esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optical and Radio Frequency Measurement Analysis
### Optical Measurements Analysis
A deeper analysis of the measurements in Figure 6 confirms that the EDFA-based HPLS outperforms the Raman-based HPLS in terms of NF for identical power levels or for equivalent RF gains. However, when the RF gain and NF are compared (Table 1), the EDFA-based HPLS yields a gain of 21.3 dB at its lowest NF value, whereas for the Raman HPLS, the gain at the lowest NF is only 3.3 dB. To obtain a comparable RF gain of 21 dB (Figure 6) with the Raman-based link, its input HPLS power must be increased to 37 dBm for 5 km fiber, resulting in a significant NF to 46 dB in this case. Regarding the electrical power required at the remote station, both the 5 km Raman-based and EDFA-based HPLS configurations are viable options. Beyond these, only the 10 km EDFA-based configuration meets the minimum requirements. However, the 5 km Raman-based configuration exhibits higher NF levels, while the 5 km EDFA-based configuration has lower RF gain compared to the optimal values achieved by the 10 km EDFA-based HPLS. This suggests that the 10 km EDFA-based HPLS is also the most suitable for applications at distances of less than 10 km, offering a better trade-off between RF performances and power requirements for our application. More generally, these results highlight a key trade-off: while the EDFA-based HPLS exhibits lower noise figure degradation, it does so at the expense of higher input HPLS power or longer fiber length. This increase in SMF length reduces the power received at the remote station, which impacts the overall efficiency of the PoF system, and limits \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fiber Length** & \multicolumn{2}{c}{**5 km**} & \multicolumn{2}{c}{**10 km**} & \multicolumn{2}{c}{**15 km**} \\ \hline HPLS & Raman & EDFA & Raman & EDFA & Raman & EDFA \\ \hline \(NF^{M}\) (dB) & 41.8 & 33.3 & 45.6 & 31 & 42.6 & 34.7 \\ \hline \(\mathrm{G}_{\mathrm{RF}}^{M}\)(dB) & 3.3 & 5.5 & 2 & 21.3 & 2.6 & 8.8 \\ \hline \(SFDR^{M}\) (dB.\(\mathrm{Hz}^{2/3}\)) & 85 & 87.5 & 81.5 & 86.4 & 81.8 & 86.8 \\ \hline \(P_{HPLS}\) (dBm) & 35 & 37 & 33 & 36 & 32 & 33.4 \\ \hline \(P_{PPC}\) (dBm) & 31.5 & 34.3 & 28.4 & 32.6 & 26.2 & 29.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optical and RF metrics for the lowest NF in different setup configurations. Figure 9: Electrical spectra of the EDFA link for different HPLS powers. the feasibility of using a new LNA with a better NF, as the latter will consume more electrical power. Another limiting factor is the unconventional use of 1610 nm data transmission in the EDFA-based link, which may pose compatibility challenges for existing optical systems. Consequently, the Raman-based link presents a more practical alternative in scenarios where higher noise figure (NF) levels are not a critical constraint.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 6 Concluding Remarks  
A new method of motion error modeling and compensation for robotic manipulators was proposed in this paper. Forward and inverse kinematics models for a 6-DOF robotic manipulator were established. The Jacobian matrix and stiffness matrix were calculated based on the parameters of the joint modules. The mapping model of the 6 joint angles, generalized force at the end, and spatial stiffness was built. In the case that generalized force is known, the joint stiffness can be simulated for the motion errors of the manipulator end. Based on the simulation results, the error compensation method based on joint angles was established. In the simulation and experimental studies, a reasonable compensation effect was obtained. The compensation effect of the displacement errors in the X direction, Y direction, and Z direction reached 95.2%, 98.7%, and 96.2%, respectively. The findings are useful in posture selection and joint angle compensation for maintaining the optimal stiffness of the manipulator. The research work is also helpful for enhancing the overall stiffness and reducing the motion errors of the robotic manipulator, and hence, improving the working performance and accuracy of the manipulator. This improvement will extend the application of the robotic manipulator in precision machining and measurement.  
