esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment
### Frequency and Diameter of the Transducer  
The beam angle plays an important role in the selection of transducers for several reasons. First, because of the low concentration of the sound field, the propagation of the beam reduces the amplitude of the reflection. Second, the light beam's spreading may hinder the interpretation of the signal due to other features outside the reflective or examination region from the side of the test subject. Therefore, representing the sound field generated by the transducer is a prerequisite for understanding the observed signal.  
The beam spread depends to a large extent on the frequency and diameter of the transducer. The larger the beam angle, the poorer the directivity and the more dispersive the acoustic energy. The directivity of the sound field can be improved by decreasing frequency and increasing diameter. The specific relationship is as follows:  
\[\sin\theta=1.2\times\frac{V}{Df} \tag{10}\]  
where \(\theta\) is the angle between the tangent of the main lobe beam and the adjacent secondary lobe beam and the axis of the main beam, \(V\) is the sound velocity in the material (inch/sec or m/sec), \(D\) is the diameter of the transducer (inch or mm), and \(f\) is the frequency of the transducer (Hz).  
Figure 5a-c shows the received waves for a frequency of 2.5 MHz and transducer diameters of 6 mm, 8 mm, and 10 mm, respectively. The beam divergence angle of each sample was calculated according to Equation (9), and the respective results were \(\theta_{a}=27.6\lx@math@degree\), \(\theta_{b}=20.3\lx@math@degree\), and \(\theta_{c}=16.1\lx@math@degree\). The waveform shown in Figure 5c was clearly observed and possessed a clear outline. Therefore, for the 2.5 MHz transducer, the optimum diameter was 10 mm. This means that the suitable beam divergence angle of the 2.5 MHz transducer was ca. \(16\lx@math@degree\).  
The signals shown in Figure 5d-f were obtained for a diameter of 6 mm and varying frequencies (1, 2.5, and 5 MHz, respectively). Similarly, since \(\sin\theta_{d}>1\), \(\theta_{d}\) does not exist, in other words, the energy of this signal scattered, and the signals cannot be interpreted. \(\theta_{e}\) and \(\theta_{f}\) were \(27.6\lx@math@degree\) and \(13.4\lx@math@degree\), respectively. For a given transducer diameter of 6 mm, only the waves received by the 5 MHz transducer were available. Thus, for the 6 mm transducer, the optimum frequency was 5 MHz. Therefore, the proper beam divergence angle of the 6 mm transducer was approximately \(13\lx@math@degree\).  
Figure 5: Received waves for various diameters and frequencies of the transducer.  
According to the above analysis, in order to ensure the consistency of residual stress detection, transducers with different frequencies should have similar beam divergence angles. In this paper, we selected \(16^{\circ}\) as the beam divergence angle. This means that the diameter of the 1 MHz transducer is 25 mm, while the diameters of 2.5 and 5 MHz transducers can be 10 and 5 mm, respectively.
# Activity Evaluation Methodology for the Disposed Medical Linear Accelerators
## 3 Results  
### Component Dose Rate Measurements  
The material, mass, and volume information of the linac components are listed in Table 2. The measured dose rates for the target, 15 MV primary collimator, and 15 MV flattening filter were above 6 \(\mu\)SV/h because of the effect of the frequent collisions with high-energy electrons and photons. Dose rates lower than 1 \(\mu\)SV/h were measured from the lead block, upper Y jaw, scattering foil, ionization chamber, MLC, 6 MV flattening filter, and the mirror components.  
