esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Online Fault Diagnosis Model
### Proposed New Fault Mode Detection
#### 3.3.2 The Proposed MPDFCDF Cluster Evaluation Method
The MPDFCDF cluster evaluation method, thus, consists of the following steps, which are illustrated in Figure 12:  
* Step 1: Classify samples belonging to \(k\) clusters by \(k\)-means clustering;
* Step 2: Calculate the mean \(\mu\) and covariance \(\sum\) of all clusters;
* Step 3: Calculate the PDFs for all clusters using Equation (8);
* Step 4: Calculate the local distribution factor for each cluster;  
\[Local\_Distribution\_Factor_{c}=\frac{\max(\rho_{c}(X))}{2\times\max(\sum_{c})};\]  
the 2-sigma rule [41] is used here, which states that 95% of samples exist within 2-sigma of the mean and the remaining 5% can be regarded as outliers. * Step 5: Calculate the global density factor for the distribution  
\[Global\_Density\_Factor=min(Local\_density\_factor);\]  
Figure 11: Two cluster distribution examples, (a) well separated data into easily identifiable clusters and (b) two data groups are in close proximity to each other but far away from the other groups. * Step 6: Calculate the global separability factor _Global Separability Factor = min(Inter_Cluster_Dist)_ where _Inter_Cluster_Dist(\(c_{i}-c_{j})=\sqrt{(c_{i}-c_{j})^{2}}\);
* Step 7: Calculate the MPDFCDF _MPDFCDF = abs(Global Density Factor-Global Separability Factor)_ where the minimum MPDFCDF value identifies \(k_{opt}\).
## 5 Performance Analysis of the CL-APCC Protocol
### Control Complexity Analysis of CL-APCC Protocol  
**Theorem 2.** It is \(O(N)\) that is the complexity of control message in the whole network.  
_Proof._ The majority of control information is carried out incidentally at packet header. It is divided into two kinds of control information. 1 During the network initialization, each node monitors the notice information of its neighbors to obtain its own conflict region. The monitoring process requireseach node to send data once. The complexity is \(O(N)\) for this kind of control information. 2 The A-type node of each grid at the end of period \(t\) broadcasts information about the next period, including the average rate of the grid, the sending probability of each node, and the new A-type node information. The complexity is \(O(K)\) for this kind of control information.  
To sum up the complexity of the two kinds of control information, it can be seen that \(O(N)\) is the control complexity of the network. Therefore, the theorem 2 is proved to be correct.
## 4 Experimental Results  
In this section, we will present several simulation results which illustrate the effectiveness of the proposed methods. All experiments are performed by using MATLAB R2013a on a PC equipped with an Inter Core i7 3770k CPU, 3.5 GHz and 32 GB memory. The state-of-the-art sparse recovery methods such as MP, BP, FOCUSS, SBL, H/1-DCD, _etc._ are selected for comparison. As a benchmark we will utilize an oracle sparse recovery (OSR) to represent the best inversion performance, which knows the true support of \(\mathbf{x}\). In addition, we set parameter \(\alpha=10\) in Equation (4) for HR-DCD, in Equation (5) for Hlog-DCD, and \(p=0.8\) in Equation (6) for H\(l_{p}\)-DCD, and \(M_{b}=10,\tau_{\min}=0.02\), \(\gamma=0.9\) by experience from extensive simulation results. Other predefined parameters such as \(H\), \(L\), \(S\), and the grid refinement times \(Q\), _etc._, are decided according to the actual conditions, which will be given in details in the next text.
