esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Modified Compact Tension Specim (MCTS)
#### 4.1.3 G4 Specimen  
The effect of the crack path as the hole shifts away from the crack front is seen in Figure 9. The 8 mm diameter hole of specimen G4 is located at a distance of h1 = 14.5 mm. The crack path for the G4 specimen began straight, but since the hole was in the path of the crack, the path deviated towards the hole. The predicted paths in both FRANC2D/L and ANSYS were almost identical to the experimental and Franc3D work performed by [32] as shown in Figure 10. The \(K_{\text{I}}\) range in the FRANC2D/L software for specimen G4 is 615 to 1085 MPa.mm\({}^{0.5}\), while it was 485 to 1190 MPa.mm\({}^{0.5}\) for ANSYS as shown in Figure 11. At the same crack length, there is a 1.2% discrepancy in the first mode of SIF values. Two further comparisons were performed as shown in Figure 10,e using the numerical data provided by [29] and the FRANC3D findings [28], which revealed a relatively short crack growth path when compared to the experimental and numerical results in this study. There were also comparable results for \(K_{\text{I}}\) with the numerical results obtained by [29], as shown in Figure 11a. Furthermore, as shown in Figure 11b, for the second mode of stress intensity factors \(K_{\text{II}}\), there are significant values that affect the crack growth path to propagate in a curving direction toward the hole, which were also found ambiguously by [29].  
Figure 10: _Cont._  
Figure 9: Stress intensity factors with corresponding crack length for specimen G3, (**a**) \(K_{\text{I}}\) and (**b**) \(K_{\text{II}}\).
# Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis
## 3 Results
### Waterfall Plots Approach  
The second method evaluated for identifying the impact frequency from MCSA is based on the short-time Fourier transform (STFT), which splits the motor current time signal into short segments of equal length to then compute the FFT separately on each segment. The resulting frequency spectra are displayed in a 3D waterfall plot, which facilitates visualizing the variations of the spectrum over time. Figure 9 shows a waterfall plot for the motor current during the SP stage, considering time segments of 2.66 s. This figure clearly shows the variations in the amplitude of the fundamental component of the frequency spectrum over each time segment. Considering that these variations can reveal the impacts of the DTH hammer over the drilling surface, the next step is to extract the amplitudes of the fundamental component of motor current to create a new data array (I\({}_{m,fund}\)) and then to apply the FFT to the new data array to identify the impact frequency. Figure 10a shows the evolution of the resulting I\({}_{m,fund}\) data array over time. The enlarged view of this data is shown in Figure 10b, where circles represent the amplitudes of the fundamental component captured for each sampled segment. For this analysis, a time segment of 0.02 s is chosen, considering that these windows cover approximately one complete cycle of the fundamental component of 50 Hz (1/0.02 = 50 Hz), which allows us to capture variations on fundamental current on each cycle. The frequency spectrum in Figure 10c confirms that the impact frequency of 14.10 Hz is detected. Notably, the maximum frequency captured in the spectrum in Figure 10c is approximately 24 Hz, corresponding to the Nyquist frequency of I\({}_{m,fund}\).  
Since this method must capture the fundamental cycles of the motor current, which are defined by the rated frequency of the motor, the maximum frequency of the spectrum will naturally be limited to half of this frequency, i.e., 25 Hz for a nominal frequency of 50 Hz  
Figure 8: Motor current signal analysis during stage-z: (**a**) zoom of I\({}_{m}\) signal; (**b**) frequency spectrum of I\({}_{m}\); (**c**) zoom of frequency spectrum of I\({}_{m}\).  
and 30 Hz for a nominal frequency of 60 Hz. This limitation is important, considering that the impact frequency can be within a wide range of values (see Section 1) and can even exceed the fundamental frequency of the stator. Therefore, the application of this method will be restricted to drilling machines with operational impact frequencies lower than the Nyquist frequency. To overcome this limitation, the following method studied in this paper is the enveloping approach.