However, in this study, the speed of the manipulator was relatively slow during movement; therefore, the influence of static stiffness was mainly considered without considering the factor of speed. In future research, motion velocity will be considered in the compensation model to establish a more comprehensive relationship among the magnitude of velocity, acceleration, and compensation angle to further improve the error compensation effect and position accuracy during the movement of the manipulator.  
Writing--original draft preparation, experiments, and algorithms, X.H.; conceptualization, writing--review and editing, supervision, and funding acquisition, L.K.; writing--review and editing, G.D. All authors have read and agreed to the published version of the manuscript.  
National Key R&D Program of China (No. 2017 YFA0701200), National Natural Science Foundation of China (No.52075100), and Shanghai Science and Technology Committee Innovation Grant (No. 19 ZR1404600).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
An optical measuring method is presented, with which it is possible to measure and evaluate reflective cylindrical surfaces using a combination of deflectometry and subsequent reconstruction of the surface. The system is set up and tested on rolling elements of cylindrical roller bearings. However, it is not limited to this use case and can be applied to other cylindrical specular surfaces. The system distinguishes itself from existing test methods through the combination of high-resolution three-dimensional defect measurement with a very short recording time, and offers the possibility of introducing tolerance limits in the production of cylindrical specular surfaces. With this method, it is possible to record a defect with the dimensions of 1.3 mm by 1.8 mm within 5 s and to reconstruct the absolute depth. The resolution of the system is below 10 um in both X- and Y-direction, and is therefore sufficiently accurate to detect typical surface defects such as scratches, dents, or deformations. To validate the measured values of the system, the results of an artificially generated 10.35 um deep defect location were compared with those of a highly accurate mechanical stylus measurement.  
automated optical inspection; roller bearing; line scan camera; deflectometry; reconstruction; surface defects; quality control Article
## 4 Discussion
Nevertheless, the spatial scale mismatch between the actual flux tower footprint and the pixel of GOSIF product used in the comparisons is an issue. The coarse spatiotemporal sampling and low signal-to-noise ratio of the raw SIF data hamper their application over small or fragmented ecosystems. Fortunately, with the development of satellite SIF observations, TROPOMI within the TROPOSIF project funded by the European Space Agency can provide daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. Recently, a new global SIF dataset between May 2018 and April 2021 was produced [61], which could reduce the uncertainty of scale mismatch to a certain extent. Although this study developed the accurate SIF-based models to capture the variability in tower-based GPP, more forest flux sites across different climate zones and stand age structures are necessary to enhance the empirical model's robustness. In addition, a recent study [59] revealed that extreme weather events such as heatwave broke down the linearity between SIF and GPP of evergreen broadleaved trees. As illustrated in Figure 7, this work also found that the SIF lacked sensitivity to the influence of summer drought in 2003 on forest GPP in the QYZ site. However, the process-based BESS GPP captured the dramatic decline. Multi-year mean GPP observations in the subtropical conifer and broadleaved forests in the study reached up to 1799 \(\pm\) 95 g C m\({}^{-2}\) y\({}^{-1}\) and 1411 \(\pm\) 89 g C m\({}^{-2}\) y\({}^{-1}\), respectively, which were apparently larger than that of the boreal black spruce (812 g C m\({}^{-2}\) y\({}^{-1}\)) and Scots pine forest (about 1000 g C m\({}^{-2}\) y\({}^{-1}\)) [62; 63]. Recent work has also implied that the implementation of a series of aforestation and reforestation programmes could significantly increase vegetation cover condition and enhance the vegetation carbon sequestration capacity [42; 64]. Alternatively, deforestation could severely reduce the forest coverage, as well as the carbon fixation [65]. However, the explicit effect of various human-induced activities on regional or global carbon budget remained unclear. Our study provided a new framework to quantify the magnitude and dynamic of forest carbon fixation.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 2 Physical Components and Methods
### Components of the Ground Station  
An FSO-based ground station requires the ability to establish a communication link with a satellite. The FSO link should be acquired in a timely manner and maintained reliably throughout a given time window. Additionally, to increase the overall capacity of the suggested FSO network, there is a need for a significant number of ground stations. Therefore, we address the need to use cost-effective COTS products that are widely available and integrated.  