The dose rates for each component were measured on the day of disposal (Table 3) and after five weeks (Table 4). The dose rates were measured using a survey meter held at the surface at 5 cm distances with 1 cm intervals. As shown in Figure 4, the dose rate decreased logarithmically with increasing measurement distance. The components that exhibit 0.5 \(\mu\)SV/h or lower dose rates at 5 cm can be disposed of under Canadian law regarding the clearance level for disposal/recycling/reusing the activated medical accelerator components [17]. Following the Canadian guideline, the target, flattening filter, and primary collimator components were not allowed to be discarded on the day of disposal or for five weeks after.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Component** & **Material** & **Weight (g)** & **Volume (cm\({}^{3}\))** \\ \hline Target & SST304, Graphite, Au, Fe, Cr, Ni & 20 g & 14.84 cm\({}^{3}\) \\ Target Holder & W & 3680 g & 152.0 cm\({}^{3}\) \\ Scattering foil & Brass, Au, Ag & 90 g & 8.06 cm\({}^{3}\) \\
15 MV primary collimator & W, Al & 4720 g & 343.18 cm\({}^{3}\) \\
6 MV primary collimator & W & 4720 g & 343.18 cm\({}^{3}\) \\
15 MV flattening filter & SST304 & 120 g & 14.84 cm\({}^{3}\) \\
6 MV flattening filter & SST304 & 100 g & 13.41 cm\({}^{3}\) \\ Ionization chamber & Al, Fe, Cr, Mg & 190 g & 23.98 cm\({}^{3}\) \\ Mirror & H, C, O & 50 g & 4.77 cm\({}^{3}\) \\ Y jaw & W & 31250 g & 1886.5 cm\({}^{3}\) \\ Multileaf collimator (MLC) & W & 740 g & 54.63 cm\({}^{3}\) \\ Shielding block & W & 16,060 g & 942.48 cm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of the linac head components and characteristic.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Background Dose Rate** & **0.135 \(\mu\)Sv/h** & & & & \\ \hline
**Component/Distance** & **0 cm** & **1 cm** & **2 cm** & **3 cm** & **4 cm** & **5 cm** \\ \hline Target & 26.856 & 15.984 & 11.300 & 7.902 & 5.674 & 4.205 * \\ Target holder & 0.949 & 0.748 & 0.438 & 0.401 & 0.365 & 0.339 \\ Scattering foil & 0.654 & 0.496 & 0.368 & 0.332 & 0.292 & 0.286 \\
15 MV primary collimator & 7.650 & 4.918 & 3.330 & 2.282 & 1.930 & 1.228 * \\
6 MV primary collimator & 0.223 & 0.206 & 0.181 & 0.176 & 0.173 & 0.175 \\
15 MV flattening filter & 6.966 & 4.658 & 3.330 & 2.282 & 1.843 & 1.613 * \\ \hline \hline \end{tabular}
\end{table}
Table 3: On the day of disposal: Dose rate according to the distance of components (\(\mu\)Sv/h).
## 3 Results
### Simulation and Prediction of the MODEL  
Taking the MODEL as the dependent variable and GPP, FVC, WET, LAI, and LST as the independent variables, 2248 samples were collected in 2008 and 2018. The simulation and prediction models of the MODEL of Fuyun County in 2008 and 2018 were established. The regression models were as follows (significant at the 0.01 level):  
\[\text{MODEL}_{2008}=0.265\text{GPP}+0.447\text{FVC}+0.133\text{WET}+0.170 \text{LAI}-0.231\text{LST}+0.109\,\left(\text{R}^{2}=1\right) \tag{14}\]  
\[\text{MODEL}_{2018}=0.271\text{GPP}+0.473\text{FVC}+0.127\text{WET}+0.150 \text{LAI}-0.242\text{LST}+0.150\,\left(\text{R}^{2}=1\right) \tag{15}\]  
From the absolute value of regression coefficients of each index, FVC had the most significant impact on ecological quality in Fuyun County, followed by GPP, LST, LAI, and WET, which were consistent with the results of principal component analysis.  
