esa-sceva/satcom-chunk-collection huggingface dataset:
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 4 Simulation Results
### Simulation Settings  
In the simulations, a uniform linear array (ULA) was used to receive the GNSS signal in the jamming scenario. The structure of the ULA is shown in Figure 2. The parameters used in the simulation scenarios are listed in Table 2. Also, the tracking loop configuration is summarized in Table 3. It can be seen from Figure 2 that the steering vector has the form  
\[\mathbf{a}(\theta)=[1,\mathrm{e}^{-|2\pi d\sin\theta/\lambda},\ldots,\mathrm{e }^{-|2\pi d(N-1)\sin\theta/\lambda}]^{\mathrm{T}} \tag{28}\]  
where \(d\) is the array spacing, \(\theta\) is the DOA of the incident signal, and \(\lambda\) is the carrier wavelength.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Steps** & **Costs** \\ \hline PI & \(\mathbf{R}_{xx}=(1/K)\mathbf{X}\mathbf{H}\) & \(\mathcal{O}(N^{2}K)\) \\  & \(\mathbf{w}=\mathbf{R}_{xx}^{-1}\mathbf{b}/(\mathbf{b}^{\mathrm{H}}\mathbf{R}_ {xx}^{-1}\mathbf{b})\) & \(\mathcal{O}(N^{3}+2N^{2}+N)\) \\  & \(\mathbf{y}=\mathbf{w}^{\mathrm{T}}\mathbf{x}\) & \(\mathcal{O}(NK)\) \\  & \(r_{\bullet}=(1/K)\mathbf{y}\mathbf{H}\) & \(\mathcal{O}(3 KM)\) \\ MVDR-like & \(\mathbf{R}_{xx}=(1/K)\mathbf{X}\mathbf{x}^{\mathrm{H}}\) & \(\mathcal{O}(N^{2}K)\) \\  & \(\overline{\mathbf{X}}=\mathbf{R}_{xx}^{-1}\mathbf{x}\) & \(\mathcal{O}(N^{3}+N^{2}K)\) \\  & \(\mathbf{r}_{\bullet}=(1/K)\overline{\mathbf{X}}^{\mathrm{H}}\) & \(\mathcal{O}(3 NKM)\) \\  & \(r_{\bullet}=(\overline{\mathbf{x}}_{0}^{\
## 3 Data
### Data Setting for NMF  
This study focuses on the demographic trend change in local regions with rather small populations, shown in Figure 5. In order to evenly deal with the prefectures regardless of their population size, the foreign cohorts' data (5-year age groups) is aggregated into the ratio of cohorts shifted between two cross sections for both genders as shown in Equation (8):  
\[x_{m,f}^{2015}=\frac{N_{m,f}(2015)}{N_{m,f}(2010)}\quad,\qquad x_{m,f}^{2020}= \frac{N_{m,f}(2020)}{N_{m,f}(2015)} \tag{8}\]  
where \(x_{m,f}\) are the above ratios, and \(N\) is the number of foreign residents in each cohort for 47 prefectures.  
If the ratio is calculated for the same age-group record in a couple of datasets observed in the different cross-sections for each, the ratios do not indicate the transition of the same cohort. Therefore, we applied cohort shifting to keep the consistency of the same cohorts among the different cross sections. For example, a cohort in 2010 (ex., 15-19) is recorded as the cohort in 2015 with 5-years' upward shifting (ex., 20-24) because of the observation interval. It is worth noting that if a cohort in 2010 simply remains in the same region in 2015, the cohort growth will be 1. The ratios with cohort shifting can detect the net migration during the interval. Note that the shifting cannot apply to the newborn cohort (0-4), so then it is excluded from the NMF analysis. Moreover, the ratio of cohort shifting between two cross sections (later we will refer this term as cohort transition) is always positive, so then the dataset made by Equation (8) can be analyzed with NMF. Table 1 shows the aggregated dataset of the cohort transition calculated based on Equation (8). The color scales show the deviation from the average cohort transition of the prefectures for each cohort's groups. Blue color represents the decreasing deviation, and red color represents the increasing deviation from the average cohort transition. According to Table 1, we can observe some prefectures attracting or losing the foreign cohorts. For example, from 2010 to 2015, Akita and Tokushima seriously lost the female children's cohorts, and this trend changes from 2015 to 2020. In Oita, the cohort transitions of both male and female younger age cohorts were above the average in 2015, and this trend continues in 2020.  
