esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Ecosystem Services Value Change
#### 3.3.2 Spatial Variation Characteristics  
In this study, a 3 km \(\times\) 3 km grid was used to process data for the Southern Jiangsu urban agglomeration to analyze the spatial distribution characteristics of its ESV. Based on the range of ESV within each grid cell, ESV is categorized into five levels: ESV < 0.1 as the low-value area, 1 < ESV < 0.2 as the relatively low-value area, 0.2 < ESV < 0.3 as the medium-value area, 0.3 < ESV < 0.4 as the relatively high-value area, and ESV > 4 billion as the high-value area. Among them, the unit is in billion CNY. Its spatial distribution is depicted in Figure 6.  
As shown in Figure 6, the ESV's spatial layout within the Southern Jiangsu urban agglomeration showed a considerable degree of consistency between 2000 and 2020, exhibiting a characteristic pattern of higher values in the east and lower values in the west. Notably, the regions with higher ESV are predominantly situated in the eastern sector of the urban cluster, particularly in areas around water bodies such as Lake Tai. The region is rich in water resources, and the ESs provided by the aquatic systems are relatively strong, resulting in a significantly higher ESV level. The areas with lower ESV are mainly concentrated in the western region of the agglomeration, mainly in areas of farmland and built-up land. This is because the ESV per unit area of farmland is relatively low, and built-up land does not provide ESV. The medium-value and higher-value areas are situated in the far eastern part of the agglomeration. In terms of spatial pattern changes, the medium-value and higher-value areas in the east have gradually been replaced by lower values. This change is primarily closely related to the acceleration of the urbanization process, where urban expansion has led to the conversion of forests, farmlands, and some wetlands into built-up land, thereby weakening the ecological service functions of these areas.  
Figure 6: Spatial distribution of ESV.
# Design of a Ku-Band Monopulse Antenna with a Truncated Reflector and an Open-Ended Waveguide Feed
###### Abstract  
This paper presents a design for a monopulse reflector antenna with asymmetric beamwidths for radar applications at the Ku band. The proposed design features a rectangular waveguide monopulse feed and a truncated parabolic reflector. An array of four open-ended rectangular waveguides were employed to realize a compact monopulse feed. The reflector is cut in the \(H\) plane of the feed producing a wider beam in the azimuth plane. This type of pattern is useful in applications such as projectile tracking and airport surveillance. The design parameters for optimum performances are chosen at all stages of the design. The design and analysis have been carried out using the commercial simulation tool CST Studio Suite 2022. The directivity of the sum, elevation difference and azimuth difference channels of the reflector antenna are 32.1, 28.1, and 26.4 dB at 14 GHz; 30.9, 29, and 27.3 dB at 15 GHz; 31.7, 29.6, and 27.6 dB at 16 GHz; 31.6, 29.9, and 27.8 dB at 17 GHz.  
monopulse antenna; truncated parabolic reflector; sum and difference channels ac acmcopyright
## 8 Changes in Section 5. Range Versus Data Rate: Results  
In paragraph 2 line 2, the sentence \"Using the Free Space Path Loss model, it is found that a 2.4 GHz LoRa signal can travel up to 333 km in free space and still be received properly.\" should be  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Frequency & \(f\) & 2.4 & GHz \\ Spreading factor & SF & 5-12 & – \\ Bandwidth & BW & 203/406/812/1625 & kHz \\ Code rate & \(R_{\text{C}}\) & 4/5 & – \\ Transmission power & \(P_{TX}\) & 12.5 & dBm \\ Transmitter antenna gain & \(G_{TX}\) & 2 & dBi \\ Transmitter cable losses & \(L_{TX}\) & 2 & dB \\ Fading margin & \(L_{m}\) & 0 & dB \\ Receiver antenna gain & \(G_{RX}\) & 2 & dBi \\ Receiver cable losses & \(L_{RX}\) & 2 & dB \\ Base station height & \(h_{b}\) & 20 & m \\ Mobile station height & \(h_{m}\) & 2 & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for path loss modeling.  