In addition, a total of 4496 points were collected in 2008 and 2018. The relationship between the MODEI and the four indicators with the largest contribution rate (FVC, GPP, LST and LAI) was analyzed, and the MODEI samples were projected into the three-dimensional space (Figure 8). In Figure 8a, the top of the scatter plot represents the area with better ecological quality, which has higher FVC and higher GPP. Conversely, the bottom of the scatter plot represents the area with poor ecological quality, which has lower FVC and lower GPP. Multiple linear regression models also showed that both FVC and GPP had a positive contribution to ecological quality. The top of the scatter plot represents an excellent ecological environment with higher LAI and lower LST (Figure 8b), indicating that LST and WET had opposite effects on ecological quality, which was consistent with the results of principal component analysis and regression models.  
Figure 7: Probability statistics of variation of MODEI in different seasonal pastures.
## 3 Results and Discussion
### Binding Efficacy of Natural Compounds against Selected Target Enzymes  
Ten enzymes were selected for molecular docking based on their relevance as therapeutic targets in the management of AD and inflammation. Acetylcholinesteraseterase (AChE), \(\beta\)-secretase 1 (BACE1), Tau-protein kinase-I (TPK-I), cyclin-dependent kinase 5 (CDK5) and glycogen synthase kinase-3\(\beta\) (GSK-3\(\beta\)) are most attractive targets for anti-Alzheimer drug development [64] whereas 15-Lipoxygenase (15-LOX), inducible nitric oxide synthase (iNOS) and cyclooxygenaseases 1 and 2 (COX1 and COX2) have been implicated in the pathogenesis of many inflammation-related diseases including AD [31]. As per our hypothesis, we calculated the docking score or binding efficacy (kcal/mol) of each ligand against the ten selected enzymes (Table 2). According to the recorded molecular docking score, all ligands exhibited activity within the range of \(-6\) to \(-11\) kcal/mol, where \(-10.8\) kcal/mol was the most effective result for bangwausrene B against 15-LOX-2, and \(-6.4\) kcal/mol was the lowest potential score for 7\(\beta\),15\(\alpha\)-dihydroxy-3\(\beta\)-palmitatedup-20(29)-ene against GSK-3\(\beta\). Individually, 7\(\beta\),15\(\alpha\)-dihydroxy-3\(\beta\)-palmitatedup-20(29)-ene (\(-10.6\) kcal/mol) exhibited the greatest potential against 15-LOX-2, 3-O-\(\beta\)-D-glucopyranosyl-28-hydroxy-\(\alpha\)-amyrin (\(-9.6\) kcal/mol) against M_iNOS, apigenin- 8-C-\(\beta\)-D-glucopyranoside (\(-11.4\) kcal/mol) against M_iNOS, globimetulin A (\(-9.8\) kcal/mol) against 15-LOX-2, globimetulin B ( \(-10.7\) kcal/mol) against 15-LOX-2, globimetulin C (\(-10.4\) kcal/mol) against 15-LOX-2, bangwaursene B (\(-10.8\) kcal/mol) against 15-LOX-2 and 3\(\beta\)-acetoxy-11,12-epoxytaraxerol (\(-9.9\) kcal/mol) against BACE1 (Table 2). Overall, most candidates showed effectiveness against the 15-LOX-2 and M_iNOS targets, where apigenin- 8-C-\(\beta\)-D-glucopyranoside was the most prominent candidate followed by bangwausrene B as per the docking score. In addition, the protein-ligand interaction analyses indicated that apigenin- 8-C-\(\beta\)-D-glucopyranoside formed a greater number of hydrogen bond interactions with the respective target enzymes (Figure 4). Based on a comparison of _in silico_ with _in vitro_ data, globimetulin B and bangwausrene B, which showed the highest binding scores against 15-LOX-2 (\(-10.7\) and \(-10.8\) kcal/mol, respectively), exhibited a marked inhibitory effect against 15-LOX (IC\({}_{50}\) of 16.22 and 43.16 uM, respectively). Additionally, bangwausrene B, with a binding score of \(-10.6\) kcal/mol against M-iNOS, also exhibited an anti-inflammatory effect by inhibiting NO production (Figure 3A). These results indicate the accordance between the molecular docking
# Fast Localization and High Accuracy Recognition of Tire Surface Embossed Characters Based on CNN
## 3 Machine Visual Processing
### Template Matching
#### 3.4.2 Template-Based Template Matching  
Normalized Cross Correlation (NCC) is a typical grayscale correlation-based matching method with the advantages of being less affected by illumination variations and unaffected by scale factor errors [21]. The matching steps are as follows:  
Step 1. As shown in Figure 18, suppose the size of the search map S is N \(\times\) N, the size of the model image T is M \(\times\) M, and N \(>\) M; the template T is overlaid on the search map S, the search map under the template overlay becomes the submap \(S^{i;j}\); and i, j are the coordinates of the upper left pixel point of this subgraph in the S-graph, called the reference point.  