## 3 Data and Methods  
Due to the fact that the present study is based on the spatial analysis of land-use/land-cover changes in relation to the changes produced within the flash-flood potential across the study area  
Figure 1: Study area location.  
between 1989 and 2019, the vast majority of the data used are geospatial. Thus, the determination of land-use/land-cover changes was based on the use of two Landsat 5 and Landsat 8 satellite scenes. The scenes from Landsat 5, acquired on 18 August 1989, and Landsat 8, acquired on 17 May 2019, were downloaded from the United States Geological Survey (USGS ) Earth Explorer platform and were used in the supervised classification procedure. It should be mentioned that the spatial resolution of Landsat scenes was \(30\times 30\) m. Along with the Landsat imagery, other databases were used to assess the flash-flood potential across the study area. Thus, six flash-flood predictors are morphometric factors that were derived based on the digital elevation model (DEM). The DEM for the study area was created from contour lines digitized at a 5-m equidistance based on the Topographical Map of Romania [56]. The DEM was derived using the same spatial resolution as the Landsat scenes. Another two flash-flood predictors, represented using hydrological soil group and lithology, were extracted from the Digital Soil Map of Romania, 1:200,000 [57], and the Geological Map of Romania, 1:200,000 [58]. The monthly precipitation amounts from 1961 to 1989 and 1990 to 2018, collected from 32 meteorological stations around the study area, were taken into account along with the DEM, in order to calculate the spatial variability of the flash-flood predictor represented by the modified Fournier index.  
In order to analyze the relationship between land-use/land-cover changes and flash flood potential during the period 1989-2019, the collected data was inserted into the methodological workflow (Figure 2), which includes four main steps: remote sensing image processing, land-use/land-cover change analysis, flash-flood potential assessment, and geo-statistical analysis. These steps are presented in the following sub-sections.  
Figure 2: Methodology performed in the study.
## 2 Governing Equations and Dynamic Coefficients
### Reynolds Equation
Once the pressure has been computed, the bearing forces in the \(x\)- and \(y\)-directions are obtained by integrating the pressure over the whole area of the oil-film  
\[ \begin{cases}F_{x}\\ F_{y}\end{cases} =-C_{b}\int_{-\frac{1}{2R}}^{\frac{1}{2R}}\int_{0}^{2\pi}P(\Phi,Z) \begin{cases}\cos\Phi\\ \sin\Phi\end{cases} \mathrm{d}\Phi\mathrm{d}Z, \tag{6}\]  
where \(C_{b}=6\mu\Omega R^{4}/c^{2}\) is the coefficient containing the parameters of the bearing. Theoretically, the vertical oil-film force \(F_{y}\) should equal the static load \(W\) with the horizontal force \(F_{x}\) equal to zero at the equilibrium position for the horizontal rotor. Accordingly, the following condition should be satisfied [46]  
\[\tan^{-1}\frac{F_{x}}{F_{y}}=0. \tag{7}\]  
However, \(F_{x}\) is not usually equal to zero, since the attitude angle \(\Phi_{0}\) is unknown before calculation. To reach the equilibrium position and to calculate the bearing forces accurately, the following procedures are adopted to determine the attitude angle. First, a predefined value \(\Phi_{0}^{0}\) is assumed as the initial attitude angle to calculate the initial bearing forces. Then, an error function is defined as  
\[\Delta\Phi_{0}^{k}=\tan^{-1}\frac{F_{x}^{k}}{F_{y}^{k}}, \tag{8}\]  
where \(k\) represents the \(k\)th iteration. The attitude angle \(\Phi_{0}\) is updated by a Newton-Raphson iteration such as  
\[\Phi_{0}^{k+1}=\Phi_{0}^{k}-\Delta\Phi_{0}^{k}\frac{\Phi_{0}^{k}-\Phi_{0}^{k-1 }}{\Delta\Phi_{0}^{k}-\Delta\Phi_{0}^{k-1}}. \tag{9}\]  
Eventually, the attitude angle is determined by using Equations (8) and (9) alternately, until Equation (7) is satisfied. The stopping criterion of the Newton-Raphson iteration is based on the error at the \(k\)th iteration. The attitude angle is determined if \(|\Delta\Phi_{0}^{k}|\leq\delta_{\Phi}\) is satisfied, where \(\delta_{\Phi}\) is a prescribed error tolerance. The pressure distribution and the oil-film forces at equilibrium position are obtained. The Sommerfeld number representing the relationship of the major variables in the hydrodynamic lubrication analysis is calculated by  
\[S=\frac{\mu\Omega RL}{\pi W}\bigg{(}\frac{R}{c}\bigg{)}^{2}. \tag{10}\]  
Figure 2: Schematic diagram of a misaligned HDB.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 3 Sensor Selection
### Sun Sensors  
Sun sensors are electronic devices capable of measuring the direction of the incoming sunlight in the body reference frame. Comparing this measurement with the known position of the Sun in the inertial reference frame allows solving the attitude problem, given another independent vector direction is measured as well. As said, in the proposed sensor architecture, the geomagnetic field vector completes the measured attitude directions.  
