esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 2 Creation of Kinematic Pair Clearance Model
### Creation of Kinematic Model for Revolute Joint Clearance
#### 2.2.1 Collision Force Modeling in the Normal Direction  
Due to the presence of clearance in kinematic pairs, the kinematic constraints between the components at the kinematic pairs transform into force constraints. Modeling of the collision forces at the clearances is the basis for study of kinetic properties of the mechanism with clearances. This paper primarily focuses on an in-depth study of the L-N model, which is further optimized and improved upon the basis of the Hertz model, thereby providing more precise theoretical support at the academic level for dynamic analysis of machines with clearances.  
The Hertz model equation is expressed as:  
\[F_{n}=K\delta_{R}^{n} \tag{11}\]  
A more comprehensive consideration of energy losses in the L-N model, represented by the normal force, is represented by:  
\[F_{n}=K\delta_{R}^{n}+D\dot{\delta}_{R} \tag{12}\]  
where \(F_{n}\) represents the normal impact force, \(\delta_{R}\) represents the impact depth, \(n\) is the correction coefficient, \(K\) denotes the impact stiffness, \(D\) denotes the damping factor, and \(\dot{\delta}_{R}\) represents speed of penetration depth.  
In Equation (12), the expression for \(K\) is given by:  
\[K=\frac{4}{3(\sigma_{1}+\sigma_{2})}\bigg{(}\frac{R_{1}R_{2}}{R_{1}+R_{2}} \bigg{)}^{\frac{1}{2}} \tag{13}\]  
where \(\sigma_{1}=\frac{1-v_{1}^{2}}{E_{1}},\sigma_{2}=\frac{1-v_{2}^{2}}{E_{2}}\), \(v_{1}\) and \(v_{2}\) represent the Poisson's ratios of materials, and \(E_{1}\) and \(E_{2}\) denote their elastic moduli.  
The expression for \(D\) is given by:  
\[D=\frac{3K(1-c_{e}^{2})\delta_{R}^{n}}{4\dot{\delta}^{(-)}} \tag{14}\]  
where \(c_{e}\) represents the recovery factor and \(\dot{\delta}^{(-)}\) indicates the original shock speed at the collision point.
## 3 Simulations
### Results  
#### 3.2.1 Contrast Recovery  
In Figure 3, the contrast recovery convergence with MLEM updates is shown for TOF data with \(CTR=750\) ps and background diameter \(D=275\) mm. It can be appreciate how, with TOF, convergence speed does not depend on the object position within the background and how higher \(\beta\) values result in faster convergence, as predicted by Equation (10).  
Contrast recovery values at 50 updates for contrast activity \(\beta=2.0\) are shown in Table 1 for non-TOF and TOF conditions corresponding to different \(D\) and CTR values. As expected, in non-TOF, the CR strongly depends on the background dimension. With TOF this dependence disappears, while slower convergence with worse CTRs can be observed.  
In Table 2, CR values at MLEM update numbers provided by rule (14) are shown. On TOF data, almost identical CR values are found in all the conditions. The update number reduces by a factor 3 passing from 750 ps to 250 ps. The rules appears working properly also on non-TOF data, where matched C.R values are achieved, at the expense however of a larger number of iterations (up to 185 for the largest background).  
Figure 2: True image of the simulated phantom for background diameter \(D=275\) mm.
## 3 Methods
### Accuracy Assessment  
To evaluate the quality and precision of the computed DEM, it is recommended to perform DEM of differences (DoD) analyses using, if available, independent 3D reference data in areas that are predominantly stable. This approach was commonly employed in related studies (see Section 2). It should be noted that this step is not part of the _Planet4 Stereo_ pipeline. To address horizontal and vertical shifts usually inherent in DEMs, we employ _demcoreg_, a library of tools developed by [28]. Removing relative offsets is essential for reliable 3D change detection and, equally, for the accurate evaluation of the calculated PlanetScope DEMs. Within _demcoreg_, we use a slightly adapted version of the _dem_align_ tool, which implements the co-registration algorithm described by [4]. Glacier regions are excluded using outlines from RGI v7.0. For consistency, the reference DEM and the calculated PlanetScope DEM are resampled to the average resolution of both datasets. This is achieved through statistical analysis, using key metrics such as the median, mean, standard deviation (StdDev), and NMAD.