# Experimental Study of the Effect of Ultrasound on the Freezing Process of Bo Chinh Ginseng
## 4 Conclusions  
The research results show that ultrasound irradiation has a positive effect on the freezing process of ginseng materials. Factors such as broadcast power level (at the same 20 kHz frequency), irradiation temperature and intermittency ratio all affect the freezing process. It can be concluded that it is very important to choose the appropriate ultrasound parameters and conditions to support the material freezing process before sublimation drying. In comparing ultrasound-assisted frozen samples with ultrasound-unassisted frozen samples, the freezing time, nucleation temperature, microstructure and color all have better results at a 100 W (20 kHz) power and ratio interruption rate of 0.6.  
When a higher ultrasound power is continuously transmitted to the material, it does not shorten the freezing time; instead, the larger the power capacity, the less effective it is in helping to shorten the freezing time, and at the same time, it increases the nucleation temperature. The results also show that the nucleation temperature is a function of the irradiation temperature in the studied temperature range from \(-0.5\) to \(-2.5\)\({}^{\circ}\)C. In addition, the intermittency ratio also has a positive effect on shortening the freezing time, as it helps to reduce the heat generation effect of ultrasound irradiation. Ultrasound irradiation also causes many microchannels and holes on the surface of the material, enhancing the process of heat and substance transfer. Moreover, ultrasound irradiation do not change the material color.  
The results show that selecting the appropriate parameters during the ultrasound-assisted freezing process of ginseng root is crucial. Although this study only shows quantitative research results on the influence that ultrasound application has on the freezing process, the results provide useful information for the future application of ultrasound-assisted food freezing.  
Investigation, experimenting and writing, M.-H.N.; editing and approval, H.N. and T.-B.N.; experimenting, T.-D.L.; providing and setting up the experiments, Q.-H.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 3 Development of Height Indicators Using Global Appearance Descriptors  
Our previous works [26; 34] have focused on building a visual model of the environment and estimating the position of the mobile platform when its movement is contained in the ground plane, using the global appearance of the scenes with this aim. However, as stated previously, it is also interesting to study the possibility of estimating the altitude of the vehicle with respect to the plane where it moved when the visual model was created. With this goal, several methods are proposed in this section and analysed in the subsequent sections in order to know the accuracy and advantages of each one.  
In all cases, only visual information will be used to estimate the relative altitude. The two images to compare are named _reference_ and _test_ image (\(im_{R}\) and \(im_{T}\) respectively) and the algorithms estimate the height of \(im_{T}\) with respect to \(im_{R}\). Since the objective of this work consists in studying the performance of some methods in altitude estimation, we consider the images to compare have been captured along a line which is parallel to the z-axis of the WRS, being the axis of the catadioptric system in vertical position (Figure 1). This way, we isolate the effect of height changes in the images.  
To compare two scenes using their global appearance, a distance measurement must be defined. In this work, the _image distance_ is defined as the Euclidean distance between descriptors. Being \(\vec{d}_{T}\in\mathbb{R}^{n\times 1}\) the descriptor of the test image, and \(\vec{d}_{R}\in\mathbb{R}^{n\times 1}\) the descriptor of the reference image, the image distance can be obtained as:  
\[dist(\vec{d}_{T},\vec{d}_{R})=\sqrt{(\vec{d}_{T}-\vec{d}_{R})^{T}\cdot(\vec{d }_{T}-\vec{d}_{R})}. \tag{6}\]  
The best match among a set of different comparisons is found by choosing the one with minimum distance.  
The next subsections present the relative height estimation methods in detail. Four methods based on global appearance have been implemented and tested. They are based on the descriptorspresented in Section 2.2. Also, for comparative purposes, an additional method that uses local features is proposed in Section 3.5.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### IoT World Alliance  
The IoT World Alliance formerly known as the M2M World Alliance consists of a global partnership of telecommunications providers striving to enable seamless IoT device connectivity around the world, particularly for their proprietary technologies [63]. The alliance provides a single global subscriber identity module (SIM) that works on all member networks, they provide centralized management of connected devices and a single web platform portal to manage connections worldwide [63]. The interest of IoT World Alliance may be more or less related to IoT marketing and education.  