Figure 5: Share of Foreign Residents in Domestic Population (a) 2010 (b) 2015 (c) 2020.
# Feasibility of Non-Remanufactured Waste Bottle Glass as Supplementary Cementitious Material
## 2 Methodology
### Mortar Study
#### 2.2.3 The mechanical Strength
In brief, the ASR linear expansion procedure is as follows: three prismatic specimens (25 \(\times\) 25 \(\times\) 285 mm\({}^{3}\), see Figure 9) were produced for each mortar type (Table 4) and demoulded from steel moulds after 24 h. Afterwards, specimens were submitted to accelerated curing in a water bath at 80 \({}^{\circ}\)C for 24 h. At that time, specimen lengths were assessed using a digital indicator with an accuracy of 0.001 mm. This length was considered the initial length of each specimen (L\({}_{0}\)). Then, the mortar specimens were introduced into a NaOH solution and linear length was measured at pre-defined ages up to 28 days. research [15; 20; 21; 22; 24; 25; 41]. However, the waste glass powder employed in the current work has particularities compared with the literature survey. The chemical composition is slightly different, as discussed in Section 2.1.4, and some contaminants seem to affect the mortar's mechanical behaviour. The presence of fibre contaminants (as presented in Section 2.1.2 and Figure 3) reduced size during the wet milling process (see Figure 4), but significantly interfered with mechanical strength, compromising the use of these binary binders for structural applications. Figure 11 shows the mechanical strength results of incinerated waste glass-modified mortars (WG700). As can be perceived, the incinerated WG as a cement replacement resulted in a similar performance of the REF mortar both in flexure and compression. The compressive strength was 51.5 and 49.9 Mpa for the REF and WG700 mortars, respectively. This resulted in an SAI of 96.9% at 28 days, which indicated that the milling treatment (Section 2.1.2) was insufficient to obtain a glass powder without impurities (namely, fibres). However, incineration treatment of this particular waste may be an option. Considerations of cost and technical viability must be reflected and balanced. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Age (Days)** & **WG10** & **AGWG10** \\ \hline
7 & 42.2\% & 35.2\% \\
28 & **43.1\%** & **35.2\%** \\
54 & 41.7\% & 37.5\% \\
86 & 41.9\% & 38.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SAI for the WG10 and AGWG10 mortars at 7, 28, 54, and 86 days. Figure 10: Mechanical strength at 2, 7, 54, and 86 days: (**a**) flexure; (**b**) compressive.
# Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques
## 7 Experiments and Results Obtained  
As explained, we applied the rule induction PART algorithm combined with the Bagging approach of homogeneous classifiers. The experiments were implemented in the popular Weka suite [50], keeping its standard default parameter configuration. We used the set of 18,899 images downloaded from the web as training examples, manually classified by the experts as explained in our previous conference paper [9]. From this initial dataset, a combination of rules was extracted with the PART algorithm that can be extrapolated to classify new images.  
Some examples of the individual rules automatically obtained are:  
IF (number-of-primary-rectangles \(\leq\) 0) AND (number-of-segments \(\leq\) 11)  
THEN Negative (it is not a diagram)  
This rule classified 9,445 of our 18,899 examples as non-diagrams,  
with 55 false negatives (0.6%).  
IF (number-of-primary-rectangles \(>\) 2) AND (number-of-hues \(\leq\) 6)  
THEN Positive (it is a diagram)  
This rule classified 176 examples out of 18,899 as diagrams,  
with 7 false positives (4%).  
Figure 12: Inference stage: emulating the expert’s classification through machine learning.  
Figure 13: Application stage: classifying new images with the generated rule-based classifier.  
