esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 5 Conclusions  
Accelerating the convergence speed of the PPP algorithm is one of the most concerning aspects of LEO navigation augmentation systems, and it is also a development hotspot in the field of LEO navigation augmentation. LEO navigation augmentation requires broadcasting the dedicated LEO navigation augmentation signals to achieve the goal of real-time high-precision positioning services. This paper analyzes the design requirements of LEO navigation augmentation signals. On this basis, BOC (2,1), BOC (2,2), BOC (4,1), and BOC (4,2) are designed for the L1 and L5 frequency bands. The proposed signals consist of a high-precision measurement component and a data component with a high data rate, which were multiplexed into one composite signal using MCSK. Finally, from the  
Figure 5: Analysis results of the degradation of effective \(C/N_{0}\) at L5: (**a**) GPS L5, (**b**) Galileo E5a, and (**c**) BDS B2a.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Desired Signal**} & \multicolumn{1}{c}{**BPSK(1)**} & \multicolumn{1}{c}{**MBOC(6,1,1/11)**} & \multicolumn{1}{c}{**QPSK(10)**} \\ \hline BPSK(1) & \(-61.7332\) & \(-67.9619\) & \(-\) \\ MBOC(6,1,1/11) & \(-67.9619\) & \(-65.1434\) & - \\ QPSK(10) & - & - & \(-70.9845\) \\ BOC(2,1) & \(-73.4420\) & \(-73.3847\) & \(-70.4891\) \\ BOC(2,2) & \(-73.4933\) & \(-68.8786\) & \(-70.3528\) \\ BOC(4,1) & \(-79.0205\) & \(-78.8849\) & \(-71.8354\) \\ BOC(4,2) & \(-79.0782\) & \(-74.4045\) & \(-71.6140\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: SSC results.  
perspective of the degradation of effective \(C/N_{0}\), compatibility between the proposed LEO navigation augmentation signals and GNSS signals is assessed. The results show that BOC (4,1) and BOC (4,2) have better compatibility, which can be considered the design of LEO navigation augmentation signals at L1 and L5.  
Conceptualization, T.Y. and Y.W.; formal analysis, T.Y. and L.B.; investigation, T.Y. and Y.W.; data curation, T.Y.; writing--original draft preparation, T.Y. and Y.W.; writing--review and editing, T.Y. and Y.M.; visualization, T.Y. All authors have read and agreed to the published version of the manuscript.  
The research was funded through the Outstanding Youth Talent funding program of the China Academy of Space Technology (Xi'an) (grant number: Y23-RCFY)Q2-04).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
Authors were employed by the company China Academy of Space Technology (Xi'an). The authors declare that this study received funding from China Academy of Space Technology (Xi'an). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## 4 Discussion
### Uncertainties and Limitations
Therefore, this study provided a reliable evaluation of the seasonal variation in GPP/SIF by separating the data into two groups based on different growth temperature conditions. In addition, improvement of the remote estimation of GPP is the ultimate motivation for studying the SIF-GPP relationship [34,88]. Large-scale GPP estimation relies on fine spatio-temporal satellite SIF products [89]. In this study, we used the aggregated eight-day TROPOMI SIF to obtain more data at the global scale. Future SIF products could provide daily and sub-daily observations, which would be more valuable for studying the SIF-GPP relationship under changeable environmental conditions. In addition, some studies have reported that an increased cloud fraction threshold can result in a decrease in estimated SIF [90]. Therefore, the availabilities would impact the number of selected samples for each PFT used to analyze the different responses of SIF and GPP to low temperatures. Overall, our analyses revealed obvious differences in the seasonal patterns of SIF and GPP under different growth temperature conditions, where the GPP/SIF ratio decreased under low-temperature conditions. The decrease in GPP/SIF with low temperatures highlighted the importance of considering this behavior when estimating GPP from satellite SIF observations [91]. Future studies should conduct more field experiments to explore the potential physiological and structural factors affecting the seasonal patterns of SIF and GPP. Although the mechanisms underlying the seasonal GPP/SIF patterns under environmental pressure require further investigation, the results obtained in this study regarding the effects of low temperatures on the SIF-GPP relationship can help to improve our understanding of the SIF-GPP relationship.
## 5 Discussion  
### Characteristic Basis for RRIM to Identify GALs in Lengqu River Section  
The basis for generating an RRIM-visualized topographic map based on a 12.5 m DEM to identify GALs mainly includes the following.  