Other alliances, which are not fully discussed here are as follows, but not limited to the Hypercat Alliance, Alliseen Alliance, Open Mobile Alliance, Internet Protocol for Smart Objects (IPSO) Alliance, Continua Alliance, Connected Lighting Alliance, ZWave Alliance, and the EnOcean Alliance. These alliances are geared towards the standardization and interoperability of all facets of the IoT. This ever-growing list of alliances emphasizes the intense focus on IoT development and on the development of improved LPWAN technologies.  
In summary, Table 5 provides a list of consortia involved in the standardization of different IoT technologies. Essentially, in the absence of an all-encompassing alliance, the IoT market will continue to comprise different non-interoperable technologies, which may stifle the expected growth of the LPWAN market.
## 4 Security Analysis and Discussion
### Participant-Based Security Threats
#### 4.2.1 Parameter Manipulation Attack Analysis  
Consider an adversary-controlled participant (hereafter referred to as the attacker) attempting to manipulate the agreement key. Upon receiving the confidential parameters from the legitimate counterpart, the attacker may pursue several attack vectors:  
The attacker's manipulation capabilities are fundamentally constrained following quantum state transmission. Once the quantum state sequence has been transmitted, the attacker can only manipulate undisclosed confidential parameters. However, the quantum no-cloning theorem prevents duplication of transmitted quantum states, and measurement results, once obtained, cannot be retracted--establishing inherent physical limitations on the attacker's manipulation space.  
Regarding decoy photon position declaration attacks, the attacker might attempt to falsely declare decoy photon positions. Our protocol mitigates this threat through a robust verification mechanism wherein both parties must independently and simultaneously declare their respective sets of decoy photon positions. The protocol subsequently utilizes the intersection as the final set of decoy photon positions. At these positions, both parties disclose and compare measurement outcomes. Should an attacker declare false positions, they must correctly guess the corresponding quantum state for each decoy photon; otherwise, the deception will be detected during the verification phase.
# Photoconductive TiO\({}_{2}\) Dielectrics Prepared by Plasma Spraying
## 3 Results and Discussion
### Dielectric Behavior  
The relative permittivity of the n-TiO\({}_{2}\) sample, Figure 7, at room temperature (R.T.) started at 160 Hz with the value of 34, which is lower than the literature-based dielectric constant of polycrystalline TiO\({}_{2}\) (55 to 89 [24]). With growing frequency, the permittivity decreased, with a final value of 9.3 at a frequency 28 MHz. This drop was because of structural imperfections inherently present in plasma-sprayed coatings (pores, micro-cracks, local deviations from stoichiometry). With the increasing measurement temperature, the permittivity rose. The difference at low frequencies is huge, and at a high frequency, the values converge toward a unified magnitude of ~10. The difference at 160 Hz was 34 for R.T. versus 2100 for 250 \({}^{\circ}\)C, whereas the difference at 28 MHz was 9.3 for R.T. versus 10.8 for 250 \({}^{\circ}\)C. This is in full concordance with the polarization mechanisms taking place at various frequencies. The interfacial or space charge polarization produces considerably large relative permittivity only at low frequencies. If the frequency rises, the dipolar polarization subsequently ceases. The ionic polarization was also active in the MHz range, but this was the same for all the temperatures applied, since the compound is a simple oxide with no dopants. Due to these physical aspects, the run of permittivity, as in Figure 7, is expectable.  