changed to \"Using the Free Space Path Loss model, it is found that a 2.4 GHz LoRa signal can travel up to 133 km in free space and still be received properly.\"  
In paragraph 3 line 3, the sentences \"When transmitting with a spreading factor of 12 and the lowest bandwidth (i.e., 203 kHz), the path loss equals 150.5 dB. Consequently, a maximum communication range of 107 m can be achieved. Furthermore, the highest possible data rate at that range becomes 0.595 kbit/s. At the other extreme, the highest achievable data rate of 253.91 kbit/s is possible at a range of up to 26 m.\" should be changed to \"When transmitting with a spreading factor of 12 and the lowest bandwidth (i.e., 203 kHz), the path loss equals 142.5 dB. Consequently, a maximum communication range of 74 m can be achieved. Furthermore, the highest possible data rate at that range becomes 0.595 kbit/s. At the other extreme, the highest achievable data rate of 253.91 kbit/s is possible at a range of up to 18 m.\"  
In paragraph 4 line 1, the sentence \"Finally, the communication range of the urban ECC-33 path loss model varies from 25 m at the highest achievable data rate of 253.91 kbit/s to 867 m at the lowest possible data rate of 0.595 kbit/s.\" should be changed to \"Finally, the communication range of the urban ECC-33 path loss model varies from 3 m at the highest achievable data rate of 253.91 kbit/s to 443 m at the lowest possible data rate of 0.595 kbit/s.\"  
In the original article, there was a mistake in Figures 2-4 as published. The X-axis data in Figure 2 should be changed from 0-340 to 0-150, the X-axis data in Figure 3 should be changed from 20-110 to 15-80, and the X-axis data in Figure 4 should be changed from 0-900 to 0-500. The corrected Figure 2 appear below.  
Figure 3: Communication range and data rate for every combination of spreading factor (SF) and bandwidth (BW) in an indoor environment.  
Figure 2: Communication range and data rate for every combination of spreading factor (SF) and bandwidth (BW) in a free space line of sight (LoS) environment.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### Energy Consumption Evaluation  
We have also evaluated the energy consumption of ABE scheme on a Raspberry Pi 3 and a OnePlus A6000 smartphone. Figure 9 shows the power consumption (mAh).  
The battery capacity of a OnePlus A6000 is 3300 mAh. The ABE Decryption process, which is the most energy-consuming process, only takes approximately 0.13% of the capacity.
## 4 Experimental Methodology and Measurement Results
### RRU Synchronism  
Three experiments were performed to demonstrate the RRU synchronism. The jitter measurement (Section 4.2.1) showed the carrier frequency stability over time and provided the asynchronous jitter information in the time domain. The spectrum measurement (Section 4.2.2) illustrated the asynchronous phase noise spectrum in the frequency domain. The last one calculated the phase difference between two RRUs using the estimated channel frequency responses.
## 3 Dynamic Response under the Moving Train Loads
### Effect of Train Speeds on the Dynamic Response  
Based on the above analysis, train speeds of 350 km/h, 250 km/h, 200 km/h, 150 km/h, and 100 km/h were taken for calculations to study the effect of train travel speed on the dynamic response of the pile-soil foundation with the adjacent tunnel, respectively. The peak vibration acceleration, peak vibration velocity, and peak vibration displacement of the monitoring points in both the vertical and horizontal directions are shown in Figures 12 and 13.  
Figures 12 and 13 represent that the peak vibration acceleration, peak vibration velocity, and peak vibration displacement in different directions inside the foundation under different train speeds are similar. With the increase in train speed, each dynamic response index shows a positive correlation change. This is mainly due to the increase in the train speed corresponding to the increase in the moving train loads, and the dynamic response of the foundation caused by the train vibration becomes more obvious, which eventually leads to the increase in each dynamic response index. The dynamic response indexes increase the most at the surface, and the peak vibration acceleration, peak vibration velocity, and peak vibration displacement increase by 279.2 mm\(\cdot\)s\({}^{-2}\), 8.2 mm\(\cdot\)s\({}^{-1}\), and 0.29 mm, respectively.  
When the train speed is 100 km/h, the peak vibration acceleration, peak vibration velocity, and peak vibration displacement inside the foundation from the roof to the floor of the tunnel are reduced by 3.3 mm\(\cdot\)s\({}^{-2}\), 0.4 mm\(\cdot\)s\({}^{-1}\) and 0.15 mm, with a reduction of 28%, 26%, and 19%, respectively. When the train speed increased to 350 km/h, each dynamic response index was reduced by 86 mm\(\cdot\)s\({}^{-2}\), 2.6 mm\(\cdot\)s\({}^{-1}\), and 0.24 mm, respectively, with a reduction of 31%, 29%, and 23%. In order to show this change feature more intuitively, the reduction of the dynamic response indexes from the tunnel roof to the tunnel floor under different train speeds are shown in Figure 14. With the increase in train speed, each dynamic response index decreases with the increase in distance from the ground surface and pile, and the proportion and amplitude of the decrease all increase. The reduction of the peak vibration acceleration increases most significantly, from 3.3 mm\(\cdot\)s\({}^{-2}\) to 86 mm\(\cdot\)s\({}^{-2}\), an increase of 26 times. The impact of the tunnel structure on the attenuation of the dynamic response induced by moving train loads within the pile-soil foundation becomes more evident with the increase in train speed.  
