Spectrum & Experiment 1 & Experiment 2 & Experiment 3 \\ \hline Channel length (cm) & 3.3 & 9 & 20 \\ Plasma density (10\({}^{17}\) cm\({}^{-3}\)) & 43 & 7 & 3.4 \\ Laser power (TW) & 40 & 300 & 850 \\ Laser pulse duration (fs) & 40 & 40 & 40 \\ E\({}_{\text{peak}}\) (GeV) & 1 & 4.2 & 7.8 \\ Energy spread (\%) & 1.6 (r.m.s.) & 6 (r.m.s.) & \textasci{10} (FWHM) \\ Divergence (mrad) & 1.6 (r.m.s.) & 0.3 (r.m.s.) & 0.2 (FWHM) \\ Charge in peak (pC) & \textasci{10} & 6 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of electron beams from capillary discharge plasma channel and experimental condition for three different experiments with laser power of 40 TW [37], 300 TW [19], and 850 TW [12]. Reproduced with permission from [[PERSON], [PERSON]], [_Nat. Phys_]; published by [Nature Publishing Group], [2006]. the axis of the discharge capillary plasma channel, heated through inverse bremsstrahlung the core of the capillary discharge plasma channel and created a deeper electron density valley that could guide a PW laser pulse efficiently. Thus, the effect of the nonlinear laser propagation could be mitigated, and the laser pulse could propagate tens of centimeters in a plasma channel with an electron density of 3 \(\times\) 10\({}^{17}\) electrons/cm\({}^{3}\). This improvement of laser propagation by a steepened plasma channel made it possible to generate the most energetic electron beam of about 8 GeV from LWFA (experiment 3 in Table 2). This result implies that engineering the plasma medium is a key to realizing the maximum electron energy expected by the power-scaling of the electron energy. Consequently, more and more efforts should be exerted to control the plasma medium, not only to increase laser power but also to find suitable electron acceleration conditions for high-energy electron beams by LWFA.
## 2 Materials and Methods
### Figures of Merit  
In evaluating the results of our experiment, we employ several key figures of merit to quantify the performance.  
Accuracy is the proportion of true results (both true positives and true negatives) among the total number of cases examined. It can be calculated using the following equation:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{7}\]  
Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. It is calculated as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{8}\]Recall (also known as sensitivity, hit rate, or true positive rate) is the fraction of the total amount of relevant instances that were actually retrieved. The equation for recall is:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
The F1 score is the harmonic mean of precision and recall. It aims to find the balance between precision and recall. The F1 score can be calculated as follows:  
\[\text{F1}=2\frac{\text{Precision Recall}}{\text{Precision}+\text{Recall}} \tag{10}\]  
In these formulas, TP stands for True Positives, TN for True Negatives, FP for False Positives, and FN for False Negatives.
## 2 Related Works  
Full rotation invariance can be ensured by using a matched filter built from a CHC of the reference [7]. In other words, the reference is replaced by one of its CHCs. To improve the peak sharpness and/or the discrimination ability of the classical CHF, several designs based on CHCs, such as the CH covariance filter [8], the phase-only CHF [9], and the phase-derived CHF [9] were proposed. These attempts of improvement, however, were possible at the cost of a decrease in the signal-to-noise ratio (SNR). It is fair to say that the oldest design, the classical CHF, yields the highest SNR among the CHF family. In addition, the classical CHF maximizes the SNR while maintaining the in-plane rotation invariance [10].  
The SNR decreases as the order of the CHC is increased. The zero-order CHF is the best choice for pattern recognition under noisy conditions [10]. Unfortunately, low-order CHFs have a tendency to produce broad correlation peaks. This limitation can be overcome, and we can achieve, on the one hand, an important noise resistance, and on the other hand, sharp correlation peaks in addition to high optical efficiency. The technique consists of projecting both the scene image and the reference onto the zero-order CHC. The discrimination ability remains unchanged. The low discrimination ability of the CHFs is a direct consequence of image compression. The proposed approach can be combined with various design techniques to improve this criterion. However, this is not the objective of this work. The resulting image compression into one dimension can be extended to include parallel image processing and further geometrical invariance, as well as by using the second spatial dimension. An optical setup, including a bank of one-dimensional patterns, is proposed for parallel classification, where a dataset of scene images is entered simultaneously.