The loss tangent, Figure 8, dropped with the growing frequency as well. At R.T., its values ranged between 0.8 and 0.08. For the highest measured temperature 250 \({}^{\circ}\)C, it was between 5.0 and 0.3. Over 100 \({}^{\circ}\)C, the decrease was monotonous, whereas up to 100 \({}^{\circ}\)C, a local peak around 8 MHz existed. The existence of two separable components of losses could be recognized [25]. The high-frequency component was one, where \(\varepsilon^{\prime\prime}{}_{r}\) corresponded to the part of the losses associated with the relaxation phenomena (cf. the semicircular halo in Figure 8 with maxima above 8 MHz). The low-frequency component exists due to a purely conductive mechanism. However, compared to several materials produced by electric-field-assisted sintering [26], the conductivity due to free charges is much less pronounced, and such a loss component is more due to the interfacial polarization.  
Figure 7: Relative permittivity of plasma-sprayed n-TiO\({}_{2}\).  
The thermal coefficient of capacitance (TCC) value was 643 ppm/\({}^{\circ}\)C. A value of about 400 ppm/\({}^{\circ}\)C is typical for sintered TiO\({}_{2}\)[27]. If the plasma deposit microstructure with pores and cracks is considered, the measured value could be accepted as rather good, however, open to further optimization.
## 3 Results and Discussion
### Post-Test Cell Analysis
#### 3.3.1 Bulk Characterization  
Bulk characterization of the electrodes was carried out by ICP-MS and XRD, as shown in Figure 11. Considering that the cell was completely discharged prior to its disassembly, a significant degree of degradation occurred in the cell based on the CCCV step charging method. This was determined based on the fact that the quantity of Li found in the anode was large, whereas that found in the cathode was small (Figure 11a).  
Figure 11: Analysis of the electrode bulk characteristics: (**a**) the inductively coupled plasma mass spectrometry results, and (**b**) the x-ray diffraction results.  
Figure 10: (**a**) Electrode thickness following disassembly; and (**b**) differences in the electrode thicknesses between the positive and negative electrodes.  
The low lithium content of the cathode could be accounted for by the higher amount of irreversible lithium trapped in the graphite lattice or consumed during SEI formation. However, the lattice parameters determined from the XRD data for the anode were comparable for both the CV step and the CCCV charging cells (Figure 11b). We therefore assumed that the majority of irreversible lithium was involved in the formation of by-products at the anode. Indeed, this result as consistent with the electrode thickness data presented in Figure 9. Furthermore, Figure 11b gives the distorted lattice parameters of the LCO as determined by the analysis of the XRD peaks of the cathode. During CCCV step charging, the cathode has the largest distorted c/a lattice ratio, and so the enhanced cathode degradation appears to be related to this process. However, further investigation is required to better understand the origins of these widely distributed lattice structures.