Figure 12: Vertical dynamic response characteristics of the foundation for different train speeds. (**a**) Peak vibration acceleration. (**b**) Peak vibration velocity. (**c**) Peak vibration displacement.  
Figure 13: Horizontal dynamic response characteristics of the foundation for different train speeds. (**a**) Peak vibration acceleration. (**b**) Peak vibration velocity. (**c**) Peak vibration displacement.
## 3 Results  
### LUE at the 30 min Time Scale and Influencing Factors  
Monthly mean patterns of diurnal PAR, GPP, and LUE during the growing seasons (April-October) of the Chinese cork oak plantation in 2018 and 2019 are presented in Figure 3. For each month, the monthly mean diurnal PAR exhibited a regular \"/\" pattern during the daytime, peaking at around 12:00 (Figure 3a). Since the diurnal course of the GPP was mainly determined by that of the PAR, the monthly mean diurnal variation in the GPP was similar to that of the PAR, and the monthly mean diurnal GPP was close to its maximum value from 9:00 to 14:00 in all months (Figure 3b). The monthly mean diurnal LUE first increased sharply and reached a peak value at around 8:30, and gradually decreased until 12:00. It increased gradually in the afternoon and reached its maximum value at sunset (Figure 3c). Compared to other months, the monthly mean diurnal LUE values were lowest at around 12:00 in June (Figure 3c).  
Because of the dramatic changes in LUE at sunrise/sunset (Figure 3c) and the effect of vegetation growth on the LUE, the measured 30 min data from 10:00 to 14:00 during May-September (canopy closure period) in 2018 and 2019 were used to assess the effects of biophysical factors on the LUE at the 30 min time scale (Figure 4). As shown in Figure 4a, all the biophysical factors influenced the LUE at a significance level of \(p<0.001\). The LUE was positively correlated with the EF and GPP, with correlation coefficient (\(r\)) values of 0.48 and 0.68, respectively, and the remaining factors (PAR, CI, T\({}_{\text{a}}\), and VPD) were negatively correlated with the LUE, with \(r\) values ranging from \(-0.39\) to \(-0.67\). However, a path analysis between the LUE and the biophysical factors showed that the effects of PAR and GPP on the LUE were mainly direct; the effects of the remaining factors (EF, CI, T\({}_{\text{a}}\), and VPD) on the LUE were mainly indirect (via PAR and/or GPP); the direct effect of the CI on the LUE was \(-0.19\) (Figure 4b).
## 3 Methodology
### Research Method
\[x_{w}=\frac{\sum\limits_{i=1}^{n}w_{i}x_{i}}{\sum\limits_{i=1}^{n}w_{i}},\;y_{ w}=\frac{\sum\limits_{i=1}^{n}w_{i}y_{i}}{\sum\limits_{i=1}^{n}w_{i}} \tag{3}\]  
Standard deviation along the \(X\)-axis and \(Y\)-axis:  
\[\sigma_{x}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(w_{i}x_{i}^{\prime}\cos \theta-w_{i}y_{i}y_{i}\sin\theta\right)^{2}}{\sum\limits_{i=1}^{n}w_{i}^{2}}}, \;\sigma_{y}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(w_{i}x_{i}\sin\theta-w_{i} y_{i}^{\prime}\cos\theta\right)^{2}}{\sum\limits_{i=1}^{n}w_{i}^{2}}} \tag{4}\]  
Azimuth:  
\[\tan\theta=\frac{\left(\sum\limits_{i=1}^{n}w_{i}^{2}x_{i}^{2}-\sum\limits_{i= 1}^{n}w_{i}^{2}y_{i}^{2}\right)+\sqrt{\left(\sum\limits_{i=1}^{n}w_{i}^{2}x_{ i}^{\prime 2}-\sum\limits_{i=1}^{n}w_{i}^{2}y_{i}^{\prime 2}\right)^{2}+4\left(\sum \limits_{i=1}^{n}w_{i}^{2}x_{i}^{\prime 2}y_{i}^{\prime 2}\right)}}{2\sum \limits_{i=1}^{n}w_{i}^{2}x_{i}y_{i}y_{i}} \tag{5}\]  
where (\(x_{i}y_{i}\)) represents the coordinates of point features; \(n\) indicates the total number of features; \(w_{i}\) refers to weight; (\(x_{w}\),\(y_{w}\)) signals the weighted mean center of features; (\(x_{i}^{\prime}\),\(y_{i}^{\prime}\)) represent the coordinate deviations of different POI point features from the mean center; \(\sigma_{x}\) and \(\sigma_{y}\) refer to the standard deviation along the \(x\)- and \(y\)-axes; and \(\theta\) is the azimuth, an intersecting angle formed by rotating the ellipse clockwise from due north to the major axis. We used a geographic detector to discern the spatial differentiation of the nighttime economic index and the extent to which factors such as urban traffic construction, urban population concentration, urban industrial structure, the level of economic growth, government policy implementation, and residents' consumption level explain the spatial differentiation of the nighttime economic index.