To answer the above requirements, we suggest herein a ground station model that is composed of the following components (see Figure 3):  
* **Telescope.** Designed for tracking the satellite.
* **Laser beam with a high-speed modulator.** The laser notifies and marks the ground station's location so that the satellite can adjust its orientation directly to it. After adjusting the satellite's orientation, the optical link can be created.
* **Camera.** A wide field of view (FOV) camera for accurate tracking. The FOV of the telescope's lens is very narrow, making it hard to create the initial link and perform fast tracking accurately. However, a wide-angle camera stationed and calibrated on the telescope can detect the satellite's position and enable the telescope's tracking.
* **Laser communication transmitter and receiver.** Enables FSO communication between the ground station and the satellite.
* **Image processing.** An image processing component is required for detecting the satellite in the images of the camera and the telescope. This component enables fast-tracking and location prediction even in cases in which the satellite is hidden (e.g., clouds).  
Figure 3: The ground station components: (A) telescope; (B) laser beam (laser beacon); (C) wide FOV camera; (D) FSO transmitter; (E) image processing component; and (F) gimbal mount with two degrees of freedom system (pitch and yaw) and aiming control.  
Robotic telescopes are primarily designed for tracking celestial bodies, such as stars or planets. Hence, they are less suitable for tracking fast targets such as LEO satellites. Any robotic telescope should undergo a calibration process once (and remain fixed in place). The calibration process involves focusing the telescope lens on the target and calibrating the telescope angles to a global angle system (i.e., lat/lon). Telescopes which are designed for satellite tracking should also be able to perform relatively smooth tracking in at least 2-3 degrees per second. Yet, unlike photography telescopes that require \"sub-pixel stability\", telescopes for FSO applications do not need to \"stand still\" on the target and may be even a single milliradian off the center of the satellite, as long as it is within the field of view of the telescope, which is commonly larger than half a degree (or 8.7 milliradian). The following subsection describes the telescope's tracking algorithm in FSO-based ground stations.
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 2 Methodology
### Coverage Hole Detection Technique with Low Energy Consumption  
Energy restrictions have a substantial influence on the network's performance and operating lifespan, and they can cause coverage holes in several ways. Low-battery nodes might abruptly fail, leaving coverage holes in their surrounding areas. These holes may widen when nearby nodes boost their energy consumption rates to make up for the coverage lost by the failing node. The operating lifespan of a sensor node is directly influenced by battery capacity. Shorter lives of nodes with smaller battery capacity may result in early failures and possible coverage holes when nodes go inactive [18].
## 3 Results and Discussion
### Factors Influencing Soft Tissues around Prama Implant according to Literature  
The PES values generally improved from the moment of crown cementation to the subsequent follow-up studies, suggesting the ongoing remodeling of the gingival-connective tissue. In particular two studies also showed marked improvements in the soft tissue thickness at after 1 year [21] and 2 years [20] in comparison to platform switch implants with the same ZirTi implant surface. A six-year study on Prama implants demonstrated the morphological stability of the soft tissues as indicated by the PESs [17]. The PESs improved over a 6-year evaluation period, showing that the gingival tissues (e.g., papilla and gingival architecture) adapted well to the neck and crown finishing line, independent of the MBL and gingival biotype. However, the 6-year PES values were slightly higher in those implants placed in healed crests (early and delayed groups) compared to those placed immediately after extraction, likely due to the soft tissue contraction in the immediate post-extraction sites [17].  
Hard tissue variation after several years from loading was not related to the timing of the implant placement. Bone maturation and crestal morphology stability were observed 2-3 years following placement, likely due to higher bone mineralization, widely demonstrated in several clinical studies [8; 26; 27; 29].