# Gluten-Free Sourdough Based on Quinoa and Sorghum: Characterization and Applications in Breadmaking
## 3 Results
### Sourdough Properties
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dough Sample** & **C2, Nm** & **tC2, min** & **TC2, \({}^{\circ}\)C** & **C1-C2, Nm** \\ \hline Q100 & 0.28 \(\pm\) 0.02 \({}^{\mathrm{b,A}}\) & 18.85 \(\pm\) 0.05 \({}^{\mathrm{a,C}}\) & 58.4 \(\pm\) 0.06 \({}^{\mathrm{a,C}}\) & 0.78 \(\pm\) 0.03 \({}^{\mathrm{a,C}}\) \\ Q75 & 0.30 \(\pm\) 0.02 \({}^{\mathrm{b,B}}\) & 17.98 \(\pm\) 0.04 \({}^{\mathrm{b,C}}\) & 56.7 \(\pm\) 0.06 \({}^{\mathrm{b,C}}\) & 0.72 \(\pm\) 0.03 \({}^{\mathrm{b,B}}\) \\ Q50 & 0.52 \(\pm\) 0.02 \({}^{\mathrm{a,B}}\) & 16.87 \(\pm\) 0.05 \({}^{\mathrm{c,B}}\) & 57.2 \(\pm\) 0.06 \({}^{\mathrm{c,C}}\) & 0.58 \(\pm\) 0.01 \({}^{\mathrm{c,A}}\) \\ \hline Q100 C & 0.29 \(\pm\) 0.02 \({}^{\mathrm{c,A}}\) & 18.92 \(\pm\) 0.04 \({}^{\mathrm{a,B,C}}\) & 59.8 \(\pm\) 0.06 \({}^{\mathrm{a,B}}\) & 0.84 \(\pm\) 0.01 \({}^{\mathrm{a,A,B}}\) \\ Q75 C & 0.35 \(\pm\) 0.02 \({}^{\mathrm{b,A}}\) & 18.62 \(\pm\) 0.05 \({}^{\mathrm{b,A}}\) & 59.3 \(\pm\) 0.06 \({}^{\mathrm{b,A}}\) & 0.74 \(\pm\) 0.03 \({}^{\mathrm{b,B}}\) \\ Q50 C & 0.56 \(\pm\) 0.02 \({}^{\mathrm{a,A}}\) & 17.67 \(\pm\) 0.04 \({}^{\mathrm{c,A}}\) & 57.5 \(\pm\) 0.06 \({}^{\mathrm{c,B}}\) & 0.57 \(\pm\) 0.01 \({}^{\mathrm{c,A}}\) \\ \hline Q100 SC1 & 0.28 \(\pm\) 0.02 \({}^{\mathrm{c,A}}\) & 19.32 \(\pm\) 0.04 \({}^{\mathrm{a,A}}\) & 61.5 \(\pm\) 0.06 \({}^{\mathrm{a,A}}\) & 0.83 \(\pm\) 0.01 \({}^{\mathrm{a,B}}\) \\ Q75 SC1 & 0.34 \(\pm\) 0.02 \({}^{\mathrm{b,A}}\) & 18.48 \(\pm\) 0.04 \({}^{\mathrm{b,B}}\) & 58.7 \(\pm\) 0.06 \({}^{\mathrm{b,B}}\) & 0.71 \(\pm\) 0.01 \({}^{\mathrm{b,B}}\) \\ Q50 SC1 & 0.52 \(\pm\) 0.02 \({}^{\mathrm{a,B}}\) & 17.70 \(\pm\) 0.04 \({}^{\mathrm{c,A}}\) & 57.6 \(\pm\) 0.06 \({}^{\mathrm{c,A}}\) & 0.57 \(\pm\) 0.03 \({}^{\mathrm{c,A}}\) \\ \hline Q100 SC2 & 0.29 \(\pm\) 0.02 \({}^{\mathrm{c,A}}\) & 19.00 \(\pm\) 0.04 \({}^{\mathrm{a,B}}\) & 61.6 \(\pm\) 0.06 \({}^{\mathrm{a,A}}\) & 0.87 \(\pm\) 0.00 \({}^{\mathrm{a,A}}\) \\ Q75 SC2 & 0.35 \(\pm\) 0.02 \({}^{\mathrm{b,A}}\) & 18.43 \(\pm\) 0.05 \({}^{\mathrm{b,B}}\) & 59.3 \(\pm\) 0.06 \({}^{\mathrm{b,A}}\) & 0.76 \(\pm\) 0.01 \({}^{\mathrm{b,A}}\) \\ Q50 SC2 & 0.52 \(\pm\) 0.02 \({}^{\mathrm{a,A,B}}\) & 17.72 \(\pm\) 0.04 \({}^{\mathrm{c,A}}\) & 55.6 \(\pm\) 0.06 \({}^{\mathrm{c,D}}\) & 0.56 \(\pm\) 0.04 \({}^{\mathrm{c,A}}\) \\ \hline \hline \end{tabular} Q100—quinoa flour, Q75—quinoa flour with 25% sorghum flour addition; Q50—quina flour with 50% sorghum flour addition; Q100 C, Q75 C and Q50 C—samples prepared with spontaneously fermented sourdoughs; Q100 SICI, Q75 SC1 and Q50 SC1—samples prepared with sourdoughs fermented with starter culture SC1; Q100 SC2, Q75 SC2 and Q50 SC2—samples prepared with sourdoughs fermented with starter culture SC2. For the same type of starter culture, the average values from a column corresponding to samples prepared from different flour blends, accompanied by different lowercase letters (a, b, c) are significantly different (\(p<0.05\)), based on the Tukey test.  