## 3 Path Planning in Robotics and Additive Manufacturing  
### Path Planning in Additive Manufacturing  
In the field of path planning, it is necessary to distinguish between the different possibilities of degrees of freedom of movement. Paths can be planned either freely in three-dimensional space or two-dimensionally within a plane. In AM, the printing process must ensure the positioning and orientation of the print head in relation to the print bed. In a conventional fused deposition modeling (FDM) process, it is sufficient to position the print nozzle at the designated position and always keep it perpendicular to the print bed. In contrast, the orientation of the print head in all three rotational degrees of freedom is of great importance for the present work. Changing the direction of the external wire feed to the path tangent would have a decisive influence on the quality of the welded path. Despite the common designation of AM as '3D printing', an actual execution of paths in space is rarely found, as most processes can be strictly classified as 2.5D. The path planning of AM processes has already been researched and developed in many ways. The focus of path planning is on generating a component with the highest possible accuracy, high surface quality and good mechanical properties. Various infill strategies have been developed for this purpose. Typical strategies are raster, zigzag, contour, spiral, space-filling, grid, honeycomb, hexagonal or Voronoi diagram-based infills (cf. Figure 4). The different strategies are not always suitable for use in WAAM-based processes. In addition, certain space-filling infills are not executable by a robot due to movement limits or require many changes of direction. Therefore, in the context of this work, the simplest infills (namely raster and zigzag) are considered first. Table 1 gives an overview of different path planning algorithms. The summary describes the advantages and disadvantages in the context of robotic MDAM using WAAM. It can be summarized that none of the presented approaches, methods or algorithms can be directly applied to MDAM, especially for pure object manipulation in WAAM. The reason for this is the lack of integration of robotic parameters in the path planning of AM. Figure 4: Common infill strategies in AM.
## 1 Introduction  
An ultrasonic polar scan (UPS) is obtained by replacing the translational movement of a classical ultrasonic C-scan setup with a rotational movement. Hence, instead of scanning a plate surface at normal incidence, the UPS insonifies a certain material spot from as many oblique incidence angles \(\psi(\phi,\theta)\) as possible [1]. A schematic of the UPS method is presented in Figure 1a. Depending on the employed ultrasound, we speak of a pulsed ultrasonic polar scan (P-UPS) for a broadband pulse or a harmonic ultrasonic polar scan (H-UPS) for mono-frequency ultrasound. To improve coupling of the ultrasonic wave energy in the sample, the plate sample is immersed in water. Simply recording the transmitted (or reflected) ultrasound amplitude then yields a UPS image. Figure 1b,c shows current state-of-the-art P-UPS recordings for aluminum and [0\"]\({}_{8}\) carbon/epoxy (C/E) laminate, using an ultrasonic pulse with central frequency \(f_{\mathrm{c}}\) = 5 MHz. The vertical incident angle \(\theta\) is placed on the radial axis, the in-plane polar angle \(\varphi\) is represented along the angular axis, while the assigned gray (or color) scale is a measure of the transmitted (or reflected) pulse amplitude. Hence, the P-UPS image comprises a large collection of amplitudes of obliquely transmitted (or reflected) ultrasound pulses.  
