esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Strategy for Direct Teaching of Remote Center Motion
### Optimized Isotropic Sensitivity of Direct Teaching  
The initial values of the command generation function are roughly determined among the characteristics of the system, and those are adjusted by an optimization method through a pre-test, as it is difficult to quantify the operating sensitivity of direct teaching. In general, modeling of friction terms is required when designing impedance control algorithms. However, the friction model modeled for each joint often differs from the friction model in the assembled manipulator. Although an observer is sometimes designed to estimate friction models, it increases the computation of the controller. In this work, we use a more practical approach that is simpler by tuning the command function through a pre-test. Parameter initial values of the command generation function are defined by considering the mechanical and electrical characteristics of the fabricated prototype and the desired operating sensitivity. Assuming that the dead zone range, \(a\), is 0.2 N\(\cdot\)m and the maximum speed, \(b\), is 1 \(\times\) 10\({}^{-4}\) rad/ms, the slope \(k_{1}\) and \(k_{2}\) are determined as shown in Equation (12) so that the maximum speed can be reached with 1.5 N\(\cdot\)m torque.  
\[k_{desired}=\frac{1\times 10^{-4}}{1.5-0.2}=7.7\times 10^{-5}. \tag{12}\]  
After that, the velocity of pan/tilt motion and the generated reaction torque for arbitrary direct teaching are measured through a pre-test. The actual torque required for motion velocity is shown by contours from 18,466 data as shown in Figure 6a. The actual sensitivity of a robot differs from its intended sensitivity in amplitude and isotropy. An objective function is defined by the error norm between the actual slope and the required slope as shown in Equation (13), and the slopes of the command generation function are tuned by an optimization algorithm to minimize the objective function.  
\[\begin{split}\tau_{opt}=\left\{\begin{array}{ll}\tau_{meta}/ \alpha,&\tau_{meta}>0\\ \tau_{meta}/\beta,&\tau_{meta}<0\end{array}\right.,\\ obj\big{(}\alpha_{pun},\beta_{pun},\alpha_{tilt},\beta_{tilt}\big{)}= norm\Bigg{(}\frac{\sqrt{\alpha_{pun}^{2}+\omega_{2 ilt}^{2}}}{\sqrt{\tau_{ opt,pun}^{2}+\tau_{opt,tilt}^{2}}}-k_{desired}\Bigg{)}/n\,\end{split} \tag{13}\]  
where \(\tau_{meta}\) and \(\tau_{opt}\) are measured torque and optimized torque respectively, and \(\alpha\), \(\beta\) are tuning parameters to be multiplied by each slope \(k_{i}\). \(\omega\) is motion velocity, and \(n\) is the number of data. As a result, the error is reduced and the values of the slope are tuned as shown in Equation (14) and Table 2, resulting in the result shown in Figure 6b. Figure 6 shows the results of a verification test, and the error norm is decreased to 1.32 \(\times\) 10\({}^{-4}\) from the pre-test result.  
\[k_{1}=\alpha\cdot k_{initial},\ \ \ k_{2}=\beta\cdot k_{initial}\ . \tag{14}\]  
Figure 5: Relationship between input torque and robot velocity command.
## Materials and Methods
### Leaf-Level Measurements of the Photosynthetic Carbon Uptake Rate and Stomatal Conductance  
CO\({}_{2}\) and water exchange processes between the vegetation and atmosphere were characterized at different scales. Gas exchange measurements at the leaf level represent the net photosynthetic CO\({}_{2}\) uptake rate and transpiration rates of different individual leaves within the canopy. These measurements were complemented at the canopy level by eddy covariance (EC) tower measurements, representing exchange processes of the entire field.  