\end{table}
## 3 Methods
### Characterization of Change in Productivity Using TSS-RESTEND
#### 3.1.3 Identification of Structural Changes  
Structural changes of each pixel within NM ecosystems were identified based on three properties that include the significance of the breakpoints, direction of change (i.e., increasing and decreasing in productivity), and method of detection (as described in Section 3.1.2). With the regard to the significance level and direction of change, all pixels were classified into nine categories as shown in (Table 1) following [55] and similar to other dryland degradation studies in Australia [8] and China [5].  
Consequently, a pixel can be considered having:  
* Non-reversable degradation or initiation of degradation if it exhibited a breakpoint with \(p<0.05\) and a negative change (Table 1) in productivity as detected by Segmented VPR or Segmented RESTREND, respectively,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Direction of Change** & **Significance** & **Description** \\ \hline I1 & & \(p<0.01\) & Pixels with significant \\ \cline{3-4} I2 & Slope \textgreater{} 0 & \(0.01\leq p<0.025\) & increasing trend of residual \\ \cline{3-4} I3 & & \(0.025\leq p<0.05\) & at four classes of \(p\) levels \\ \cline{3-4} INC & & \(0.05\leq p<0.1\) & (0.01, 0.025, 0.05 and 0.1) \\ \hline DI & & \(p<0.01\) & Pixels with significant \\ \cline{3-4} D2 & Slope \textless{} 0 & \(0.01\leq p<0.025\) & decreasing trend of residual \\ \cline{3-4} D3 & & \(0.025\leq p<0.05\) & at four classes of \(p\) levels \\ \cline{3-4} DNC & & \(0.05\leq p<0.1\) & (0.01, 0.025, 0.05 and 0.1) \\ \hline NSC & & \(p\geq 0.1\) & No significant change in productivity \\ \hline \hline \end{tabular} INC = Increase No Change, DNC = Decrease No Change, I1 = increasing trend in productivity level 1, I2 = increasing trend in productivity level 2, I3 = increasing trend in productivity level 3, D1 = Decreasing trend in productivity level 1, Decreasing trend in productivity level 1, D03 = Decreasing trend in productivity level 3, NSC = Non-significant Change.  
\end{table}
Table 1: Categories of threshold for vegetation change.  
* Reversal or initiation of reversal in degradation, if it met the previous conditions except with a positive as detected by Segmented VPR or Segmented RESTREND, respectively,
* Stable increase in productivity, if it exhibited a breakpoint with \(p<0.05\) and a positive change as detected by RESTREND,
* Stable decrease in productivity if the opposite of the previous condition was detected (a condition that can be considered as an initiation of degradation) [11; 13].
* Non-significant change (NSC) in productivity irrespective of the detection technique used (i.e., Segmented VPR, Segmented RESTREND, RESTREND, or Indeterminant), if it had a breakpoint with \(p>0.1\) and a constant direction change (i.e., 0),
* Indeterminate change if it had \(p=0\) and slope = 0.  
A summary of the pixels based on these structural change categories was presented relative to NM's total area, quadrant, coregions, and major land use/land cover classes.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 2 Mathematical Preliminaries: SVS Indices  
As aforementioned, this paper aims to investigate short-term voltage stability monitoring techniques and to propose a new SVS technique using data based methods. It has been focused on the simply computed SVS indices which have acceptable reliability and speed from the short-term instability detection viewpoint. In this section, Lyapunov exponents as an important SVS index are studied first; Then, five other voltage indices are introduced and are then modified for power system SVS monitoring by adopting proper changes.