As explained, the algorithm provides not only individual rules, but also a function that combines the rules and maximizes the overall results. We performed 10 experiments with the available dataset. In each experiment we used the 794 positive instances (manually classified as UML static diagrams, see our previous conference paper [9]) together with other 794 negative instances (other non-UML static diagrams and non-diagrammatic pictures), randomly chosen, without replacement, from the total set of negative instances. We performed the usual 10-fold cross validation [51], obtaining the classifiers shown in Table 3.  
For the purpose of comparison, we repeated the experiments with the neural networks approach, i.e., using the same Bagging method of homogeneous classifiers, this time on a multilayer perceptron (the Weka tool has default options to automatically build the network). The results are shown in Table 4. As can be observed, there is no statistically significant difference between both approaches, that is, both methods are completely equivalent regarding the effectiveness of the classification.
## 3 Factors Influencing U-WSN Design
### Security and Regulation  
Security and regulation of the data collected from a U-WSN, either while stored inside the UAV or during their transmission from the UAV to a gateway, is a major unsolved concern. Furthermore, as thousands of businesses could receive clearance to fly drones in the near future, UAVs will face with various security issues such as loss of data, authentication,access control, and intrusion attacks. In U-WSNs, security solutions are required for data confidentiality, authentication and integrity at low cost.  
Moreover, there are still many regulation issues to be resolved in the coming research. Indeed, there are many requirements from the governments to regulate where the drones fly and what is done with the torrents of data collected from aerial surveillance [59]. In fact, numerous civilian drone manufacturers claim that the main barrier in U.S. and Europe is not the technology, but the regulation. For instance, the U.S. FAA (Federal Aviation Authority) provide some basic regulations and restrictions of the airspace that will govern who can fly drones in the United States and under what conditions. A standard that is formulated by 2016 will permit unmanned aircraft systems (UAS) to interoperate with manned aircraft using an \"electronic means\" to see and avoid potential aerial disasters. Due to the UAV is flying in the same national airspace, it is crucial for the world to comply with a same safety regulations and restrictions.
###### Abstract  
We analyzed the influence of exercise order using the bench press and squat as the first or second exercise of the session on velocity performance. Ten male trained individuals (20.9 \(\pm\) 0.7 years) randomly performed two protocols of three sets of six repetitions at 80% of their one-repetition maximum with different exercise sequences: the bench press followed by the squat (BP + S) and the squat followed by the bench press (S + BP). A linear velocity transducer attached to the Smith machine barbell measured the mean propulsive velocity (MPV), peak velocity (PV), and time to peak velocity. Additionally, blood lactate and heart rate were measured. Regarding the bench press, differences were found in the MPV in the first (BP + S: 0.50 \(\pm\) 0.07 m\(\cdot\)s\({}^{-1}\) vs. S + BP: 0.42 \(\pm\) 0.08 m\(\cdot\)s\({}^{-1}\); \(p\) = 0.03, g = 0.72) and second sets (0.50 \(\pm\) 0.06 m\(\cdot\)s\({}^{-1}\) vs. 0.42 \(\pm\) 0.07 m\(\cdot\)s\({}^{-1}\); \(p\) = 0.03, g = 0.73), and in the PV in the second set (0.74 \(\pm\) 0.09 m\(\cdot\)s\({}^{-1}\) vs. 0.63 \(\pm\) 0.09 m\(\cdot\)s\({}^{-1}\); \(p\) = 0.02, g = 0.86). Regarding the squat, although the S + BP sequence tended to show higher velocities, no significant differences were found between protocols. These results showed that squatting first decreased subsequent bench press velocity performance. On the other hand, squat velocity performance was not impaired when preceded by the bench press.  