Miniaturized Sun sensors for nano-satellite applications are commonly based on photodiodes. These can be utilized individually as independent Sun angle sensors or arranged in groups to compose more refined Sun direction sensors. In the first case, the complete three-dimensional direction of the Sun is only available placing at least three photodiodes aligned with different body axes. In the second case, a single sensor component can directly measure the Sun unit vector in the body frame. The proposed sensor architecture utilizes both alternatives for the fine and the coarse Sun measurements.
# Energy Efficiency Perspectives of PMR Networks +
## 5 Technical and Economic Considerations on Energy Efficiency of Next Generation PMR Systems
### LTE Energy Efficiency Gain  
The actual transition from the traditional TETRA infrastructure to the LTE platform could introduce a significant energy saving gain without considering the adoption of any particular strategy. Referring to the most power consuming stage, that is the RF stage, the main difference between TETRA and LTE is related to modulation types and techniques. In particular, TETRA, as all the systems coming out from 2G cellular technologies, employs constant envelope modulations, which improve the efficiency of power amplifiers thanks to the low peak to average power ratio (PAPR). Recently, several techniques have been proposed in order to increase the PA efficiency. In particular, Envelope Tracking has been found to be the most effective one and has been included in the LTE standard. Envelope Tracking dynamically adjusts the supply voltage to the envelope of the RF input, allowing a better efficiency also for high PAPR modulations, like Orthogonal Frequency-Division Multiplexing (OFDM) used in the LTE system. Looking at the power consumption model, a comparison could be done considering the LTE system reference case provided by the European Seventh Framework Programme (FP7) EARTH project [30]. The reference case has been obtained from the power measurements of a 10 MHz \(2\times 2\) LTE base station, and, referring to the linear power model in Equation (2), \(\Delta_{p}=4.7\) and \(P_{0}=130\) W have been set.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 3 The Smart Grid Network Model
### Case Two--Multi-Hop (Mesh)
#### 3.5.1 Network Discovery and Registration  
When a \(SM_{ng}\) is initially switched on, it must learn about its neighbours in the network, which are within its range, in order to forward packets through them. To discover its neighbours, it broadcasts a Hello message and, at the same time, is listening for Hello packets that are broadcast by its neighbours (other \(SM_{ng}\), \(GW_{g}\), or NOC). The network discovery process is repeated every T time units to accommodate updates in the NAN topology. After receiving a Hello message, each \(SM_{ng}\) inserts information about its neighbour in the Neighbours table. These tables can be optionally sent to the NOC, so that it has a total view of the NAN.