Leaf-level gas exchange was measured using the Li-COR 6400 portable photosynthesis system (Li-COR, Lincoln, NE, USA). The CO\({}_{2}\) level of the inlet air was maintained in a steady state at 390 ppm. The light response curves of the net photosynthetic CO\({}_{2}\) uptake rate (A) and the stomatal resistance (rs) [24; 25] were measured in the field using an artificial light source of the manufacturer. These measurements were performed at photosynthetic photon flux density (PPFD) levels of 2000, 1000, 500, 200, 100, 50, 20, 10 \(\upmu\)mol m\({}^{-2}\cdot\)s\({}^{-1}\) and in dark conditions. Most measurement protocols for gas exchange measurements with the Li-COR 6400 refer to laboratory measurements starting the light response curves in the dark adapted state. Under field conditions, plants are already in a light-adapted state and must be dark regulated for at least 30 min for light curves. To minimize the waiting time between individual measurements, the light curves were started at 2000 \(\upmu\)mol m\({}^{-2}\cdot\)s\({}^{-1}\) and were then down regulated to dark adaptation. Air humidity and temperature inside the assimilation chamber were adjusted to ambient conditions. Determination of a single light response curve took approximately 45 min; therefore, up to twelve measurements of one leaf per individual plant could be performed between 07:00 and 16:00 UTC on each observation day. On DOY 127, fully developed leaves from the upper vegetation layer of winter wheat were taken for gas exchange measurements. On DOY 176, since leaves in lower canopy layers had already started senescence, the flag leaf, which is the uppermost leaf on the term, was used for the measurements. For sugar beet, mature leaves were present on all days, and the gas exchange measurements were performed on randomly selected mature leaves of the external ring of the sugar beet rosette.  
To characterize the potential photosynthetic performance of different plants during the day, the maximum net photosynthetic CO\({}_{2}\) uptake rate (A\({}_{\rm max}\)) and the maximum stomatal resistance (rs\({}_{\rm max}\)) were estimated from each light response curve of gas exchange measurements using a single exponential fit (Figure S1 supplementary materials). In case the leaves did not adapt fast enough to the high light conditions, rs data were excluded from the dataset.
# Turning Video Resource Management into Cloud Computing
## 3 The Video Resource Management System in HDFS-Based Cloud Computing
### Key Algorithms  
#### 3.3.1. Pseudo Codes for Uploading Video Files from Local Servers to HDFS Clusters  
public void copytoHDFS()  
{ //Create configuration property object  Configuration conf = new Configuration(); //Fetch Hadoop configuration information  conf.addResource(new Path(str_conf)); //Create file sysystem object  FileSystem hdfs = FileSystem.get(conf); //Get absolute path of local files  Path src = new Path(src_conf); //Upload files to specified HDFS directories  Path dst = new Path(dst_conf); //Upload files  hdfs.copyFromLocalFile(src, dst); }
# Laser Sintering of TiO\({}_{2}\) Films for Flexible Dye-Sensitized Solar Cells
## 3 Experimental Results and Discussion
### IR Laser Sintering
The charge collection efficiency is determined by the dynamic competition between the internal electron transport and recombination [34]. The longer the electron lifetime is, the higher the charge collection efficiency is. Figure 5c shows the effect of incident light intensity on the apparent electron lifetime, and indicates that the IR laser sintering increases the electron lifetime between the TiO\({}_{2}\) conduction band and electrolyte redox potential, which results in an improvement of the charge collection efficiency. To further study the effect of IR laser sintering on P25 TiO\({}_{2}\) nanoparticle films, the micro Raman spectroscopy technology is applied to detect and analyze the IR laser sintered TiO\({}_{2}\) nanoparticle films (Figure 5d). Since commercial P25 consists of about 80% anatase and 20% rutile TiO\({}_{2}\) phase [35], for the Raman spectrum of the original sample, the peaks, such as 144, 399, 519 (overlap with 515), and 639 cm\({}^{-1}\), represent anatase phase [36; 37], and the Raman peaks belonging to the rutile phase (235 cm\({}^{-1}\), 445 cm\({}^{-1}\) and 612 cm\({}^{-1}\)) are weak. However, after the IR laser sintering, the characteristic peaks of rutile are significantly enhanced, indicating that the proportion of rutile phase in the TiO\({}_{2}\) nanoparticle film is increased. This reveals that one part of the nanoparticles in the P25-type TiO\({}_{2}\) film is transformed from anatase phase to rutile phase by the laser sintering. In the conventional high-temperature sintering process, to realize the transformation from anatase phase to rutile phase, TiO\({}_{2}\) nanoparticles are required to be heated above 550 \({}^{\circ}\)C for several hours, so the IR laser realizes the high-temperature sintering of TiO\({}_{2}\) films on low melting point flexible substrates. In the aspect of optical properties, the refractive index of rutile phase is higher than that of anatase phase [38], and rutile phase possesses a stronger scattering ability, it is widely used as a scattering layer in DSCs to increase the absorption rate of incident light [39; 40]. Therefore, the IR  
Figure 5: (**a**) Incident photon-to-electron conversion efficiency (IPCE) stability test, (**b**) current density-voltage, and (**c**) apparent recombination lifetime of the flexible dye-sensitized solar cells (DSCs) based on the laser sintered and unsintered TiO\({}_{2}\) films, (**d**) Raman spectra of the TiO\({}_{2}\) nanoparticle films before and after the laser sintering. laser sintering not only increases the charge collection efficiency, but also enhances the absorption of incident light. These two factors ultimately result in the improved performance of the flexible DSCs.