strength; mechanical variables; exercise sequence; monitoring; performance
## 5 Computational Results and Discussion
Table 9 shows that there are differences between \"previous\" and all the other approaches for the set of tested instances. The \(p\)-values obtained for both the Friedmann-aligned ranks and Quade tests indicate very strong evidence against the null hypothesis that the results using previous results and the other three approaches have the same median (see Table 10) for solving job-shop scheduling problems. Therefore, there are at least two solution sets that have different medians of the results. The results from comparing the racing, CBR and racing + CBR approaches against previous results, which acts as a control group,  
Since for each sample the normality was not rejected (see Table 9), for the pairwise comparison, the paired-sample \(t\)-test was used. The results depicted in Table 13 shows, at a 5% significance level, that \(\eta_{Previous}>\eta_{CBR}>\eta_{Racing+CBR}\) and \(\eta_{Previous}>\eta_{Racing}>\eta_{Racing+CBR}\). In conclusion, there is evidence, with 95% certainty, that:  
* The median results using previous are larger than the medians of the results obtained from CBR; racing; racing + CBR. Therefore all of the approaches CBR, racing and racing + CBR present improvements when compared to \"previous\". * There are no significant differences between CBR and racing, so using one of those techniques improves the results obtained with previous, but there are no differences between the performances of CBR and racing approaches. * Combining CBR and racing into a new approach
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 4 Simulation and Results
### Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speed Stage** & **Speed** & **CEL-OLSR** & **PM-OLSR** & **ML-OLSR** & **LD-OLSR** & **OLSR** \\ \hline \multirow{2}{*}{Low speed stage: 10 m/s\(\sim\)20 m/s} & 10 m/s & 8.0115 ms & 10.4237 ms & 14.4682 ms & 16.9842 ms & 24.8149 ms \\  & 15 m/s & 7.1125 ms & 11.3785 ms & 15.3781 ms & 17.3932 ms & 25.2993 ms \\  & 20 m/s & 7.4568 ms & 11.9879 ms & 15.8749 ms & 18.8631 ms & 26.9474 ms \\ \hline \multirow{3}{*}{Mid low-speed stage: 25 m/s\(\sim\)35 m/s} & 25 m/s & 8.0299 ms & 12.6382 ms & 16.5078 ms & 19.4923 ms & 27.7827 ms \\  & 30 m/s & 8.0965 ms & 13.0215 ms & 16.8876 ms & 20.8086 ms & 32.3468 ms \\  & 35 m/s & 9.9706 ms & 14.7824 ms & 18.2349 ms & 23.7232 ms & 35.1833 ms \\ \hline \multirow{3}{*}{Mid high-speed stage: 40 m/s\(\sim\)50 m/s} & 40 m/s & 9.8539 ms & 15.8246 ms & 19.8537 ms & 25.1414 ms & 40.4587 ms \\  & 45 m/s & 10.4783 ms & 15.5259 ms & 20.8123 ms & 26.3367 ms & 44.1619 ms \\ \cline{1-1}  & 50 m/s & 11.0763 ms & 16.4649 ms & 23.3782 ms & 28.1896 ms & 46.5146 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of average end-to-end delay (AEED) for comparison protocols at different speed stages.
## 2 Related Works and Problem Statement
### Problem Statement  
Considering the formation composed of \(N\) UUSVs, the kinematic and dynamic equations of UUSV \(i\) are described as  
\[\left\{\begin{array}{l}\dot{\textbf{{P}}}_{i}=\textbf{{{J}}}_{i}(\theta_{i}) \textbf{{Q}}_{i}\\ \dot{\theta}_{i}=r_{i}\end{array}\right. \tag{5}\]  
\[\left\{\begin{array}{l}\dot{\textbf{{Q}}}_{i}=\textbf{{{F}}}_{i}(x_{i},y_{i} )+\textbf{{{\tau}}}_{i}^{\alpha}+\textbf{{{\tau}}}_{i}\\ \dot{r}_{i}=f_{i}(\sigma_{i})+\frac{1}{m_{i}^{\alpha}}\textbf{{{\tau}}}_{i}^{ \alpha\alpha}+\frac{1}{m_{i}^{\alpha}}\textbf{{{\tau}}}_{i}^{\alpha\tau}\end{array}\right. \tag{6}\]  