# A New GNSS Single-Epoch Ambiguity Resolution Method Based on Triple-Frequency Signals
###### Abstract  
Fast and reliable ambiguity resolution (AR) has been a continuing challenge for real-time precise positioning based on dual-frequency Global Navigation Satellite Systems (GNSS) carrier phase observation. New GNSS systems (i.e., GPS modernization, BDS (BeiDou Navigation Satellite System), GLONASS (Global Navigation Satellite System), and Galileo) will provide multiple-frequency signals. The GNSS multiple-constellation and multiple-frequency signals are expected to bring great benefits to AR. A new GNSS single-epoch AR method for a short-range baseline based on triple-frequency signals is developed in this study. Different from most GNSS multiple-constellation AR methods, this technique takes advantage of the triple-frequency signals and robust estimation as much as possible. In this technique, the double difference (DD) AR of the triple-frequency observations is achieved in the first step. Second, the triple-frequency carrier phase observations with fixed ambiguities are used with the dual-frequency carrier phase observations to estimate their ambiguity. Finally, to realize reliable GNSS single-epoch AR, robust estimation is involved. The performance of the new technique is examined using 24 hours of GPS/GLONASS/BDS observation collected from a short-range baseline. The results show that single-epoch AR of the GNSS signals can be realized using this new technique. Moreover, the AR of BDS Geostationary Earth Orbit (GEO) satellites' observations is easier than are those of the Medium Earth Orbit (MEO) and Inclined Geosynchronous Satellite Orbit (IGSO) satellites' observations.  
GNSS; ambiguity resolution; single-epoch; multiple-constellation; triple-frequency signal; short-range baseline +
Footnote †: journal: Geo-Informational Journal of Geo-Informational
## 1 Introduction
Two levels of stratification were analyzed and EIS with different superimposed DC-currents were performed to get reliable spectra [18]. To understand the observations, a spatially-resolved equivalent electrical circuit (srEEC) model was implemented to simulate the inhomogeneous current distribution and the consequently different State-of-Charge (SoC) between bottom and top of the battery. Dependencies to current, SoC and acid concentration were included and mass transport processes were linearized. Furthermore, EIS measurements were performed to parameterize the srEEC. Models including descriptions of mass transport processes were already described for the simulation of current distribution in case of stratification [9,13,19]. Such models have large computing times and a detailed modeling of mass transport is not required here. Because of the adjustment of the stratification independently to the SoC the initial conditions of the test cell were well-known and the short measurement durationsallowed the regular determination of conditions, so that processes, which changed the conditions during measurements, could be linearized. [PERSON] et al. used also a srEEC for the modeling of current distribution. Different local OCV and the acid concentration dependency of the acid resistance were considered. However, no dependency of charge-transfer on acid concentration were included and no parameters values of the model were provided [20].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Deltoid Muscle Anterior _MA_**} & \multicolumn{2}{c}{**\(\Delta\)**} & \multicolumn{2}{c}{**Deltoid Muscle Posteriori _MA_**} & \multicolumn{1}{c}{\(\Delta\)**} \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \cline{2-6}  & **ON** & **OFF** & & **ON** & **OFF** & \\ \hline Participant 1 & 121.88 & 168.53 & 27.68 & 64.27 & 67.75 & 5.14 \\ Participant 2 & 109.19 & 166.75 & 34.52 & 59.35 & 58.98 & 0.62 \\ Participant 3 & 112.74 & 137.98 & 18.29 & 64.06 & 72.84 & 12.06 \\ Participant 4 & 109.68 & 119.15 & 7.95 & 109.80 & 140.93 & 22.09 \\ Participant 5 & 40.54 & 46.30 & 12.43 & 85.49 & 94.56 & 9.59 \\ Participant 6 & 37.85 & 44.89 & 15.67 & 95.19 & 97.20 & 2.07 \\ Participant 7 & 41.19 & 50.15 & 17.87 & 54.73 & 67.49 & 17.87 \\ Participant 8 & 49.80 & 57.92 & 14.03 & 66.65 & 94.96 & 29.