Landslides identified based on the RRIM are basically real landslides when compared with the landslides actually investigated in the field, but we need to be able to summarize the characteristic law of landslides identified based on the RRIM, taking the GALs identified in the Lengqu River section as an example: firstly, the locations of landslides are determined along rivers, roads and fractures, and previous studies also show that landslides are often prone to occur here, while human activities are frequent near rivers and roads. In summary, more attention should be given to the areas around rivers and roads, both for the identification of GALs and for the consideration of human safety. The RRIM generated based on the 12.5 m DEM makes both the morphology and color features of GALs in the Lengqu River section significantly enhanced. In terms of morphology, there are more obvious landslide back edges, steep walls on both sides of landslides and depositions where landslides occur; in terms of color, gray rocks are exposed where landslide pullouts occur, while its surrounding area is red. Additionally, by comparing the landslide features presented on the RRIM with those presented on Google Earth, one can see that the landslide surfaces presented in the RRIM are more detailed, and since the RRIM simplifies the colors in the map to red and gray, it allows better identification of landslides if the features presented on the RRIM are grasped, without the obscuration of the landslide by the vegetation cover on Google Earth.
# Dual Roles of Water Availability in Forest Vigor:
## 2 Materials and Methods
### Materials  
#### 2.2.1 Satellite-Derived Vegetation Index  
The _NDVI_ derived from the Moderate Resolution Imaging Spectroradiometer (_MODIS_) of Terra was used to monitor forest vigor in our research. The MOD13A3 (Version 6) dataset is a global monthly composite product that spans 2001-2015 with a spatial resolution of 1 km ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)), accessed 9 December 2020). The _NDVI_ data were resampled to a spatial resolution of 0.01\({}^{\circ}\) by using ArcGIS (Version 10.2) (Esri, Environmental Systems Research Institute, New York, NY, USA). Additionally, to reflect the relative changes in forest vigor and to facilitate comparisons of forests, as well as to reduce the complex impacts of both biotic and abiotic factors on the _NDVI_ itself, the raw _NDVI_ data for each pixel were transformed into the deficit of _NDVI_ (_NDVI_) based on the maximum value of _NDVI_ during 2001-2015 (Equation (1)). Compared to the original value  
Figure 2: The seasonal changes in (**a**) monthly means of the temperature (_T_) and water balance (_WB_), (**b**) area percentage of \(T\) \(<\) 0 (\({}^{\circ}\)C) and _WB_ \(<\) 0 (mm/month), and (**c**) monthly means of the _NDVI_ of forests during 2001–2015. The PC, PM, NC, and NM represent planted coniferous, planted mixed, natural coniferous, and natural mixed forests, respectively. The _NDVI_ data were obtained from the Moderate Resolution Imaging Spectroradiometer (_MODIS_), and the temperature and water balance data were obtained from Climatic Research Unit (CRU, University of East Anglia, UK), for which detailed information can be found in Sections 2.2.1 and 2.2.4, respectively. Only the regions with the minimum _NDVI_ greater than 0 during January-September for the entire study period were considered in the results, which occupied 90.93% of the total forest cover. Detailed information on this can be found in Section 2.2.2. The whiskers of the bar represent the standard deviation of related data.  
of _NDVI_, the _dNDVI_ reflected the change in grid _NDVI_ that would be more directly related to the changes in water availability.  
