esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field
## 3 Experiments and Results
### Analysis and Comparison of Experimental Results
#### 3.2.2 Reconstruction Time  
Time efficiency is a critical factor in UAV image reconstruction. In the conventional reconstruction process, dense reconstruction estimates the depth of each pixel, resulting in significant time consumption. In contrast, the neural radiance field method bypasses this step, significantly reducing the reconstruction time. Determining the completion of model training based on a specific time is infeasible for neural network training. The duration of scene reconstruction using the neural radiance field primarily depends on the speed of neural network training. Typically, a predefined number of iterations is employed to terminate the neural network training process. In this study, the reconstruction time is defined as the duration required for 60,000 iterations. Following experimental analysis, the training duration of the neural network is constrained to 30 min.  
Table 3 clearly demonstrates that the neural radiance field method exhibits significantly lower time requirements compared to the classical method. With an increasing number of images, the time cost of both methods increases accordingly, albeit with a minimal increase observed in the neural radiance field method.  
Figure 7: Comparison of details of reconstruction results by different methods.
## 3 Autostereoscopy-Raman Spectrometry-Based Measurement System  
### System Design and Optical Layout  
Based on the proposed Autostereoscopy-Raman Spectrometry-based measurement methodology, an ARS 3D measurement system is developed accordingly.  
The presented micro Raman spectrometer based on light field imaging is built. Figure 3 shows our design of optical layout. The Kohler lighting module illuminates the object, and the object is imaged through the microscopic objective (MO, NA = 0.4, 20\(\times\)), and spectral information is collected by Raman scattering. The tube lens (TL) imaged the object once,  
Figure 2: Schematic diagram of cross validation between the sharpness function signal of autostereoscopic method and Raman spectrum signal.  
and the micro lens array (MLA) imaged the object twice. The 750 nm shortpass dichroic mirror (BS1) separates visible light and Raman signals. MLA forms a relay imaging system with the microscopic system. The position of each micro lens satisfies the imaging formula:  
\[1/u+1/v=1/f, \tag{4}\]  
where \(u\), \(v\) and \(f\) are the distance from the micro lens to the image plane of microscopic system, the distance from the micro lens to the sensor and the focal length of the micro lens respectively. In our setup, \(v\) is greater than \(f\). In order to prevent the light field image from overlapping, we need the F number of the objective to match the F number of the micro lens array. The F number \(F_{n}\) of the objective can be calculated by this formula [30]:  
\[F_{n}\approx M/2 NA, \tag{5}\]  
where \(M\) is the magnification of the objective. In order to ensure that there would be no overlapping, we chose MLA with a smaller F number, with \(pitch=300\)\(\upmu\)m and \(f=5\) mm. The image sensor is a monochrome CCD with a pixel size of 3.45 microns. The 785 nm beam generated by the semiconductor laser is purified by the 785 nm laser clean-up filter (EX) and reflected by the ultra-steep 785 nm laser dichroic mirror (BS2) into the objective. The objective focuses the laser on the sample to stimulate Raman scattering. Meanwhile, the scattering signal is collected by the objective. After being filtered by a 792 nm longpass emission filter (EM), the signal is coupled to the fiber with NA = 0.22 and core diameter of 600 \(\upmu\)m by coupling objective (CO) and finally transmitted to the Maya2000 Pro High-Sensitivity Spectrometer (Ocean Insight Co., Shanghai, China). Greater detection depth can be obtained by changing the small magnification objective.  
Figure 3: Schematic of the Autostereoscopy-Raman Spectrometry-based three-dimensional metrology system.