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of the average muscle activity of deltoid muscle anterior and deltoid muscle posteriori for respective participants, where _MA_—muscle activity, ON—activated anti-rollback system, OFF—deactivated anti-rollback system, \(\Delta\)—a percentage difference between muscle activity measured with the activated anti-rollback system and the deactivated anti-rollback system. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{_Push_} & \multicolumn{2}{c}{\(\Delta\)} & \multicolumn{2}{c}{\(\Delta\)} & \multicolumn{2}{c}{\(\Delta\)} & \multicolumn{2}{c}{\(E\)} & \multicolumn{2}{c}{\(\Delta\)} \\ \cline{2-10}  & **(-)** & **(-)** & **(\%)** & **(-)** & **(-)** & **(\%)** & **(J)** & **(J)** & **(\%)** \\ \cline{2-10}  & **ON** & **OFF** & & **ON** & **OFF** & & **ON** & **OFF** & \\ \hline Participant 1 & 0.61 & 0.62 & 1.61 & 1.10 & 1.19 & 7.56 & 0.60 & \(-\)1.36 & 144.12 \\ Participant 2 & 0.53 & 0.64 & 17.19 & 1.10 & 1.31 & 16.03 & 3.37 & 1.24 & 171.77 \\ Participant 3 & 0.62 & 0.76 & 18.42 & 0.86 & 0.62 & 38.71 & \(-\)3.28 & \(-\)1.83 & 79.23 \\ Participant 4 & 0.69 & 0.63 & 9.52 & 0.58 & 0.95 & 38.95 & \(-\)3.41 & \(-\)0.50 & 582.00 \\ Participant 5 & 0.63 & 0.55 & 14.55 & 0.61 & 0.98 & 37.76 & 0.54 & 0.63 & 14.29 \\ Participant 6 & 0.64 & 0.59 & 8.47 & 0.57 & 1.02 & 44.12 & 0.27 & 0.79 & 65.82 \\ Participant 7 & 0.60 & 0.59 & 1.69 & 0.89 & 1.13 & 21.24 & 0.92 & 0.75 & 22.67 \\ Participant 8 & 0.61 & 0.57 & 7.02 & 0.91 & 0.82 & 10.98 & 1.37 & 0.12 & 1041.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of the average values of the push phase share in the entire propelling cycle, a coefficient of the generated power and loss power ratio in the propelling cycle \(\lambda\), total mechanical energy for a single propelling cycle \(E\). Where: \(\Delta\)—a percentage difference between the parameter measured with the deactivated anti-rollback-system and the activated anti-rollback system, ON—activated anti-rollback system, OFF—deactivated anti-rollback system.
## 3 Results and Discussion
### Antioxidant Activity and Phenolic Content
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Solvent**} & **Sideritis** & **Thyme** & **Mean** & **Sideritis** & **Thyme** & **Mean** \\ \cline{2-9}  & \multicolumn{4}{c}{**FRAP mg/g Trolox**} & \multicolumn{4}{c}{**DPPH\%**} \\ \hline ACT & 1.66 ba & 1.49 b & 1.57 A & 86.07 b & 55.67 e & 70.87 C \\ MET & 1.16 c & 1.82 a & 1.49 A & 87.98 a & 77.26 d & 82.62 B \\ EtOH-H\({}_{2}\)O & 1.09 dc & 1.47 b & 1.28 B & 85.75 b & 83.06 c & 84.4 A \\ EtOAc & 0.88 d & 1.01 d c & 0.94 C & 85.75 b & 55.16 e & 70.46 C \\ \hline \hline \multicolumn{9}{c}{Acidum ascorbicum} & \multicolumn{4}{c}{90.03} \\ \hline Mean & 1.2 B & 1.45 A & 86.39 A & 67.79 B \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Antioxidant activity of the tested plant extracts. and Folin-Ciocalteu methods. MeOH performed the best in extracting total polyphenols content (8.10, 5.95, and 5.20 mg GA/g DW for thyme, sage, and marjoram, respectively). Furthermore, the thyme MeOH extract was reported to have the highest antioxidant capacity, exceeding that of other plants, \(\alpha\)-tocopherol, and BHA. This was corroborated by our study, which found that the thyme MeOH extract had the highest total phenolic acids content (141.09 mg CA/g EX) and antioxidant activity-FRAP (1.82 mg/g Trolox). Alcoholic extracts of thyme are rich in polyphenols and possess strong antimicrobial properties [38]. [PERSON] et al. [32] determined the total polyphenols content to be 8.89 mg GAE/g-DW and the total flavonoids content to be 3.87 mg\(\cdot\)QE/g\(\cdot\)DW for thyme MeOH extract, with thymol, apigenin, rosmeary acid, and carvacrol being the most abundant phenolic compounds for thyme MeOH. EtOAc and hexane extract from thyme leaves were tested for their activity, with both (especially the former) showing antibacterial action [28].