## 2 System Model  
When it comes to the system model of localization and sensing, there are four types of networks to be discussed, namely, wireless localization networks, target sensing networks, single-slot static ISAL networks [25], and multi-slot dynamic ISAL networks. Considering the complexity of the multi-slot networks, this part discusses the dual-slot (\(N=2\)) dynamic ISAL networks instead [26].  
The general system description can be seen in Figures 1-4. Assume that there are \(N_{\mathrm{a}}\) anchors, \(N_{\mathrm{r}}\) radars, and \(N_{\mathrm{t}}\) targets in each network, where the anchors' positions are known and determined. In the dual-slot dynamic ISAL networks, the radars' and targets' positions remain unchanged in a single time slot but change in different time slots. Anchors and radars called active nodes are both considered to be able to transmit and receive signals, while targets called passive nodes can only reflect signals.  
Figure 1: Wireless localization system model with spatial cooperation, where the solid red and blue lines, respectively, represent the anchor transmission link and the radar transmission link.  
Figure 3: ISAL system model with spatial cooperation, where the solid red, blue, and black lines, respectively, represent the anchor transmission link, the radar transmission link, and the reflection link.  
Figure 2: Target sensing system model, where the solid red, blue, and black lines, respectively, represent the anchor transmission link, the radar transmission link, and the reflection link.
## 4 Array Processing
The integration time, \(T_{int}\), must be significantly greater than the propagation time across the array \(\left(T_{int}>\frac{da_{max}}{c}\right)\)[15]. The second requirement on CPI is determined by the bandwidth of the input signals and the shape of their temporal spectra. When the product \(B\cdot T_{int}\) is large enough and the input signals' temporal spectra are almost flat, the frequency-domain snapshot model is usually applied [15]. The steering vector of a signal impinging from a direction \(\phi_{sa,i}\) is defined in Equation (7), where \(f_{s}\) is the sampling frequency, \(f_{p}\) is the carrier frequency, \(N_{CAF}\) is the number of samples in a CPI and \(c\) is the speed of light. \[\mathbf{a}[p,\phi_{sa,i}]=\left[e^{j\frac{2\pi}{c}\cdot\left(f_{s}\frac{p}{N_{ CAF}}+f_{p}\right)d_{1}sin(\phi_{sa,i})},e^{j\frac{2\pi}{c}\cdot\left(f_{s} \frac{p}{N_{CAF}}+f_{p}\right)d_{2}sin(\phi_{sa,i})},...,e^{j\frac{2\pi}{c} \cdot\left(f_{s}\frac{p}{N_{CAF}}+f_{p}\right)d_{N}sin(\phi_{sa,i})}\right]^{T} \tag{7}\]  
Array signal processing allows the generation of multiple beams pointing to a set of azimuth directions, \(\mathbf{\Phi}=\left\{\phi_{sa,1},\phi_{sa,2},...,\phi_{sa,N\phi_{ua}}\right\}\), using appropriate weight vectors, \(\mathbf{w}[p,\phi_{sa,i}]\). The resulting signal after beamforming processing is expressed in Equation (8), where superscript \(H\) represents the Hermitian conjugate operation. \[\mathbf{y}[m,p,\phi_{sa,i}]=\mathbf{w}[p,\phi_{sa,i}]^{H}\cdot\mathbf{s}_{s}[m,p] \tag{8}\]
###### Abstract  
The geochemical maps and geochemical anomaly maps produced based on the data in the databases of the Regional Geochemistry-National Reconnaissance (RGNR) and the National Multipurpose Regional Geochemistry Survey (NMPRGS) projects have played a crucial role in China's geochemical exploration. A geochemical survey of the Mojiang area, Yunnan Province, China, has been completed and reveals potential new regions for Ni exploration related to occurrences of serpentinite melanges. The geochemical maps and geochemical anomaly maps need to be drawn in this area. Traditional geochemical maps, heavily dependent on data quantity, are less suitable for consistent comparisons across distinct regions and elements. Here, a fixed value method is proposed to contour the Ni geochemical map on 19 levels, which is convenient for the comparison among elements. On the geochemical maps, the two known Ni deposits are located in a region with Ni surely screening risk level (on the national standard of pollution risk of heavy metals in China) and a region with Ni economic level (Ni as an associate or main economic metal on the national standard of Ni deposit in China), respectively. In addition, we have determined that the Sn and Li levels in this area are at (low or high) background levels compared to other regions. Then, the method of seven levels of classification, which is also suitable for the comparison across different areas or elements, is used to draw the geochemical anomaly maps in the Mojiang area. On the anomaly maps, the two known Ni deposits are located in the regions with Ni anomaly levels not less than four, while the anomaly areas of Sn and Li are sporadic, with anomaly levels not larger than two in this area. These consistent results with the known facts of Ni, Sn, and Li deposits in the Mojiang area not only consolidate the roles of geochemical maps and geochemical anomaly maps but also illustrate the comparison among elements in mineral exploration. Furthermore, we predicted three Ni potential regions in the Mojiang area on the geochemical survey.  