## 3 Numerical Simulation Results and Discussions
### Construction and Validation of Circulating Air Mixing Ratio Model
Therefore, the weighted average value of the effective areas covered by the air velocities at the measuring points was the average air velocity u of the cross section of the tunnel. In other words, \(u=\frac{\Sigma V_{\mathrm{j}}\Delta F_{\mathrm{j}}}{\Sigma F}\), where \(\Delta F_{\mathrm{j}}\) and \(V_{\mathrm{j}}\) are the area of each part and the air velocity of the measuring point within each area. Obtaining the CO\({}_{2}\) concentration of the inlet and outlet tunnel by using CO\({}_{2}\) concentration transmitters, the arrangements of concentration measurement points are shown in the Figure 15(b). As for #1 tunnel, H = 1.5 m, X = 7.0 m; as for #2 tunnel, H =1.8 m, X = 5.5 m. The arithmetic mean value of the CO\({}_{2}\) concentrations at these measuring points is the average concentration of the cross section of the tunnel. Based on the measured air velocity and CO\({}_{2}\) concentration, \(\varphi_{\mathrm{c}}\) can be calculated according to the following formula:  
\[\varphi_{c}=\frac{C_{\mathrm{in}}-C_{0}}{C_{\mathrm{out}}-C_{0}}\frac{u_{ \mathrm{in}}}{u_{\mathrm{out}}} \tag{6}\]  
In the formula, \(C_{\mathrm{in}}\) and \(C_{\mathrm{out}}\) is the CO\({}_{2}\) concentration of the cross section of the inlet and outlet tunnels, respectively; \(C_{0}\) refers to the local concentration of CO\({}_{2}\). Meanwhile, \(\varphi_{\mathrm{c}}\) under different \(u_{\mathrm{in}}/u_{\mathrm{out}}\) were predicted according to the relation model established in this paper as well as the model proposed by [PERSON] (2013). The prediction and test results are shown in Figure 17. It shows that, calculated by measured data, \(\varphi_{\mathrm{c}}\) gradually increases with the increasing of \(u_{\mathrm{in}}/u_{\mathrm{out}}\). The variation tendency of the \(\varphi_{\mathrm{c}}\) value predicted by [PERSON]'s formula agrees with the measurement; however, the predicted \(\varphi_{\mathrm{c}}\) value of #1 twin tunnels with staggered inlet and outlet is generally smaller than the measured value, and the predicted \(\varphi_{\mathrm{c}}\) value of #2 twin tunnels with aligned inlet and outlet air tunnel portals is generally bigger than the measured value. Hence, it can be concluded that [PERSON]'s formula is only applicable to tunnels the entrance slope gradient of which is 90\({}^{\circ}\) and have aligned inlet and outlet air tunnel portals, not to tunnels with staggered inlet and outlet or with relatively small entrance slope gradients. Unlike [PERSON]'s formula, the \(\varphi_{\mathrm{c}}\) value of #1 twin tunnels with staggered inlet and outlet predicted by the model built in this paper agrees with the variance tendency of the measured value, showing a good numerical match, while the maximum relative error is only 7.2%. Compared with [PERSON]'s formula, the \(\varphi_{\mathrm{c}}\) value of #2 twin tunnels with staggered inlet and outlet predicted by this model is closer to the actual measurement. Figure 16: Distribution of velocity measuring points.
## 3 Results  
The surface topography of the two specimens (A1 and A2) with 0.1 mm and the two specimens (B1 and B2) with 0.3 mm of print layer thickness was assessed (1) after printing without any surface finishing (before polishing assessment) and (2) after polishing with SiC sandpaper (Figure 5).  
Figure 4: (**a**) Stylus probe profilometer (Hommelwerke GmbH, Germany). (**b**) Support for fixing the specimens for the surface topography reading.  
Figure 5: Surface topography of the specimens without any surface finishing (**a**–**c**) and after polishing with SiC sandpaper (**d**–**g**).  
In the first analysis, specimen B1's roughness was higher than the profilometer tip's measurement range (approximately 300 \(\upmu\)m), making it impossible to measure. A considerable difference was observed in the surface roughness of the specimens in the two evaluation moments, and, as expected, all specimens showed higher surface roughness in the first measurement (Table 2). After the polishing procedures, Ra, Sa, and St decreased significantly in each specimen.  
Regarding the comparison between specimens with the same print layer thickness, it was not possible to compare B1 and B2 before polishing. However, all other comparisons (A1 with A2, before and after polishing, and B1 with B2 after polishing) showed very close values in the evaluated parameters, both before and after final polishing (Table 2).  