\[dNDVI_{x,y,i}[\%]=\frac{(NDVI_{x,y,i}-NDVI_{x,y,i,\text{max}})}{NDVI_{x,y,i, \text{max}}}\times 100 \tag{1}\]  
where \(x\) and \(y\) represent the longitude and latitude of every forest pixel, respectively; \(i\) ranges from January to September; and _max_ represents the maximum value of the monthly _NDVI_ during 2001-2015.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IPS & indoor positioning system \\ VLC & visible light communication \\ RSS & received signal strength \\ TOA & time of arrival \\ TDOA & time difference of arrival \\ AOA & angle of arrival \\ QAM & quadrature amplitude modulation \\ ADC & analog-to-digital conversion \\ MCU & microcontroller control unit \\ DSP & digital signal processing \\ CRC & cyclic redundancy check \\ OOK & on-off keying \\ \end{tabular}
## 10 Experimental Analysis
### Statistical Comparisons on CEC2014 Problems
#### 10.1.2 Analyzing the Effectiveness of the Proposed Hybridization
Based on the findings in Table 2, we observe that the proposed ICDEBOA algorithm outperforms other well-known algorithms on the large number of considered benchmark objective functions, and only on several isolated cases does the proposed algorithm has worse or similar performance to other considered algorithms. It should be noted that the ICDEBOA algorithm outperformed the SHADE algorithm on all considered problems. Results demonstrated that the HPSOBOA algorithm is in terms of performance right behind the ICDEBOA algorithm in the majority of cases, and successfully outperformed the ICDEBOA algorithm on \(f_{23}\) and \(f_{28}\) over all dimensions, and on \(f_{6}\) HPSOBOBOA showed similar performance to ICDEBOA. On \(D=10\) and \(D=100\), the HPSOBOA achieved better solutions on functions \(f_{11},f_{13},f_{23}\) and \(f_{28}\). Over dimensions \(D=30\) and \(D=50\), the HPSOBOA showed better performance over composition functions \(f_{23},f_{24},f_{25},f_{27}\). The proposed and HPSOBOA algorithm achieved similar performance for \(D=30\) and \(50\) over simple multi-modal functions \(f_{4},f_{6},f_{9},f_{11}\) and \(f_{13}\), as well as for \(D=10\) and \(100\) over functions \(f_{6},f_{7},f_{14}\) and \(f_{27}\). The jDE algorithm performed better than ICDEBOA on \(D=30\) and \(50\) on composite functions \(f_{24}\) and \(f_{27}\), while the algorithms demonstrated similar performance on function \(f_{28},D=10\). The original BOA algorithm outperformed the proposed method on \(f_{23}\) and \(f_{27}\) functions over all dimensions, and \(f_{24}\) and \(f_{25}\) on \(D=30\) and \(50\). Since the proposed method ICDEBOA had significantly better performance than other algorithms in the majority of cases, especially compared to BOA and SHADE, it further proves the validity of the proposed hybridization. Using the Wilcoxon signed-rank test, the performance of the proposed ICDEBOA algorithm is compared to the performance of other algorithms that have been considered. Table 3 displays the statistical and comparative findings of the Wilcoxon signed-rank test applied to the CEC2014 benchmark problems. Based on the obtained results in Table 3, we observe that the ICDEBOA algorithm provides significantly better performance compared to other considered algorithms on all dimensions. Compared to conventional BOA and SHADE algorithms, we observe improvement of performance, which further confirms the validity of the proposed hybridization. On \(D=30\) and \(D=50\) ICDEBOA achieved similar performance to HPSOBOA algorithm; however, the proposed algorithm has the higher value \(R^{+}\) compared to \(R^{-}\). Moreover, the Friedman test is used to assess if there is a statistically significant difference between performance of considered algorithms. In this way, Table 4 shows the average rankings of the Friedman test for each method studied on the different functions and dimensions of the CEC2014 benchmark problems. Here, the first-place rank is displayed in bold, and underline id used to denoted second one.
## 4 Discussion
### Lift and Drag of the Investigated Commercial 4-Fin (T:80/A:150) Configuration
#### 4.2.1 Comparing Both 4-Fin Configurations  
Lift: The maximum lift coefficient of the _FCS Accelerator_ 4-fin configuration is 0.69 and differs by 27.3% compared to the _FCS k2.1_ fins. The stall point of the _FCS k2.1_ 4-fin configuration is located at an AoA of 35\({}^{\circ}\) and that of the _FCS Accelerator_ at an AoA of 20\({}^{\circ}\).  
Drag: The drag coefficient of the 4-fin configuration with _FCS Accelerator_ fins increases over the whole range of AoA to a maximum of 0.61 at an AoA of 45\({}^{\circ}\) and that of the _FCS k2.1_ fins to a maximum of 0.74 at an AoA of 40\({}^{\circ}\). The minimum difference of the drag coefficients of the 4-fin configuration with _FCS Accelerator_ fins and of the _FCS k2.1_ fins is 0.03 at an AoA of 5\({}^{\circ}\).  
Lift-to-drag: The lift-to-drag of the 4-fin configuration using the _FCS Accelerator_ fin reaches a maximum of 7.44 at an AoA of 5\({}^{\circ}\), whereas the maximum of the _FCS k2.1_ fins is 3.65 at an AoA of 10\({}^{\circ}\).