f _Applied sciences_ Article
## 6 Experiments
### Selecting Background Knowledge Configurations with Low-Entropy Feature Frequency  
To explore the impact of varying entropy thresholds, we considered a range of values for the percentage threshold, denoted as \(n\%\), from 10% to 50%. This range enabled us to assess the effects of selecting more or less data to restrict the background knowledge instances used to evaluate the risk.  
We examined background knowledge configurations with knowledge sizes of 1 and 2, representing different levels of background knowledge available to an adversary. This variation allowed us to investigate the influence of knowledge and background knowledge on re-identification risk.  
The features under consideration in our experiments were location and time. By examining these features, we aimed to comprehensively evaluate the efficacy of targeting low-entropy values in different dimensions of trajectory information. Incorporating these variations into our experimental design enabled us to conduct a comprehensive analysis of the impact of entropy-based filtering on privacy risk across multiple datasets, producing valuable insights into the effectiveness of this approach in reducing re-identification risk computation.  
To quantify the divergence in risk distributions between the two evaluation approaches, we used the Kolmogorov-Smirnov (KS) test [18] and \(p\)-value. The test compares the distribution of a sample to a theoretical distribution to determine if significant differences exist. The Ks-statistics value and \(p\)-value from the ks test indicate whether the risk distributions of the **Original** and proposed entropy methods are statistically different. A low \(p\)-value suggests significant differences, while a high \(p\)-value suggests similarity. On the other hand, a low statistics value suggests that the distributions are more similar, and a high value suggests that values are different.
## 3 Results
### Impact of Vegetation Types and Elevation Changes on CUE  
The Three Rivers Source region is a quintessential high-altitude region in China, located in the south-eastern expanse of the Qinghai-Tibet Plateau. The altitudes in this area range from 1960 m to 6520 m, with an overwhelming 92.14% of the area falling  
Figure 6: Spatial distribution of correlation coefficient and significance test between vegetation CUE and temperature (**a,b**), precipitation (**c,d**) and evapotranspiration (**e,f**) in Three Rivers Source region.  
between 3500 m and 5500 m. The vegetation within the TRSR can be broadly classified into six different categories: alpine vegetation, steppes, meadows, forests, scribs, and crops. In particular, steppes, meadows, alpine vegetation, and scribs dominated the landscape. They accounted for about 22.31%, 56.89%, 10.16%, and 6.15% of the total vegetated area, respectively (Figure 7).  