Within the amplitude landscape of the P-UPS, characteristic contours emerge that more or less relate to the condition for critical (or in-plane) stimulation of the quasi-longitudinal, horizontally and vertically polarized quasi-transverse wave modes [2]. Note that along symmetry orientations, the prefix \"quasi-\" may be omitted, resulting in pure wave modes. These stimulation conditions are of course linked to the mechanical properties, like elasticity, of the insonified plate sample through [PERSON]'s equation and Snell's law [3,4]. As such, the P-UPS image actually represents an acoustic fingerprint of the mechanical properties. In the example of Figure 1b, the circular symmetry clearly puts on view the isotropic mechanical nature of the aluminum sample. The isotropic nature further invokes a degeneration of the quasi-transverse horizontal and quasi-transverse vertical wave, thus resulting in a P-UPS image with two characteristic contours. Figure 1c, on the other hand, displays a stretched appearance due to the unidirectional nature of the [0\({}^{\circ}\)]s C/E laminate. Higher stiffness values lead to smaller critical angles (Snell's law), and thus correspond to the contours being locally pressed inward in the P-UPS image. Considering the inner contour of Figure 1c, which is dominated by the quasi-longitudinally polarized wave, one can immediately determine that highest stiffness is found along \(\varphi=0^{\circ}\), which obviously corresponds to the orientation of the carbon reinforcement fibers of the [0\({}^{\circ}\)]s C/E laminate.  
In the remainder of this manuscript, a historical overview of the UPS research is given, starting from the initial results of the pioneering researchers, going over our recent advances [5], and finishing with currently investigated research lines.
## 3. Results
In an absolute value, the decrease was equal to 0.13. For the OHI, the percentage decrease was equal to 11.59%. In an absolute value, the decrease was 0.24. In the end, it could be stated that the percentage difference between the two groups at the two times for the Debris Index parameter was 6.26%, for the Calculus Index it was 8.55% and for the OHI it was 7.37%. Figure 1: Plaque Index (score) evaluated in both test and control groups at two different time points (T0–T1). Table 2 shows the data relating to the evaluation of the gingival abrasion areas (Figures 4 and 5) detected after a time of 48 h without daily oral hygiene. Figure 4 specifically shows the areas of gingival abrasion that could be evaluated, i.e., mid-gingival, interdental and marginal. Figure 5, on the other hand, shows the oral hygiene in two subjects treated 48 h after the end of the experiment to evaluate the GAA. Data collected for the test group (a total of 20 subjects) showed that 75% of subjects maintained a stable gingival health (H-H) after using a revealing solution that highlighted the abrasive surfaces of the oral epithelium. Figure 3: Oral Hygiene Index (score) reported in the control group at different times (T0–T1). Figure 2: Oral Hygiene Index (score) reported for the test group patients at different times (T0–T1). In total, 15% of the test group (3 treated subjects) maintained their clinical condition of non-health (NH-NH) stable; finally, 10% of the patients in the test group suffered a worsening of gingival health (from parameter H to NH), probably due to delayed brushing after 48 h.  
Table 2 also reports the data of the control group (20 subjects) where we observed 70% of stable patients (HH) corresponding with 14 subjects who used a traditional toothbrush; 20% were also stable (NH-NH) and 10% of subjects had a worsening of their clinical condition (H-NH). These data clearly demonstrated that the product under test with the thermoplastic bristles (Boie toothbrush) compared with the standard soft bristle toothbrush had an almost similar gingival abrasion result: in fact, a 5% percentage difference was observed between the test group and the control group for the HH parameter, an equal  
\begin{table}
\begin{tabular}{l  
5% for the NH-NH parameter and no percentage difference for H-NH. However, there is a minor advantage of the test product over the control group with better oral hygiene results starting from a similar brushing technique and a fairly similar distribution of patients within the two investigated groups: this may indicate that elastomeric toothbrushes have been compared with other devices.
# Farm Monitoring System with Drones and Optical Camera Communication
## 5 Experimental Results
### Experimental Condition
#### 5.1.4 Demodulation  
The transmitters mounted on the sensor nodes transmitted continuous light that was captured by a drone-mounted camera. The captured video was sent to an edge server to be divided into a series of static images. The edge server used the filmed images to demodulate the optical signals from the sensor nodes. Specifically, it demodulated the optical signals by mapping the RGB values of the LED panels to the (x, y) color space. The color coordinates of each bit were determined by pilot signals. The optical signals were demodulated based on the determined color coordinates.