# The Cube Surface Light Field for Interactive Free-Viewpoint Rendering
## 5 Results and Discussion
### Time Performance
For example, the local light field fusion [6] limits the viewpoints of the light field in a local small range, while the viewpoint of our method is arbitrary in arbitrary positions and directions outside the cube surface. NeRF [15] takes a lot of time to render a single image while our method can reach more than 75 FPS on a personal computer with moderate configurations. Figure 7: The time performance of rendering the cube surface light field with various image resolutions. (**a**) rendering resolution is 512 \(\times\) 512, (**b**) rendering resolution is 1024 \(\times\) 1024, (**c**) rendering resolution is 2048 \(\times\) 2048, (**d**) rendering resolution is 4096 \(\times\) 4096. In addition, the proposed method is compared with a related method, i.e., 2 PP light field to study its performance. To ensure the fairness of this experiment, we use the same approach, i.e., CBSI to render the 2 PP light field. The difference is that the input data is no longer the cube surface light field images of the Cornell Box scene but the 2 PP light field images which viewpoints are co-planar and imaging planes are also located on a common plane. We report the average frame rate and memory usage of our method and 2 PP method when rendering 100 novel views with a resolution of \(2048\times 2048\) pixels, as shown in Table 1. The similar results should be extended to other resolutions and interpolations. As can be seen from Table 1, the average frame rate of 2 PP light field rendering is basically the same as our method, but the memory usage is less than the proposed method. This is because 2 PP light field only represent a subset of light rays in the scene, which reduces the total light field data amount but also limits the DOF of rendering viewpoints. On the contrary, by sampling all the light rays on the cube surface, our method can provide free-viewpoint rendering, even though the data amount is greater than the 2 PP light field. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Method**} \\ \cline{2-3}  & **2 PP Light Field Rendering** & **Cube Surface Light Field Rendering** \\ \hline Memory Usage (MB) & 128 & 384 \\ \hline Average Frame Rate (FPS) & 75 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of memory usage and frame rate between our method and the 2 PP light field. Figure 8: The time performance of rendering the cube surface light field using different interpolations. (**a**) NNI, (**b**) LL, (**c**) BLI, (**d**) BCI, (**e**) CBSI, (**f**) the average frame rate of each interpolation method.
###### Abstract  
Flexure-based micropositioning stages with high positioning precision are really attractive. This paper reports the design and analysis processes of a two-degree-of-freedom (2-DOF) flexure-based XY micropositioning stage driven by piezoelectric actuators to improve the positioning accuracy and motion performance. First, the structure of the stage was proposed, which was based on rectangular flexure hinges and piezoelectric actuators (PZT) that were arranged symmetrically to realize XY motion. Then, analytical models describing the output stiffness in the XY directions of the stage were established using the compliance matrix method. The finite element analysis method (FEA) was used to validate the analytical models and analyze the static characteristics and the natural frequency of the stage simultaneously. Furthermore, a prototype of the micropositioning stage was fabricated for the performance tests. The output response performance of the stage without an end load was tested using different input signals. The results indicated that the stage had a single direction amplification capability, low hysteresis, and a wide positioning space. The conclusion was that the proposed stage possessed an ideal positioning property and could be well applied to the positioning system.  
micropositioning; rectangular flexure hinge; two degrees-of-freedom; piezoelectric actuators 1
# Towards a Real-Time Description of the Ionosphere:
## 9 Final Analyses and Comparisons between IRI and IRTAM
This means that steep spatial gradients and fast time variations that are below the limits that can be resolved with the current spatial and temporal resolution of the URSI formalism would not be represented by IRTAM, even with an increased availability of assimilated data. Since steep spatial gradients and fast time variations are customary under specific Space Weather conditions, and the aim of data-assimilation methods is the reliable representation of such conditions, this poses serious limitations for the IRTAM model that its developers should bear in mind in the future.