Because the specimens differed in their printing characteristics--specifically, the print layer thickness--they were grouped according to this characteristic to calculate average roughness parameters (Table 3). Comparing the average values of Ra, Sa, and St of the two  
Regarding the PMMA specimens (P1, P2, and P3), surface topography was carried out on all specimens (Figure 7), and the parameters Sa, Ra, and St were assessed. A considerable difference was observed in the surface roughness between the unpolished and polished specimens. However, the specimens' surface roughness was similar in the different polishing steps applied, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**P1**} & \multicolumn{2}{c}{**P2**} & \multicolumn{1}{c}{**P3**} \\
**Specimens** & **(Unfinished** & **Polished with SiC** & **(Conventional** \\  & **Specimen)** & **Sandpaper)** & **Finish)** \\ \hline Roughness & Sa & 0.74 & 0.4 & 0.49 \\ Roughness & Ra & 0.26 & 0.11 & 0.11 \\ Parameters & St & 9.75 & 7.68 & 6.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Roughness test results for each PMMA specimen—Ra, Sa, and St.  
Figure 7: Three-dimensional image of the surface roughness of PMMA specimens P1, P2, and P3.  
When comparing the PEEK specimens with the PMMA specimens, although the PEEK specimens had higher roughness before polishing, after polishing with SiC sandpaper, the PEEK specimens had equal or lower roughness than the PMMA specimen under the same conditions (Table 5).
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Metrics in Human Mobility Data Network  
With the technology currently available, it is possible to combine devices with heterogeneous systems, such as smartphones with mobile networks (3G/4G and, in the future, 5G), Bluetooth devices, wireless networks, sensors, among others, to disseminate human movement data and its analysis. These data holds an immense promise for understanding human behavior on a vast scale, and with a precision and accuracy never before possible with censuses, surveys, or other existing data collection techniques. However, it is needed to discuss challenges with existing data sources and describe metrics for measuring human mobility.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 4 Experimental Setup
### Compared Methods
#### 4.1.1 Shallow Baseline Models  
1. \(OC\)-\(SVM\)/\(SVDD\) as per the formulation in [6]. When the Gaussian kernel function is used, these two methods are equivalent and are asymptotically consistent density-level set estimators. For the OC-SVM/SVDD models, the kernel size is the reciprocal of the number of features, and the fraction of outliers \(v\in(0,\ 1)\) is set according to the obtained outlier proportions.
2. _Isolation Forest (IF)_ as per the formulation in [27]. The amount of contamination is set according to the proportion of outliers in the dataset, and the number of base estimators in the ensemble is 100, as recommended in [24].
3. _Kernel Density Estimation (KDE)_ as per the formulation in [28]. The bandwidth of the Gaussian kernel is selected from \(h\in\left\{2^{0.5},\ 2^{1},\ \ldots,\ 2^{5}\right\}\) using the log-likelihood score, and the best result is reported.
## 3 Results and Discussion
### Power Consumption Results
At the end of the second phase, the power increased again and varied between 10.0 kW and 18 kW for the last 80 min (Figure 13).
# Sustainability in Bio-Based Edible Films, Coatings, and Packaging for Small Fruits
## 5 Edible Film and Coating Functionalities Important for the Small Fruit Industry
### Bioactivity of the Edible Films and Coatings  
Bioactive compounds incorporated into edible coatings used to protect small fruits can include diverse compounds (Table 2), such as colorants, flavors, nutrients, and compounds with antimicrobial or antioxidant properties [119].  
Researchers are increasingly interested in using natural sources of antioxidants and antimicrobials, such as natural extracts [121]. This is due to consumer expectations, their health concerns about using synthetic antioxidants or antibiotics, and the broadly understood concern for the natural environment [122; 123; 124; 125]. An additional challenge for innovation in edible coatings may be the introduction of natural substances with targeted health-promoting effects, e.g., antihypertensive, anti-inflammatory, or anticancer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fruit** & **Coating/Film Compounds** & **Activity** & **Reference** \\ \hline Blackberries & chitosan, lactic acid & anti-mold & [126; 127] \\ \hline  & chitosan and alginate-bases with inulin, oligofructose, and apple or & antioxidative & \\  & orange fibers & anti-fungal & [128] \\ \cline{2-4} Blueberry & chitosan and quinoa proteins with thymol oil & antimicrobial & [129] \\ \cline{2-4}  & chitosan enriched with blueberry leaf extract & antimicrobial & [130] \\ \hline Blue honeystckle & chitosan and Aloe vera gel & antioxidative & [131] \\ \hline Highbush blueberry & starch and gelatin bases with cinnamon oil & antioxidative & [132] \\ \hline  & Aloe vera gel & antioxidative & [133] \\ \cline{2-4} Grapes & Brazilian pine seeds starch, citric pectin, and feijoa fruit extracts & antimicrobial & [134] \\ \hline Raspberries & zein, Argentinian propolis extracts & antimicrobial & [135] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The antioxidative and antimicrobial activities of edible coatings/films used in perishable fruits.