# Investigation of Load Environment and Bending Load Capacities of Aged Prestressed Concrete Sleepers
## 3 Evaluation of Bending Load Capacities of Aged PC Sleepers
### Test Methods
#### 3.1.2 JIS E 1201 Bending Test  
Figure 13 shows an outline of the bending test specified in the JIS E 1201 [18]. Positive and negative bending tests were conducted on the rail seat and center sections of the PC sleepers, respectively. The loading span was 700 mm. The flexural proof load \(P_{\text{cr}}\) and flexural fracture load \(P_{\text{u}}\) specified in JIS E 1201 were obtained using Equations (1) and (2), respectively. In the test, no cracks occurred when the \(P_{\text{cr}}\) was applied, and the PC sleepers did not break when the \(P_{\text{u}}\) was applied.  
\[P_{\text{cr}}=4\times(\sigma_{\text{CPr}}\times 0.9+f_{\text{ta}})\times Z/L \tag{1}\]  
\[P_{\text{u}}=4\times 3\sigma_{\text{pe}}\times Z/L \tag{2}\]  
where \(\sigma_{\text{CPr}}\) is the concrete stress due to the applied prestress force, \(f_{\text{ta}}\) is the allowable tensile stress (3 N/mm\({}^{2}\)), \(\sigma_{\text{pe}}\) is the concrete stress due to the effective prestress force (65% effective rate), \(Z\) is the section modulus, and \(L\) is the loading span (700 mm). Regarding test results, the relationship between the aging and accumulative passing tonnage (hereafter, the \"passing tonnage\") was determined for the crack generation and ultimate loads. The crack generation load was the load at which a crack was first detected via visual observation. The ultimate load was the maximum load, past which the PC sleeper could no longer hold the load due to bending.
# The Efficient Processing of Moving \(k\)-Farthest Neighbor Queries in Road Networks +
## 7 Empirical Evaluation
### Empirical Settings  
Table 3 describes the three real-world road networks [28] considered in our empirical evaluation. These road networks are part of the road network in the United States and vary in size. Ten query points were considered to measure query execution time. The query points regularly evaluate \(k\)FN queries, and each query point moves within a query segment. A data space can typically be considered as a unit square with its upper-left corner at the  
coordinate (0,0) and its lower-right corner at (1,1). Facilities were generated to simulate highly skewed point of interest distributions. First, the centroids \(c_{1},c_{2},\cdots,c_{m}\) are randomly populated within the data space, where \(m\) (\(1\leq m\leq 10\)) denotes the total number of centroids. The facilities around each centroid exhibit a Gaussian distribution with the mean indicating the centroid and the standard deviation set to \(\sigma=10^{-2}\). Table 4 lists the empirical parameter settings. Each of these parameters was determined through a series of experiments in which a single parameter was varied while the other parameters remained constant at the bolded default values.  
A conventional algorithm regularly evaluates \(k\)FN queries for the moving query point \(q\). This method was considered as a benchmark to evaluate MOFA's performance. All algorithms were implemented using C++ in the Microsoft Visual Studio development environment. All common subroutines of the algorithms were reused for similar tasks. For this empirical study, it was assumed that all indexing structures of the algorithms were stored in the main memory to rapidly process the M\(k\)FN queries. This assumption has often been used in other studies [22; 29] on online LBSs. Repeated evaluations were performed to compute the average time required to answer M\(k\)FN queries. The TNR method [30] was employed to quickly calculate the network distance between two points during query processing. The empirical study was executed on a computer running the Windows 11 operating system equipped with an 8-core processor (i9-9900) running at 3.1 GHz with 32 GB of RAM.
## 4 Robust Design Based on Cutting-Set Method
### Optimization Step under Finite Subsets of Users' Location  
Note that the worst-case locations of users are changed with the change of UAV trajectory in the optimization process. To simplify the problem, we assume that the worst-case locations of users are included in a finite subset of potential locations. Let \(\mathcal{S}_{n}\) denote the potential worst-case locations of the \(n\)th user. Then, we can express the finite subset of the \(n\)th user in the \(k\)th optimization as \(\mathcal{S}_{n}^{k}\subset\mathcal{S}_{n}\). For a given finite subset, we can transform the original problem to  
\[\begin{split}\textbf{P5}:&\quad\max_{\Theta}\min_ {n\in\mathcal{N}}\max_{\textbf{q}_{n}\in\mathcal{S}_{n}^{k}}E_{gain,n}\\ s.t.&\quad C1-C6,\end{split} \tag{22}\]  
while the problem **P5** can be solved by Algorithm 1.