In view of the key role of vegetation in the modulation of carbon sequestration capacity, its influence on CUE is essential. CUE dynamics are significantly influenced by structural differences in vegetation and its response to climatic changes. In terms of carbon sequestration capacity, the vegetation types are ranked from highest to lowest (Figure 8a): alpine vegetation (0.8673), steppes (0.8658), meadows (0.8488), and scribs (0.8305). A nuanced pattern is revealed as the elevation gradient interacts with the CUE dynamics. Specifically, CUE demonstrates an initial decline followed by an ascent concerning elevations. Below 3500 m, increases in elevation correlate with decreases in CUE, whereas between 3500 m and 6520 m, increases in elevation correlate with increases in CUE (Figure 8b). A clear pattern emerges when vegetation types are juxtaposed with CUE variations across altitudes. Steppes, meadows, and shrublands show a congruent trend, with an initial decrease in CUE followed by an increase. In contrast, alpine vegetation shows a unidirectional increase in CUE, consistent with its distribution mainly above the 3500 m threshold.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 2 Technology Advancements
### Technology Advancement Summary  
GNSS-R has been rapidly growing since 2013 UK-TDS-1 and 2016 CYGNSS launches. The contributions made by both missions have positioned GNSS-R as a novel and low-cost approach suitable for use in the Earth observation of massive constellations. Current airborne campaigns are integrating multi-constellation, dual-polarization, and multi-band GNSS-R receivers. Future spaceborne GNSS-R missions, such as HydroGNSS, with a tentative launch in Q4 2024/Q1 2025 [58], will also follow the same approach. Since the first implementations of the GNSS-IR ground-based receivers, multiple configurations and applications have been developed, allowing for localized assessments of soil moisture, vegetation, water-level, sea ice and snow information. Those local measurements enable applications at small scale, such as monitoring the water level of a lake or assessing soil moisture deficiencies of agricultural fields for farmers, but also enable the refinement of models.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
###### Abstract  
Currently, fault identification in most photovoltaic systems primarily relies on experienced engineers conducting on-site tests or interpreting data. However, due to limited human resources, it is challenging to meet the vast demands of the solar photovoltaic market. Therefore, we propose to identify fault types through the current-voltage curves of solar arrays, obtaining curves for various conditions (normal, aging faults, shading faults, degradation faults due to potential differences, short-circuit faults, hot-spot faults, and crack faults) as training data for the model. We employ a multi-input model architecture that combines convolutional neural networks with deep neural networks, allowing both the imagery and feature values of the current-voltage curves to be used as input data for fault identification. This study demonstrates that by inputting the current-voltage curves, irradiance, and module specifications of solar string arrays into the trained model, faults can be identified quickly using actual field data.  
solar power plant; I-V curve; data preprocessing; fault diagnostic; deep learning Article
## 4 Grouting Control Technology
### Application Effect  
To evaluate the effect of engineering application, borehole peeping and roadway deformation monitoring after grouting were conducted [47; 48]. The results are shown in Figures 16 and 17.  
Figure 16: Borehole peeping results after grouting. (**a**) Depth = 0.71 m; (**b**) Depth = 1.16 m; (**c**) Depth = 2.07 m; (**d**) Depth = 3.58 m; (**e**) Depth = 5.78 m; (**f**) Depth = 10.46 m. The one in the red circle is the slurry filling the cracks.  
Figure 16 shows that the overall integrity of the surrounding rock was improved after grouting. The fractures and voids were filled by the slurry and hence the surrounding coal rock mass became intact. The damaged coal rock has been effectively bonded and consolidated due to grouting. As shown from the curves in Figure 17, both the roadway deformation value and its range were decreased obviously than those before grouting. The maximum value of the two sides' convergency was about 750 mm before grouting, while it was about 560 mm after grouting, which totals a reduction of about 12.0%. The maximum value of the floor-to-roof convergency was about 550 mm before grouting, while it was about 400 mm after grouting, which totals a reduction of about 27.3%.  
It can be concluded from the comparative analysis that the grouting operation has greatly improved the broken surrounding rock of the reused roadway and reduced its deformation dramatically.
## 3 Results and Discussion
### Thermal Properties: Thermogravimetric, Differential Scanning Calorimetry and Conductivity Analysis  
#### 3.2.1 Thermal Stability  
The results obtained for both TGA and DSC were discussed to further characterize the damage responses of the samples. The temperature of the main degradation peaks obtained from TGA test for all samples are illustrated in Table 2. The data recorded for hemp fiber/PCL show a clear decrease of the degradation peak temperature (close to 10 \({}^{\circ}\)C) compared to the PCL matrix, especially marked for the highest ARs of 30 and 38. It is clearly evident that the closest value of degradation temperature, to the neat PCL sample with highest value, was obtained with the HF/PCL bioccomposite sample of AR of 26. The results of thermal stability of the neat PCL matrix are very similar to the result obtained by [PERSON] et al. [49] and significantly affected with the hemp fiber reinforcement, as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Samples** & **Main Degradation Temperature (\({}^{\circ}\)C)** \\ \hline AR\_00 & 433 \\ AR\_19 & 428 \\ AR\_26 & 432 \\ AR\_30 & 424 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermogravimetric analysis (TGA) measurements.  