## 4 Discussion  
This study addresses whether _D. polymorpha_ and _A. anatina_ could be used to reveal the presence of MC-producing cyanobacteria in freshwater. In addition, we studied which MC analysis, the free or the total (free and protein-bound) fraction, in mussels' tissues is more suitable to monitor MC-producing cyanobacteria. To achieve this, both species were exposed to a MC-producing strain of _P. agardhii_ for 21 days followed by 21 days of deprivation, and kinetics of free and total MC were studied in their tissues (i.e., in the digestive gland of the larger species _A. anatina_ and in the entire body of _D. polymorpha_, representing whole mussels). Bound MC were detected at the first day of exposure in _D. polymorpha_ exposed to 10 and 100 ug MC/L and in _A. anatina_ whatever the exposure concentration. This means that MC binding to intracellular proteins may have occurred in less than 24 h, as observed with purified proteins [12; 56; 57]. Free MC were detected within the first 24 h of exposure in digestive glands of _A. anatina_ and from day 3 of exposure in whole _D. polymorpha_. This delayed detection of MC in whole individuals of _D. polymorpha_ could be a result of a biodssolution among all organs, as the digestive gland is known to be MC target organ in invertebrates [37; 38]. Here, MC accumulation tended to be concentration-dependent in whole _D. polymorpha_, for which MC concentration in individuals exposed to 10 ug MC/L was intermediary, between the concentration measured in those exposed to 1 and 100 ug MC/L. However, the differences in MC accumulation between exposure concentrations were not significant for both species and both accumulation fractions. Several hypotheses could explain this observation based on bivalves' physiology. First, a temporary decrease or stoppage of the ingestion of _P. agardhii_ may have occurred during the exposure, but this hypothesis is not supported by the data. Indeed, both species continuously ingested the MC-producing cyanobacteria during the three-week exposure, and _D. polymorpha_ even increased it consumption over time, demonstrating that the mussels do not seem to have reacted to the toxicity by closing their shells. Similarly, filtration rates of _Limnoperna fortunei_ were not significantly different for toxic and non-toxic _M. aeruginosa_ throughout a laboratory exposure [58]. Moreover, in our study, mussels exposed to 100 ug MC/L ingested more filaments compared to those exposed to 10 ug MC/L. However, short-term punctual changes in the filtration rates may be part of the variations of MC concentrations in tissues over time. Indeed, it is known that the filtration is not a constant phenomenon and inter-individual variations are high [59].
## 1 Introduction  
Endodontic retreatment becomes necessary when a previously treated tooth shows a persistent or recurrent infection. The success of retreatment depends on the removal of root canal materials and the eradication of bacteria that led to the treatment failure [1, 2].  
In recent years, there has been a significant increase in the clinical use of latest-generation premixed calcium silicate-based sealers, due to their ease of use, promising characteristics [3] and clinical advantages [4].  
Traditionally limited to cold endodontic techniques, such as single-cone, these premixed bioceramic sealers are now being successfully applied in warm techniques as well, as demonstrated by recent in vivo clinical research [5, 6, 7, 8].  
Given the increased use of these sealers, there will be a higher possibility of coming across teeth that need to be retreated. The literature presents conflicting findings regarding their potential to occlude the apical foramen, compromise patency and make retreatment procedures more difficult and time consuming [9]. On the other hands, some studies assert that premixed bioceramic sealers are easier to remove compared to epoxy resin-basedsealers [10; 11]. Further investigations are needed to elucidate the material properties and clinical implications of premixed bioceramic sealers in endodontic therapy. NeoSealer Flo (Avalon Nusmile, Houston, TX, USA) is a premixed bioceramic-based resin-free sealer composed of tantalite (50%), tricalcium silicate (25%), calcium aluminate (25%), dicalcium silicate (10%), tricalcium aluminate (5%), and calcium sulphate (1%). Its chemical and physical properties have been described by in vitro studies, showing moderate calcium ion release, alkalizing activity and some apatite-nucleating and calcium carbonate-forming abilities after immersion in simulated body fluids [12].  