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Isotherm, Kinetics and Thermodynamic Studies  
#### 4.3.1 Isotherm Models  
The Langmuir and Freundlich isotherms (Table 1) were assessed using equilibrium data obtained at 288 K to calculate the maximum sorption and interaction mechanism for cadmium and methylene blue by PHSs, CHS-500 and CHS-700. Table 1 displays that the Langmuir isotherm model was a better fit for the results with good correlation coefficients. This result suggested that the adsorption of cadmium and methylene blue by PHSs, CHS-500 and CHS-700 took place on active sites on homogenous surfaces in a monolayer coverage [45].
# Lessons Learned from Investigating Robotics-Based, Human-like Testing of an Upper-Body Exoskeleton
## 1 Introduction
### Proposed Approach for Systematic Testing of Exoskeletons  
To address the absence of objective testing, our research investigates how to systematically test exoskeletons using robots; this paper presents the lessons we have learned through this approach. It covers conducted approaches to create a testing platform for upper-body exoskeletons utilizing standard robotic manipulators.  
In our previous work by [PERSON] et al. [26], we have outlined how three interconnected research domains interact in implementing the test platform, as illustrated in Figure 1. The first domain, Template Models and Interaction Simulation addresses the challenge of creating simplified abstractions of the interaction between the robot and the exoskeleton. These template models are crucial for gaining insights into complex interaction dynamics, developing theoretical approaches, and facilitating simulation. This primarily involves modeling the dynamics of the exoskeleton's actuator, essential for control and design processes.  
Achieving proper testing of exoskeletons with robots necessitates human-like motion generation, a challenge addressed in the second domain: Human Motion Capture and  
Figure 1: The schematic representation illustrates the interdependencies among the research fields connected to a robotic testing approach for exoskeletons. Figure adapted and simplified from [26].  
Learning. This domain is further divided into two subproblems: Human Motion Capture and Human Motion Learning. Human Motion Capture involves recording representative human movements and force profiles. As reported in [8], due to the diverse real-life applications of exoskeleton devices, selecting a reduced yet representative set of test cases becomes essential. With a representative dataset, Human Motion Learning focuses on encoding motion and force primitives through machine learning techniques.  
The third research domain, the Dedicated Robotic Test Platform, tackles challenges associated with robotic hardware. The robotic hardware must execute human-like motions based on the motion and force primitives acquired from the Human Motion Learning domain. This poses significant challenges, imposing requirements on the robot platform in terms of kinematics, dynamics, morphology, and control. The ability to perform human-like motions is critical for the effectiveness of the test platform.  
The subsequent sections include a detailed description of the scope of robotic testing, problem analysis, and requirements. Different approaches and experiments towards a concrete robotics-based testing platform are then provided and discussed. Finally, emerging challenges and lessons learned are thoroughly discussed and summarized.
## 3 Proposed Scheme: RSP-Based Message Authentication for VANET
### Requesting New Registration Set with NRG\({}_{\text{V}i}\)  
At \(T_{t+1}\), RSUs generate a new secret pool \(SP_{Rt+1}\) for \(T_{t+1}\), and the vehicle will request a new registration set, as follows.  
S1. \(V_{i}\) randomly chooses one of its identities, the secret index, and the private key (\(ID_{V_{i}x}\), \(d_{V_{i}x}\), \(PR_{V_{i}x}\)) in \(NRG_{V_{i}}\). \(V_{i}\) selects \(N_{v}\) new IDs to be \(IDS^{\prime}{}_{Vi}\) and sends the request to \(Ra\) (which is in communicating range of \(V_{i}\)).  
\[V_{i}{\rightarrow}R_{a}\colon(ID_{RX},\,T_{t},\,ID_{V_{i}},\,DS_{V_{i}}),(IDS^{ \prime}{}_{V_{i}})\]  
S2. After receiving the message, \(R_{a}\) randomly chooses a subset of (\(I{\sim}R_{R}\)) to be the new secret index set, \(DS^{\prime}{}_{V_{i}}\); sets a new set of private keys as \(PRS^{\prime}{}_{V_{i}}\), and selects one index in \(DS_{V_{1}}(d_{V_{i}x})\)  
\[PRS^{\prime}{}_{V_{i}}=\{PR^{\prime}{}_{V_{i}x}\ \big{|}\ PR^{\prime}{}_{V_{i}x}=SP_{Rt+1} \ (d^{\prime}{}_{V_{i}x})\ H\left(ID_{Ra}\ \parallel ID^{\prime}{}_{V_{i}x}\ \parallel T_{t+1}\right)P,\,x=I{\sim}N_{V}\},\]  
making \(P_{Ra,V_{i}}=\hat{\mathfrak{e}}\) (\(SP_{Rt}\) (\(d_{V_{i}x}\)) \(H\) (\(ID_{Ta}\ \parallel ID_{Ra}\ \parallel T_{0}\)) \(P\), \(H\) (\(ID_{Ra}\ \parallel ID_{V_{i}}\ \parallel T_{t}\)) \(P\)), and then returns the message, before recording information that includes the anonymous identity and the new \(NRG_{\text{V}i}\).  