Figure 16: Tire expansion map.  
Figure 17: Making a matching template (**a**) is character image. (**b**) is the created template edge image.  
Figure 15: Tire deployment schematic.  
Step 2. The search graph was searched from left to right, from top to bottom, and the similarity measure R (i, j) of each subgraph, \(\mathrm{S^{i,j}}\), and template image T were calculated separately [22].  
\[R(i,j)=\frac{\sum_{m=1}^{M}\sum_{n=1}^{M}\left[S^{i,j}(m,n)\times T(m,n)\right]}{ \sqrt{\sum_{m=1}^{M}\sum_{n=1}^{M}\left[S^{i,j}(m,n)\right]^{2}}\sqrt{\sum_{m=1 }^{M}\sum_{n=1}^{M}[T(m,n)]^{2}}} \tag{7}\]  
Step 3. The larger the value of the NCC coefficient, the greater the correlation between the the template and the subgraph. The value range of R (i, j) is 0 to 1, the maximum value of R (i, j) is obtained, and the (i, j) position is recorded; that is, the position information with the greatest similarity is obtained, and the matching region is extracted.
## 4 Discussion  
### Comparison of the Results with Others Studies  
This study shows that significant droughts from the recent past generated high yield losses at the national scale with a systemic impact on the French territory. More extreme events were computed under the current climate in terms of hazard and yield losses. Their probability of occurrence was estimated by our model to be 13 years. Our results show that the frequency of these extreme events will increase in the future to a return period of seven years.  
These results are consistent with the ClimSec project [39]; with the study of IPCC [7; 44], which focuses on extreme events; and with other European studies using EUROCORDEX models [47; 48; 49; 50]. Climatic projections indicate that droughts will have a severity never before registered in terms of spatial extension and intensity. Other studies point out that the frequency of extreme drought events will strongly increase in the future, leading to a crop yield decrease, including grasslands under the RCP 8.5 scenario in all French territory [26]. In addition, studies focusing on specific countries and analyzing the evolution of droughts using climatic indices show an increase in severe drought in Greece [51], and a decrease in wheat yield due to drought severity [52] and an increase in drought frequency and severity in Spain [53], as well as in others areas like in China [54; 55] and the United States [56].  
Many studies show that the Mediterranean region appears to be very exposed to droughts in the future [57; 58]). Indeed, the different models used at the regional scale (RCM models) to measure the impact of climate change on drought events agree that the droughts will be more intense in southern Europe, especially in the Mediterranean region [7; 57; 59]. Extreme heat wave studies show that the Mediterranean region will therefore probably record a cumulative water deficit anomaly, but this is, however, more widely throughout France, where the evolution between the climate in 2000 and in 2050 will be the most marked [60; 61; 62; 63].  
These extreme events are the most worrying for the sustainability of agricultural production systems because they generate very significant losses at the country level, affecting food security. For example, the extreme drought in 2011 was responsible for losses of more than USD 1 billion for animal production in United States [64]. In the European Union, losses due to the 2003 drought are estimated at EUR 13 billion, including EUR 4 billion for France [65]. Nowadays it is well documented that in many rural areas, small farms do not have the financial capacity to cope with systemic climate shocks [66]. In the future, climate change will increase extreme drought frequency [8; 67], which raises the question of the resilience of farm income. The improvement of risk knowledge supports the assessment of the risk management systems currently in place and their sustainability in the context of climate change.