where \(i=1,2,\ldots,N\) represents the identification number of the UUSV, \(\textbf{{{P}}}_{i}=[x_{i},y_{i}]^{\text{T}}\) is the position vector of the UUSV in the earth coordinate system, \((x_{i},y_{i})\) are the position coordinates; \(\textbf{{{J}}}_{i}(\theta_{i})=\begin{bmatrix}\cos\theta_{i}&-\sin\theta_{i} \\ \sin\theta_{i}&\cos\theta_{i}\end{bmatrix}\) is the rotation matrix related to the yaw angle, \(\theta_{i}\) is the yaw angle; \(\textbf{{{Q}}}_{i}=[u_{i},\
u_{i}]^{\text{T}}\) is the velocity vector in the hull coordinate system, with \(u_{i}\) and \(v_{i}\) representing the forward and lateral drift velocities, respectively, and \(r_{i}\) representing the yaw angular velocity of the UUSV. \(\textbf{{{F}}}_{i}(x_{i},y_{i})=[f_{i}(x_{i}),f_{i}(y_{i})]^{\text{T}}\) and \(f_{i}(\sigma_{i})\) denote the unknown unmodeled dynamics. \(\textbf{{{\tau}}}_{i}^{\alpha\
u}=\begin{bmatrix}\frac{1}{m_{i}^{\alpha\
u}} \textbf{{{\tau}}}_{i},\frac{1}{m_{i}^{\alpha\
u}}\textbf{{{\tau}}}_{i}^{\alpha \
u Y}\end{bmatrix}^{\text{T}}\) and \(\textbf{{{\tau}}}_{i}^{\alpha\
u}\) are the external disturbances of the UUSV, and \(\textbf{{{\tau}}}_{i}=\begin{bmatrix}\frac{1}{m_{i}^{\text{T}}}\textbf{{{\tau} }}_{i}^{X},0\end{bmatrix}\) and \(\textbf{{{\tau}}}_{i}^{\alpha}\) are the control inputs of the UUSV.  
Since the above model is underactuated, \(U_{i}=\sqrt{u_{i}^{2}+v_{i}^{2}}\) is further defined as the resultant velocity, \(u_{i}=U_{i}\cos\varphi_{i}\), \(\varphi_{i}\) is the sideslip angle, and \(\varphi_{i}=\arctan\left(\frac{v_{i}}{u_{i}}\right)\), so the above dynamic model can be simplified as  
\[\left\{\begin{array}{l}\dot{\textbf{{P}}}_{i}=U_{i}\textbf{{{R}}}_{i}(\theta _{i})\\ \dot{\theta
## 4 Results and Discussion  
### Nanoparticle Characterization by DLS, XRD, and SEM Analysis  
Figure 3 shows the size distribution (i.e., intensity distribution) of the TiO\({}_{2}\) NPs obtained in the DLS measurements. The average NP diameter was 46 nm. The crystal structure of these NPs was identified as rutile phase from the XRD pattern (Figure 4). The strong diffraction peaks at 27\({}^{\circ}\), 36\({}^{\circ}\), and 55\({}^{\circ}\) correspond to the (1 1 0), (1 0 1), and (2 1 1) planes, respectively. All peaks are consistent with the standard spectrum of rutile TiO\({}_{2}\) (ICSD 98-016-8138), indicating that no important impurities were present. The spherical shape of the NPs was confirmed by the SEM image (see Figure 5).  
Figure 4: X-ray diffraction pattern of rutile TiO\({}_{2}\).  
Figure 5: Scanning electronic microscopy (SEM) image of the TiO\({}_{2}\) NPs.  
Figure 3: Particle size distribution of the TiO\({}_{2}\) nanoparticles (NPs), obtained from the dynamic light scattering (DLS) analysis.
## 3 Results and Discussion
### Mechanical Performance of Geopolymers  
The compressive strength determination results for geopolymers after 28 days of curing are presented in Figure 8. The result for the reference mortar RCM is also provided for comparative purposes. The highest compressive strength was demonstrated by G10 WG specimens, containing 10% of the waste broken glass. The G10 WG geopolymer's strength was 9% higher than the RCM. The compressive strength of G20 WG geopolymer with 20% WG content was 32.57 MPa, which is 85% of the value for the RCM. However, the obtained strength is still above the minimum standard value for the cements of the strength class 32.5 (\(\mathrm{f_{c,\,min}}\) = 32.5 MPa) according to the European Standard EN 197-1 [45]. The compressive strength of GW0 WG and GW30 WG was 45% and 50% of the value for the RCM, respectively.  
Figure 9 presents the results of geopolymers' flexural strength testing compared to the reference mortar RCM.  