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 4 Our Proposed Mechanism
### The Dynamic Change Method of Smart Devices and Information
#### 4.3.2 Device Information Update  
When the certificate information in a device is lost or expired, it is necessary to perform an update operation to refresh the device information. For example, let us take the device \(D_{i}\); its update process is as follows:  
First, the device generates a new public-private key pair. Device \(D_{i}\) picks a random number \(r^{\prime}_{i}\longleftarrow Z_{p}\) in \(Z_{p}\) as its new private key \(sk^{\prime}_{i}\). Then, it computes its new public key as \(pk^{\prime}_{i}=sk^{\prime}_{i}\cdot G\). Device \(D_{i}\) sends \(\{sig(pk^{\prime}_{i})sk_{i},t^{\prime}_{i},ID_{i}\}\) to \(M_{A}\) to perform the update operation for the device. Here, \(sig(pk^{\prime}_{i})sk_{i}\) means that device \(D_{i}\) signs the new public key with the old private key, \(t^{\prime}_{i}\) is the new timestamp, and \(ID_{i}\) is the identity information of device \(D_{i}\).  
After receiving the request, \(M_{A}\) verifies the legitimacy of the signature with the old public key of device \(D_{i}\). If it is not legitimate, \(M_{A}\) stops the update request. If it is legitimate, \(M_{A}\) generates a new certificate for device \(D_{i}\) with the following certificate structure.  
\[\begin{split}& Cert^{\prime}_{i}=Sig(ID_{i}||pk^{\prime}_{i}||t^{ \prime}_{i})_{sk_{A}}\end{split} \tag{2}\]  
The same \(M_{A}\) sets an expiration time \(T^{\prime}_{i}\) for the new certificate and updates the old certificate in the local database to the new one, synchronizing the expiration time of the certificate. \(M_{A}\) then computes the hash \(h_{0}(\mathit{Cert}^{\prime}_{i})\) of the new certificate and sends \(T^{\prime}_{i},h_{0}(\mathit{Cert}^{\prime}_{i}),\mathit{state}\) to the proxy node \(P_{A}\) of the blockchain. Here, \(\mathit{state}\) is also set to 1, indicating that the certificate is valid.  
Figure 2: Device joining flow chart.  
After that, \(P_{A}\) synchronizes the new certificate information to the federated chain and reaches consensus by consensus algorithm. Once complete, \(P_{A}\) feeds the synchronization result back to \(M_{A}\) and \(M_{A}\) sends the new certificate \(Cert_{i}^{\prime}\) to device \(D_{i}\). The device information is updated.
# Reliability of Automated Intracranial Volume Measurements by Synthetic Brain MRI in Children
## 4 Discussion
This highlights the substantial clinical importance of the early detection of hydrocephalus or decreased brain volume in terms of beginning brain atrophy [34; 35]. Even in cases that are currently not amenable to treatment, reliable radiological methods are urgently required for initial diagnostic confirmation and possibly necessary follow-up examinations. The strengths of our study rely on its comparison of meticulously generated manual segmentations of the intracranial compartments, CSF, and BPV, serving as a reference method described in the literature [18], and their comparison with automated SyMRI measurements. Additionally, the presence of hydrocephalus was assessed by multiple pediatric radiologists to enable the reliability of a subjective evaluation compared to the quantitative method. The limitations of our study include the retrospective study design, which resulted in an inability to retrospectively analyze certain examinations accurately. However, the overall impact of this difference compared to a prospectively conducted study should be negligible. Another constraint is the relatively modest number of examinations, with 124 included specimens. This limitation is particularly relevant in assessing different pediatric age groups. A larger sample size would enhance the comparability of age groups. Nevertheless, the authors contend that the study's general findings remain unaffected by these points. Lastly, general measurement inaccuracies, which are inherent to volume measurements, should be considered. These inaccuracies affect not only automated measurements but also manual reference measurements. The manual reference segmentations were all performed following the steps described above. To account for edematous brain lesions that might be included in thresholding, all segmentations were manually corrected as described. Deviations of several percentage points are not uncommon, especially with small absolute volumes. This may partially account for the increased measurement variability observed in young children. Generally, there seems to be a lot of room for further studies on this important matter.