## 5 Results and Discussion
### Analysis of Axial Turbine Impact on Engine Performance
#### 5.4.3 Transient Response Comparison  
A direct comparison between the radial and axial turbines' time to maximum shaft speed is shown in Figure 31. The maximum improvement on transient response was 21.05%, and the minimum was 3.49%, with an average 11.76% across all engine conditions. The spool up times are in line with the literature, with a maximum of 2-3 s. However, the improvement does not fully correlate with the results available in the literature. This is expected, as the turbine in this study is designed to be of same size as of the radial turbine. As demonstrated in Figure 29, the axial turbine produces more power compared to the radial. Therefore, if the diameter was reduced, mass reduction could have been achieved, decreasing the mass moment of inertia and likely correlating with the literature results of 25-40% improvement. The results, however, demonstrate an improved transient response at all operating conditions of the engine. The map and inertia could also be scaled to lower diameter using  
Figure 30: Comparison of engine cylinder emissions between radial (**a**,**c**,**e**) and axial turbines (**b**,**d**,**f**) at different engine operating conditions.  
the methodologies discussed earlier. Using this new data, a greater improvement of transient response could be achieved.  
It should also be noted that the spool up time of 2000 rpm at 100% load does not correlate well with those reported in the literature [3; 9]. This was probably due to the choice of scaling for the design point (Section 4.2.4).  
Figure 32 shows the transient profiles of six different engine conditions, showing the profiles of the shaft speeds and manifold pressures over a 3-s transient simulation. The results are realistic with small improvement at lower loads. This is mainly due to the lower maximum rotational speed needed to be achieved as well as the likely insufficient exhaust flow fundamentally contributing to the turbo lag at these operating conditions.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 5 Discussion
### Lessons Learned--Impact on LoRaWAN Security
#### 5.1.2 Confidentiality and Integrity  
The secure connection between a LoRaWAN device and backend servers is heavily dependent on the secure storage and handling of the **AppKey** (and **NwkKey** in LoRaWAN 1.1). In many cases, however, the standard LoRaWAN installations offer only a few measures against physical attacks, which increases the probability of key extraction attacks by means of, e.g., firmware dumps or side-channel attacks. While the former can be thwarted, e.g., using secure elements [81], the latter call for stronger mitigation methods, e.g., tampering-detection mechanisms. A leaked **AppKey** is critical for many reasons. Firstly, it allows for the decryption of the eavesdropped LoRaWAN frames, which decreases communication confidentiality. Secondly, it allows an attacker to mount a rogue end device, which might, e.g., transmit crafted sensor data. Thirdly, a worm-hole attack could be extended towards a more advanced man-in-the-middle attack, where the attackeris able to manipulate both the contents and the metadata of a LoRaWAN packet. These attack vectors pose a serious risk for several LoRaWAN use cases as, via data falsification, the attacker can indirectly affect, e.g., data analysis, visualization or even automation based on LoRaWAN sensing.  
As a response to key extraction attacks, wireless-physical-layer techniques have been presented, which can be applied to improve confidentiality and integrity. Wireless secret key agreement techniques provide a low-power alternative to refresh the entire secret key material periodically. Additionally, due to the theoretical information secrecy, wireless key generation is not prone to massive brute-force attacks. This, in turn, is beneficial for the long-term secure storage of LoRaWAN application data. Next, RF fingerprinting methods are a versatile tool for intrusion detection. For instance, with a unique (and unclonable) fingerprint, a legitimate LoRaWAN packet can be distinguished from the one produced by a rogue device, man-in-the-middle attacker or worm-hole attacker.