Figure 4: Effects of aspect ratios on (a) hardness (b) reduced modulus.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
###### Abstract  
Nowadays, wireless multimedia sensor networks (WMSNs) are used in various applications. An energy-efficient and robust routing protocol is essential for WMSNs because the quality of service is important for traffic-intensive multimedia data, such as images and videos. A WMSN with multiple sinks allows cluster heads (CHs) to deliver the collected data to the nearest sink, thereby mitigating the delivery overhead. In this study, we propose a novel evolutionary-game-based routing (EGR) protocol for WMSNs with multiple sinks, in which the evolutionary game theory is exploited for selecting CHs. In EGR, an algorithm to mitigate data redundancy, based on the overlapping field of views of the multimedia sensor nodes, is also presented. This algorithm decreases the number of redundant transmissions, thereby increasing energy efficiency and network performance. According to the performance evaluation results of this study, the proposed EGR significantly outperforms the state-of-art protocols in terms of energy efficiency, end-to-end delay, packet delivery ratio, cluster formation time, and network lifetime.  
wireless multimedia sensor networks; routing protocol; evolutionary game theory; energy efficiency; end-to-end delay; packet delivery ratio Article
## 3 Modular Gait Generation Principle
### Driven Module Waves without Overlapping  
#### 3.1.1 Generation Algorithm for MG-I  
In the transmission mode of driven module waves without overlapping, the number of transmission segments \(n_{T}\) satisfies  
\[n_{T}=n_{td},\text{ or }n_{T}=n_{p} \tag{5}\]  
where \(n_{td}\) equals the number of actuated body segments in the U-DM, and \(n_{p}\) equals the sum of the actuated segments \(n_{pu}\) and nonactuated segments \(n_{pu}\) in the B-DM, defined as Equation (4).  
The state of the \(i\)th segment at the \(t\) moment is defined as \(s_{i}{}^{t}\), in which \"0\" represents the nonactuated state and \"1\" represents the actuated state. The states of all the segments can be composed as a state vector at a time point \(t\), denoted by \(z^{t}\). So, for the earthworm-like robot with \(TN\) body segments, there is  
\[s_{i}{}^{t}\in\{0,1\}, \tag{6}\] \[z^{t}=(s_{1}^{t},s_{2}^{t},s_{3}^{t},\cdots,s_{TN}{}^{t}).\]  
The state vector at each time moment can be determined according to the receding peristaltic wave locomotion characteristic of the earthworm. The locomotion gait \((z^{t_{0}},z^{t_{1}},z^{t_{2}},\cdots z^{t_{n}})\) can be successively obtained over a period of time. For the transmission of the driven module waves without overlapping, the period of the locomotion can be acquired as  
\[T=\mu(TN/n_{T})\Delta t \tag{7}\]  
where \(\Delta t\) is the interval between each state transmission, and \(\mu\) is a smallest positive integer, rendering the value \(\mu(TN/n_{T})\) to be an integer. Then, in the locomotion of an earthworm-like robot, the condition for a state switch is given as  
\[s_{i}^{t_{j}}=s_{Mod[i-1+(j+1)\cdot\pi_{T},TN]+1}^{t_{0}} \tag{8}\]  
where \(\text{Mod}[\cdot]\) expresses the modulo operation. According to the locomotion characteristics of a receding peristaltic wave and the state transmission rule, a gait generation algorithm for the driven module waves without overlapping is developed to generate the locomotion gait. The routine of the algorithm is shown in Figure 5. First, the parameters, \(\{k,n_{T},TN\}\), are fixed. Then, the verdict of the constraint conditions for Equation (3) or (4) is confirmed. Next, the initialization gait is given. Finally, the transmission for the segments can be acquired as per Equations (6) and (8) for MG-I. According to the algorithm routine in Figure 5, arbitrary gaits can be generated.