# Characteristics of Electric Field Induced by Oscillating Metal Underwater Vehicle
## 2 Mathematical Model of Induced Electric Field for Metal Motion
### The Measurement System Shakes in the y-z Plane  
The metal navigation body measurement system shakes in the y-z plane, with the 1-1 sensor located at the coordinate origin, which is equivalent to the measurement system rotating around the 1-1 and 1-2 electrode pairs. The linear velocity of the 1-1 and 1-2 electrode pairs is zero, so the generated t interference-induced electric field is also zero, that is,  
\[E_{1r}=0 \tag{42}\]  
The 2-1 and 2-2 electrode pairs and the 3-1 and 3-2 electrode pairs are shaking around their midpoints in the y-z plane, respectively. Taking 2-1 and 2-2 electrode pairs as research objects, the distance from the center point of the system is \(l\), and it is close to electrodes 2-2, where the speed is \(\mathbf{v}_{2l}=\omega_{r}l\).  
The three components of the velocity can be found to be the following:  
\[v_{2 xl}=0 \tag{43}\]  
\[v_{2 yl}=-\omega_{r}l\sin(\omega_{r}t) \tag{44}\]  
\[v_{2 zl}=\omega_{r}l\cos(\omega_{r}t) \tag{45}\]  
The three components of the electric field at \(l\) for electrodes 2-1 and 2-2 can be obtained as follows:  
\[E_{2 xl}=v_{2 yl}B_{z}-v_{2 zl}B_{y}=-\omega_{r}l[B_{z}\sin(\omega_{r}t)+B_{y} \cos(\omega_{r}t)] \tag{46}\]  
\[E_{2 yl}=v_{2 zl}B_{x}-v_{2 xl}B_{z}=B_{x}\omega_{r}l\cos(\omega_{r}t) \tag{47}\]  
\[E_{2 zl}=v_{2 xl}B_{y}-v_{2 yl}B_{x}=B_{x}\omega_{r}l\sin(\omega_{r}t) \tag{48}\]  
Thus, the induced electric field on the 2-1 and 2-2 electrode pairs when the measurement system is swaying in the y-z plane is as follows:  
\[\begin{array}{l}E_{2r}=\int_{-\Delta l/2}^{\Delta l/2}\left[E_{2 yl}\cos( \omega_{d}t)+E_{2 zl}\sin(\omega_{d}t)\right]dl/\Delta l\\ =\int_{-\Delta l/2}^{\Delta l/2}B_{x}\omega_{r}ldl/\Delta l=0\end{array} \tag{49}\]  
Similarly, the induced electric field on the 3-1 and 3-2 electrode pairs when the measurement system is shaking in the y-z plane is as follows:  
\[E_{3r}=0 \tag{50}\]  
It can be obtained that the three components of the induced electric field output from the three-axis sensor when the measurement system is swaying at all three angles are as follows:  
\[E_{1}=E_{1d}+E_{1p}+E_{1r} \tag{51}\]  
\[E_{2}=E_{2d}+E_{2p}+E_{2r} \tag{52}\]  
\[E_{3}=E_{3d}+E_{3p}+E_{3r} \tag{53}\]  
Based on the above theoretical analysis, it can be concluded that the induced electric field output during the shaking of the three-axis measurement system is related to the center point of the shaking. The expression for the interference-induced electric field is obtained by shaking one sensor as the center point (always with the 1-1 electrode as the shaking center in Figure 1). By changing the shaking center, assuming that the shaking center is the center point of the measurement system (i.e., the intersection point of the three axes), the interference electric field output during the three-axis shaking is zero.