Currently, only in vitro Micro-CT or Scanning Electron Microscopy (SEM) investigations have been used to assess the ability to retreat teeth filled with premixed bioceramic sealers with different techniques [13; 14; 15; 16]. However, even if Micro-CT is considered the benchmark for assessing remnants and the three-dimensional configuration of canals, its application is restricted to laboratory research, as Micro-CT cannot be utilized in clinical in vivo settings.  
The primary aim of this study is to assess whether the retreatment of premixed bioceramic sealer in oval-shaped teeth allows for the re-establishment of apical patency. The null hypothesis is that there is no significant difference in retreatment outcomes between cold and warm obturation techniques. A detailed investigation using a specialized software on digital images from periapical radiographs (phosphor plates) and cone beam computed tomography (CBCT) scans was used to evaluate this hypothesis.
## 5 Multiple Scatterer DRFM  
### Multiple Scatterer Targets  
As the inevitable cycle of innovation in Electronic Countermeasures (ECM) and Electronic Counter-Counter Measures (ECCM) continues, current forms of ECCM, such as non-cooperative target recognition, have limited the effectiveness of the DRFM architectures discussed so far [22]. The primary limitation in conventional DRFM jammers is the inability to accurately replicate the skin return of a target, as only a single signal is retransmitted at one time. In reality, the skin return of a target, such as an aircraft or ship, is the sum of a large number of signals, all with slightly different delays, phases, and Doppler shifts.  
As an example, the RCS of a single cylinder is shown in Figure 17, a set of the same cylinder is then randomly distributed using a Gaussian distribution with a mean of zero and standard deviation of two, shown in Figure 18. Unfortunately, modern radars use capabilities such as NCTR, measuring not just the amplitude and Doppler shift of a single signal, but the combination of the amplitude and phase, including the individual Doppler shift of each scatterer.  
For example, the jet engine of an aircraft can cause an effect on the scattered radar wave known as Jet Engine Modulation (JEM) where an amplitude modulation of the received signal power occurs with each rotational period of the turbine.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Envelope**} & \multicolumn{2}{c}{**Complex**} \\ \hline
**Bandwidth** & \(\Delta t\) **(\(\mu\)s)** & \(G_{p}\) **(dB)** & \(\Delta t\) **(\(\mu\)s)** & \(G_{p}\) **(dB)** & **SLL (dB)** \\ \hline
1 MHz & 7.5 & \(-4\) & \(\approx\)0 & \(\approx\)0 & \(-6\) \\
2 MHz & 8.5 & \(-5\) & \(\approx\)0 & \(\approx\)0 & \(-9\) \\
5 MHz & 9.5 & \(-8\) & \(\approx\)0 & \(\approx\)0 & \(-12\) \\
10 MHz & 10.0 & \(-9\) & \(\approx\)0 & \(\approx\)0 & \(-15\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: LFM signal bandwidth comparison.  
Figure 17: The radar cross section of a cylinder with a 0.5 m radius and height of 1 m.  
The effects of the multiple scatterers and their individual contributions to the received waveform can be expressed as a sum of the signals:  
\[y\left(t\right)=\sum_{n=1}^{N}\alpha_{n}x\left(t-t_{n}\right)e^{-i\omega_{n}t} \tag{5}\]  
where \(\alpha_{n}\) is the amplitude, \(t_{n}\) is the delay, and \(\omega_{n}\) is the Doppler shift. Unfortunately, this system will vary with a variety of factors, such as the angle-of-arrival, frequency, and polarization, allowing the received signal to be expressed as a function of these factors:  
\[y\left(t,\sigma\right)=\sum_{n=1}^{N}\alpha_{n}\left(\sigma\right)x\left(t-t _{n}\left(\sigma\right)\right)e^{-i\omega_{n}\left(\sigma\right)t} \tag{6}\]  
where \(\sigma\) is a set of the following variables:  
\[\sigma=\left\langle\theta,\phi,f,\hat{\rho}\right\rangle \tag{7}\]  
containing the azimuth, \(\theta\), the elevation, \(\phi\), the frequency, \(f\), and the polarization, \(\rho\).