## 3 Case Study
### Results
#### 3.3.2 Optimal Scheduling Results Analysis
The dehumidification system is activated only at 00:00-1:00, while the ventilation system operates during the evening from 20:00 to 22:00. In Figure 9b, the radiant heating system operates less frequently than in scenario A and avoids operation during peak electricity pricing periods. The ventilation system runs only briefly during the early morning and at 17:00-18:00 in the afternoon, while the dehumidification system remains inactive. Figure 9c presents the power scheduling under the multi-objective optimization scenario. Compared to scenario A, it reduces the operation time of both radiant heating and dehumidification in certain time periods. Compared to scenario B, it increases dehumidification activity and includes radiant heating during peak load periods, indicating a more balanced approach to economic cost and comfort. Figure 8: Optimized scheduling results of a typical day in a transitional season: (**a**) single-objective optimization (F1); (**b**) single-objective optimization (F2); and (**c**) multi-objective optimization.
## 4 Results
### Seasonal Variation at Various Locations
Figure 9: Seasonal scattering plots centered on the spring and fall equinoxes and summer and win solstices using dielectric constant estimated from CYGNSS with the new technique and measured by SMAP at TxSON, Texas in U.S. The results shown in Table 2 indicate that each confirmation parameter is comparable with the value calculated using the entire duration of data. In addition, the confirmation results show that the relevant variation depends on the season, but the factors causing seasonal variation require identification by further investigation. Even though the eight CYGNSS satellites cover the tropical region of the Earth, the specular points are sparsely distributed on the EASE grid, because a single observation using reflected GPS L1 signals will span approximately four months to cover the entire Earth. Thus, by adding more satellites or implementing a multi-GNSS constellation receiver, observing not only GPS L1, but also signal transmitted from GLONASS, Galileo, and Beidou, may allow for reliable determination of seasonal variation.
## 3 Results and Discussion
### Dye Adsorption Performance  
For the dye adsorption experiments, the MS/CID/ZrO\({}_{2}\) composites were added into 10 mL of simulated MB wastewater solution with different MB concentrations. By rapid vacuum filtration with an operating pressure of \(-\)0.1 MPa, the filtrate was collected and the remaining MB concentration was measured using an enzyme-linked immunosorbent assay (ELISA) reader. The principle of measuring the MB concentration with an enzyme-linked immunosorbent assay (ELISA) reader is the same as that of using an ultraviolet spectrophotometer to measure MB absorbance at a specific wavelength. The absorbance of an MB solution with a known concentration was measured at 660 nm, and the absorbance was compared with the standard curve of methylene blue according to previous reports [47, 48] in order to estimate the solubility of MB in the sample solutions, and finally the removal rate of MB was calculated.  
To further investigate the removal efficiency of MS/CID/ZrO\({}_{2}\) towards MB, we prepared simulated wastewater solutions with varying MB concentrations (1, 10, 50, 100, and 300 mg/L). After the adsorption by MS/CID/ZrO\({}_{2}\), a significant color change was observed, with the treated water becoming nearly transparent (Figure 6a). Then, we calculated the removal efficiency of the hybrid adsorbent. When the concentrations of the MB solutions were 1, 10, 50, 100, and 300 mg/L, the removal efficiency was 99.90%, 99.99%, 99.99%, and 99.74%, respectively (Figure 6b), which proves the excellent removal efficiency of the MS/CID/ZrO\({}_{2}\) towards MB. The removal efficiency of MB by MS is relatively poor, especially at low concentrations (Figure S2). In order to evaluate the saturated adsorption performance of MS/CID/ZrO\({}_{2}\) on MB, the MS/CID/ZrO\({}_{2}\) sample was dried, and 0.1 g of MS/CID/ZrO\({}_{2}\) was mixed with 250 mL of MB solution with a concentration of 50 mg/L. The results showed that the adsorption capacity of MS/CID/ZrO\({}_{2}\) for MB was 100.55 mg/g.  
Figure 6: Color change (**a**) and removal rate (**b**) of the MS/CID/ZrO\({}_{2}\) composites towards MB.