# Triangular Position Multi-Bolt Layout Structure Optimization
## Materials and Methods
### Construct Objective Function  
#### 2.2.1 Bolt Mechanical Model  
Apply a pull in the positive direction of X and a pull in the negative direction of Y at point K, both of which are 15 KN. Among them, the bolts are arranged in the position of an equilateral triangle, whose side length is a and centroid is O. The structural diagram is shown in Figure 2.  
By simplifying the force at point K to point O, the torque at point O is formed. Carrying out force analysis on bolts \(A\), \(B\) and C:  
\[F_{A}=\left(5-\frac{2250\sqrt{3}\times\sin 30^{\rm o}}{a}\right)\stackrel{{ \rightarrow}}{{i}}+(-5-\frac{2250\sqrt{3}\times\cos 30^{\rm o}}{a}) \stackrel{{\rightarrow}}{{j}} \tag{8}\]  
\[F_{B}=\left(5-\frac{2250\sqrt{3}\times\cos 30^{\rm o}}{a}\right)\stackrel{{ \rightarrow}}{{i}}+(-5+\frac{2250\sqrt{3}\times\sin 30^{\rm o}}{a}) \stackrel{{\rightarrow}}{{j}} \tag{9}\]  
\[F_{\rm C}=\left(5+\frac{2250\sqrt{3}}{a}\right)\stackrel{{ \rightarrow}}{{i}}+(-5)\stackrel{{\rightarrow}}{{j}} \tag{10}\]  
According to Equations (8)-(10), from \(F_{A}\), \(F_{B}\) and \(F_{C}\), it can be seen that the maximum force is bolt C:  
\[F_{max}=F_{\rm C} \tag{11}\]  
At this time, the maximum shear stress on the bolt is:  
\[\tau_{max}=\frac{F_{max}}{\pi d^{2}/4} \tag{12}\]  
It can be seen from the analysis that bolt B receives the maximum torque. Bolt B is cut along the \(Y\)-axis to obtain the profile as shown in Figure 3a and the torque diagram of the plate as shown in Figure 3b.  
Figure 2: Schematic diagram of bolt structure.  
The torque and moment of inertia at bolt B are:  
\[M=F_{B}\Big{[}300+(150-\frac{a}{2})\Big{]} \tag{13}\]  
\[I=I_{max}-\Big{[}I_{hole}+I_{offset}\Big{]}=\frac{10\times 300^{3}}{12}-\Bigg{[} \frac{10d^{3}}{12}+\bigg{(}\frac{a}{2\sqrt{3}}\bigg{)}^{2}\times 25\text{d} \Bigg{]} \tag{14}\]  
Then the critical bending stress of the nickel steel plate is:  
\[\sigma_{max}=\frac{M}{I} \tag{15}\]  
Formulas (11), (12) and (15) can be used to calculate the maximum stress on bolts, maximum shear stress and critical bending stress of the nickel steel plate, which lays a prerequisite for the selection of the objective function in the following algorithm.
# Facile Synthesis of Highly Conductive Vanadium-Doped NiO Film for Transparent Conductive Oxide
## Materials and Methods
### Synthesis of Pristine and V-Doped NiO  
Nickel oxide (NiO) nanoparticles were synthesized by the solvothermal decomposition method. Briefly, 1 mmol of Ni(II) acetylacetonate was dissolved in 15 mL of oleylamine. The solution was kept in a two-necked round flask and heated to 110 \({}^{\circ}\)C with continuous stirring under nitrogen flow for 45 min. The obtained clear blue solution was then cooled to and maintained at 90 \({}^{\circ}\)C. Next, a mixture of 0.4 mL of borane tri-ethylamine and 2 mL oleylamine was quickly injected into the clear solution. The resulting dark solution was continuously stirred for 1 h. The resultant solution containing ultra-small-sized NiO nanoparticles was collected via 6000 rpm centrifugation for 15 min. Then, the NPs were washed in ethanol three times. To obtain the vanadium-doped NiO (V:NiO), different atomic percentages of vanadium pentoxide were added to the initial Ni(II) acetylacetonate and oleylamine solution. All other conditions were similar to those for the synthesis of the pristine NiO. In this reaction, oleylamine acts as a capping agent, which helps to reduce particle growth, and borane tri-ethylamine was used as a reducing agent, as shown in Scheme 1. As-synthesized pristine NiO and V:NiO were easily dispersed in DI water by 30 min ultra-sonication. Pristine and doped NiO films were deposited under room temperature on cleaned FTO glass substrates by spin coating (2500 rpm, 60 s) with the solutions mentioned above.