# Review of Photogrammetric and Lidar Applications of UAV
## 6 Discussion
### Discussion on Applications of UAVs
#### 6.2.4 Natural Hazards and Geohazards
From a flight height of 100 m, with a forward overlap of 90%, a lateral overlap of 50-60%, and a flight speed of 4 m/s, they acquired a series of aerial images, which they processed in Pix4D Mapper to produce an ortho-mosaic and a DSM of the river channel morphology. In hydrology, UAV photogrammetry is used in the same way as lidar technology is used to estimate the water level or reconstruct the topography of the river. The use of lidar is based on information about signal intensity. From the water surface, the signal reflectivity is very low; therefore, the resulting point cloud models drop out, and it is very easy to detect the boundary between water and land. With UAV photogrammetry in hydrology, complete surface images are captured from the camera, and a DSM or ortho-mosaic is then created. The choice of method depends on the observed territory, finances, or the resulting output.
# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
## 3 Results
### Elasticity of WUE to SM
In different climatic zones (Figure 4d), the impact of _SM_ on _WUE_ via the _NPP_ pathway showed a positive effect in MTMP, WTMP, and PTMP, with elasticity coefficients of \(0.74\), \(0.50\), and \(0.33\), respectively. In contrast, the impacts of _SM_ on _WUE_ via the _NPP_ pathway were negative in the other climatic zones, and the elasticity coefficients ranged from \(-1.39\) to \(-0.12\). Generally, the area percentage of the region in which _SM_ had a positive effect on _WUE_ via the _NPP_ pathway ranged from \(28.10\%\) to \(63.44\%\) in different climatic zones. Under the dual effects of _ET_ and _NPP_ pathways, the final _SM_ significantly impacted _WUE_. The elasticity coefficient of _WUE_ to _SM_ ranged from \(-74.01\) to \(39.81\), with an average value of \(-2.87\) (Figure 4e). Because the impact of _SM_ on _WUE_ via the _ET_ pathway was completely negative, the comprehensive influence of _SM_ on _WUE_ also showed a more remarkable negative effect. In the terrestrial ecosystem, _SM_ negatively and positively impacted _WUE_ in \(84.62\%\) and \(15.38\%\) of the total area, respectively. For each climatic zone, the average value of the effect of _SM_ on _WUE_ was also negative, and the negative effect was the most significant in the NST, which had an average elasticity coefficient of \(-4.27\). The average value was the lowest in PTMP, which had an average elasticity coefficient of \(-1.93\). For the other climatic zones, the elasticity coefficient of _WUE_ to _SM_ ranged from \(-4.03\) to \(-2.01\). Generally, the _NPP_ pathway mainly determined the trend of change in _WUE_ in different climatic zones, which was because the correlation coefficient between \(\frac{\partial\textit{WUE}}{\partial\textit{NPP}}\) and \(\frac{\partial\textit{NUE}}{\partial\textit{NPP}}\frac{\partial\textit{NPP}}{ \partial\theta}\) (\(R=0.84\)) was more significant than that between \(\frac{\partial\textit{WUE}}{\partial\theta}\) and \(\frac{\partial\textit{WUE}}{\partial\textit{ET}}\frac{\partial\textit{ET}}{ \partial\theta}\) (\(R=0.69\)). In contrast, the _ET_ pathway mostly dominated the influence level (absolute value of the elasticity coefficient of _WUE_ to _SM_) of _SM_ on the _WUE_ (Figure 4b,d,f).