\[R_{a}{\rightarrow}V_{i}\colon(ID_{TA},\,T_{0},\,ID_{Ra},d_{V_{i}x}),(\,DS^{ \prime}{}_{V_{i}}),(\,Ep_{Ra,V_{i}}\,(PRS^{\prime}{}_{V_{i}}))\]  
S3. \(V_{i}\) receives the message; decrypts the attached \(PRS_{\text{V}i}\) with \(P_{V_{i}Ra}\), sets \(PRG_{\text{V}i}=NRG_{\text{V}i}\) and lets \(NRG_{\text{V}i}=\{ID_{Ra},\,T_{t+1},\,IDS^{\prime}{}_{\text{V}i},\,DS^{\prime} {}_{\text{V}i},\,PRS^{\prime}{}_{\text{V}i}\}\).  
The pairing, \(P_{V_{i}Ra}=\hat{\varrho}(SP_{\text{Rt}}\) (\(d_{V_{i}x}\)) \(H\left(ID_{Ra}\ \parallel ID_{V_{i}}\ \parallel T_{t}\right)P\), \(H\left(ID_{TA}\ \parallel ID_{Ra}\ \parallel T_{0}\right)P)\) equals \(P_{Ra,V_{i}}\)  
Now, \(V_{i}\) receives its \(NRG_{Vi}\) and sets \(PRG_{Vi}\); \(R_{x}\) records the corresponding registration information, including \(NRG_{Vi}\) and the anonymous identity in \(PRG_{Vi}\) as in Table 3, but the anonymous identities in \(PRG_{Vi}\).
## 2 Materials and Methods
### Characterizations  
The base strengths (H_) of the samples were determined using Hammett indicators, including bromothymol blue (H_ = 7.2), phenolphthalein (H_ = 9.8), indigo carmine (H_ = 12.2), 2,4-dimitroaniline (H_ = 15.0), and 4-nitroaniline (H_ = 18.4) [27]. The basicity of the catalysts was measured by Hammett indicators in triplicate readings, and the average values were reported along with the standard deviation [28].  
The properties of the samples were determined by CO\({}_{2}\)-TPD. Prior to CO\({}_{2}\) adsorption, 50 mg of Ca/AC and Na/AC was pretreated under a He flow (20 mL-min\({}^{-1}\)) at 473 K for 30 min. Then, after cooling down to 373 K, CO\({}_{2}\) was pumped into the sample for 90 min. After that, physically adsorbed CO\({}_{2}\) was removed by the flow purging (30 mL-min\({}^{-1}\)) at 373 K for 2 h. Ca/AC and Na/AC were then heated to 1173 K with a ramp of 283 K-min\({}^{-1}\). Moreover, the content of desorbed CO\({}_{2}\) was detected by a gas chromatograph (GC) equipped with a thermal conductivity detector (TCD).  
The FT-IR study was performed on a Perkin-Elmer Spectrum GX system with a standard mid-IR DTGS detector in the wavenumber range of 400-4000 cm\({}^{-1}\). XRD patterns were collected on a D/max 2500 V X-ray diffractometer (Japan), and the crystalline phases of samples were identified by referencing the JCPDS card library. The specific surface areas (SSA) and pore size distributions of the samples were determined using a Quantachrome NOVA 1200e piece of volumetric gas adsorption equipment (USA). The samples were degassed for 2 h under vacuum at 473 K before being tested at 77 K with nitrogen as an adsorbate. The Brunauer-Emmett-Teller (BET) model was adopted to compute SSA, and the Barrett-Joyner-Halenda (BJH) model was used to determine the pore size distribution and pore volume.
# Ride Comfort Prediction on Urban Road Using Discrete
## 4 Results
### Correlations between DRI and MTVV
#### 4.2.2 Time Period  
As the MTVV is defined as the maximum value within a certain period of time, the correlation between the MTVV and the DRI would be affected by the time period which determines the localization resolution of the ride comfort prediction. To study the effect of the time period, we discuss three different time periods in the simulation: 1, 2, and 4 s.