Figure 8: Compressive strength of geopolymers and reference mortar RCM after 28 days of curing.  
Figure 7: The average values of thermal diffusivity of the tested geopolymers.  
The highest flexural strength was demonstrated by geopolymers containing 10% and 20% waste glass (G10 WG and G20 WG). Their strength was 105% and 101% RCM, respectively. The G30 WG geopolymer, containing 30% WG, had 87% of the RCM's flexural strength, which is still a good result. The broken glass waste improved the flexural strength of the geopolymers because of the grain's shape and sharp and non-regular edges.
## 3 Application
### Thermochemical Conversion
#### 3.8.2 Pyrolysis  
Pyrolysis refers to the degradation reaction of fuel in an inert atmosphere or limited oxygen supply to generate pyrolysis gas, tar and biomass carbon. The pyrolysis gas is mainly small-molecular gases, such as carbon monoxide, carbon dioxide and hydrogen. Due to the coupling of multiphase flow and complex chemical reaction in pyrolysis, it is difficult to accurately measure multiphase flow in a complex environment. As numerical simulation is relatively cheap, it has gained more guidelines and research and has become one of the main methods to study the pyrolysis process at the reactor scale.  
The complex particle-fluid behavior in the rapid pyrolysis process is an important focus for numerical simulation. [PERSON] et al. used the DPM/DDPM method to describe the motion of discrete phase particles and developed a pyrolysis reaction model for wood [111]. The model's accuracy may be dependent on the assumptions made about the pyrolysis process, and it may not account for all reaction pathways or the influence of environmental factors. In order to better understand the complex fluid-particle reaction, [PERSON] et al. conducted cross-scale modeling in coal pyrolysis and established a comprehensive computational fluid dynamics model based on a discrete phase model, especially considering particle scale physics, such as heat conduction inside particles [112]. The cross-scale modeling approach may face challenges in accurately representing the interactions between different length scales, potentially leading to discrepancies in the simulation results. Moreover, combined with the improved chemical percolation devolatilization (CPD) model, the particle heating and decomposition behavior within a millisecond residence time was described. It was found that particle heating and decomposition are strongly affected by the temperature grade and residence time of coal particles in the high-temperature zone [113].  
In some cases, pyrolysis will be displayed in the form of a certain link in the thermochemical process. For example, the ignition source releases enough energy to trigger particle pyrolysis before an explosion. [PERSON] et al. developed a simulation based on the coupled Euler-Lagrange formula, mainly including powder dispersion and pyrolysis/gas oxidation reaction, to simulate the whole process behavior of a wheat starch/pyrolysis gas mixture explosion in the sphere [114,115]. It was found that the pyrolysis/gas oxidation reaction has a comprehensive dissipation effect on turbulence. The pyrolysis process is also used as a pre-step of the gasification or combustion processor as the input of volatile gases [116,103].  
Since the research on reaction kinetics in the pyrolysis process has not reached the stage of maturity [117], CFD-DPM has relatively few simulations in the pyrolysis process and cannot be applied to industrial-scale reactors. However, the more detailed information of particle size provided by numerical simulation can help us further understand the finer fluid motion and the complex behavior at the particle level in the pyrolysis process.
# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 4 Ethical Considerations
### Non-Maleficence: Privacy, Security and \"Capability Caution\"
#### 4.2.3 Data Quality  
Real-world disease data tends to be noisy and incomplete. Although reporting of most notifiable diseases through various public health agencies is required by law, for the most part hospitals, laboratories, and clinicians participate voluntarily. This reinforces the need for careful evaluation of data sources and collection procedures. Moreover, event-based surveillance systems can generate a sizable amount of information on any given outbreak topic, sometimes overwhelming the systems themselves [21]. For example, the Argus system generated approximately 22,000 reports on the pandemic (H1N1) 2009 from April 2009 to March 2010 [20]. In stark contrast, the COVD-19 case reports captured early in the pandemic tallied 49,659 daily cases and 2739 deaths for 27 March 2020 only [17]. Given the serious consequences of using poor quality data to manage global health related decisions, improving data quality becomes a crucial ethical concern [63].