esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In Section 4, there are instability zones in the phase data, and this work eliminated such instability zones as described in Section 4.1. The instability data appear at the beginning of the waveform in the transmitting process, where the phase is affected by the temperature of the MMIC. Figure 14 shows MMIC temperature data in the measurement experiment of the loopback waveguide in Section 3.2, where five measurement trials are plotted. The temperature changes to become higher at the beginning of the first five bursts, as highlighted. Thus, MMIC temperature is sensitive when handling very small phase data.  
Figure 13: Raw data plot by radar and shaker.  
As mentioned in Section 4, two error components were observed through the two measurement experiments:  
* Positive offset error due to MMIC characteristics.
* Gradient (gain) error potentially caused by MMIC characteristics or shaker machine properties.  
Looking at stability data, the data exhibit strong linearity across the entire measurement range, as shown in Figure 13, and can be calibrated using appropriate signal processing techniques. For instance, Figure 15 presents a periodogram that estimates the spectral density of the measured phase during a shaker vibration case at 700 Hz with a displacement of 1.013 \(\upmu\)m. This reveals unexpected signals at lower frequencies, despite the shaker vibrating at a consistent frequency of 700 Hz.  
To address this issue, a high-pass filter (HPF) was applied to the sensor data. Figure 16 displays the results obtained after the application of an HPF with a cutoff frequency of 500 Hz. The application of the HPF reduced the offset, aligning the data more closely with the ideal line. This also improved the R\({}^{2}\) value from 0.99 to 1.00 for the 700 Hz vibration case and from 0.80 to 0.97 for the 1600 Hz vibration case, as summarized in Table 8. This improvement suggests that the offset is likely caused by low-frequency noise.  
Figure 16: Applying HPF to raw data from radar and shaker.  
Figure 15: Periodogram on phase (700 Hz vibration case).  
Furthermore, the linear regression model provides coefficients with Equation (9) describing the 700 Hz scenario and Equation (10) describing the 1600 Hz scenario, where \(d_{\text{sht\_prt}}\) and \(d_{\text{Ing\_prt}}\) are calibrated sensor data, and ds is the raw data measured by the radar sensor. These equations can be used for sensor calibration. Figure 17 presents the calibrated sensor data using the regression coefficients, demonstrating a close fit to the ideal displacement produced by the shaker. Although calibration was successfully achieved, the cause of the gradient error remains unclear. Future work should address this issue through additional test measurements across a wider range of accelerations and vibration frequencies and various types of shaker machines.  
\[d_{\text{sht\_prt}}=0.942\times d_{\text{s}}+0.572 \tag{10}\]  
\[d_{\text{Ing\_prt}}=0.927\times d_{\text{s}}+0.686 \tag{11}\]
## 3 Spectrum Management Issues
### Small Cell
#### 3.3.1 Interference Avoidance  
The authors in [141] have presented a massive SS approach for in-building small cells. They have proposed a nonorthogonal interference-free SS approach to form 3D clusters and less distance among co-channel small cells. The results have demonstrated that the average spectral efficiency is substantially improved with the increase in the number of buildings, whereas energy efficiency is decreased with the increase in the number of buildings. Overall, the results have proved that the suggested nonorthogonal scheme is much better than the orthogonal spectrum for the Licensed Shared Access (LSA) and Licensed Assisted Access (LAA) methods in terms of sharing the 60 GHz unlicensed spectrum. Another work in [142] focuses on designing small cell size networks that broadly check the issues of spectrum management. In particular, the researchers have focused on Wi-Fi and 4G wireless communication systems that share the unlicensed spectrum. They have introduced a novel network structure for both to utilize the unlicensed spectrum in the same proximity. They have also presented an Almost  
Figure 4: Small cell-based Heterogeneous Networks (HetNet).  
Blank Subframe (ABS) scheme to minimize interferences and proposed an interference avoidance scheme to cancel out the effect of types of interferences encountered by both technologies in a network. Consequently, the suggested architecture effectively contributes to cost reduction and interference avoidance; however, the transmission range is limited.
# Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation
## 3 Multi-DOF Displacement Computation
### Solution Component Uniqueness Theory
Thus, taking the infinite norm of \(\mathbf{w}_{n}\) as the independent factor (IDF), that is  
\[\text{IDF}_{n}=\left\|\mathbf{w}_{n}\right\|_{\infty} \tag{23}\]  
There is \(\text{IDF}_{n}\in[0,1]\) since \(\mathbf{v}\in\mathbb{R}_{[-1,1]}^{N\times 1}\). The closer \(\text{IDF}_{n}\) is to 0, which indicates that \(\mathbf{p}_{n}\) has weaker independence and stronger dependence in \(\mathbf{J}\), it follows from Theorem 2 that \(x_{n}\) is more likely to be not unique; on the contrary, with stronger independence and weaker dependence of \(\mathbf{p}_{n}\), \(x_{n}\) is more likely to be unique. According to the above, the IDF shows the possibility of whether a solution component is unique. After computing the IDFs of all the vectors in the Jacobi matrix, some simple classification algorithms such as the k-nearest neighbor algorithm [24] can be used to classify all the components into two categories according to the size of their IDFs, so as to determine the components' uniqueness. A simple approach is setting a threshold \(\text{TH}_{\text{IDF}}\in(0,1)\), there is  
\[x_{n} \begin{cases}\text{ is not unique, IDF}_{n}<\text{TH}_{\text{IDF}}\\ \text{ is unique, IDF}_{n}>\text{TH}_{\text{IDF}}\end{cases}  \tag{24}\]  
The brief process of the solution component uniqueness algorithm proposed above is summarized as follows. So far, by using Theorem 1, Theorem 2, and Algorithm 1, it can be determined whether each unknown in the SNES can be solved accurately. Also, the solvability of the displacement in the interferometer model can be verified, which is discussed in the following Section 3.3. ```
1:Initialization: Given \(\text{TH}_{\sigma}>0\), \(\text{TH}_{\text{IDF}}\in(0,1)\), \(\mathbf{X}\) is a solution in the solution set of (12)
2:\(\left[\mathbf{p}_{1},\ldots\mathbf{p}_{N}\right]=\mathbf{J}(\mathbf{X})\)
3:\(\mathbf{A}=\left[\mathbf{p}_{1}-\text{mean}(\mathbf{p}_{1}),\ldots\mathbf{p }_{N}-\text{mean}(\mathbf{p}_{N})\right]\)
4:\(\mathbf{C}=\mathbf{A}^{T}\mathbf{A}/M\)
5: Eigen decomposition \(\mathbf{C}=\mathbf{V}\mathbf{D}\mathbf{V}^{T}\), where \(\mathbf{V}=\left[\mathbf{v}_{1},\ldots,\mathbf{v}_{N}\right]\), \(\mathbf{D}=\mathbf{diag}(\sigma_{1},\ldots,\sigma_{N})\)
6: Find \(r\) satisfies \(\sigma_{1}>\cdots>\sigma_{r}>\text{TH}_{\sigma}>\sigma_{r+1}>\cdots>\sigma_{N}\)
7:\(\left[\mathbf{w}_{1},\ldots,\mathbf{w}_{N}\right]^{T}=\left[\mathbf{v}_{1}, \ldots,\mathbf{v}_{N}\right]\)
8:\(\left[\text{IDF}_{1},\ldots,\text{IDF}_{N}\right]=\left[\left\|\mathbf{w}_{1} \right\|_{\infty},\ldots,\left\|\mathbf{w}_{N}\right\|_{\infty}\right]\)
9: Compute the uniqueness of each component by Equation (24)
```  
**Algorithm 1** Solution component uniqueness algorithm.
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 4 G2V/V2G Operations
### V2V/V2M/M2V Operations
#### 5.1.2 Measured Results  
The measured results of the developed V2V scheme under charging current \(i_{b2}^{*}=6\) A are shown in Figure 27. Good control performance can be observed. Figure 28 shows the measured results under charging current command change \(i_{b2}^{*}=4\) A \(\rightarrow\) 6 A.. Satisfactory dynamic responses can also be observed from the results. Figure 29 shows the measured results under the preset charging current command pattern \(i_{b2}^{*}=0\) A \(\rightarrow\) 3 A \(\rightarrow\) 6 A \(\rightarrow\) 3 A \(\rightarrow\) 0 A, where \(E_{b1}\) and \(E_{b2}\) are the battery-consumed energy of EV1 and the received energy of EV2 calculated as:  
\[E_{b1}=\int P_{b1}\;dt,\;E_{b2}=\int P_{b2}\;dt \tag{25}\]  
Figure 26: CLLC resonant converter control scheme of EV2 in developed V2V scheme.  
Figure 25: Detailed power circuit of developed V2V scheme.  
Figure 28: Measured \((v_{b1},i_{b1})\), \((v_{dc},i_{dc})\) and \((v_{b2},i_{b2})\) of developed V2V scheme under charging current command change \(i_{b2}^{*}=4\) A \(\rightarrow\) 6 A.
# A Single Motor-Driven Focusing Mechanism with Flexure Hinges for Small Satellite Optical Systems
## 2 Materials and Methods
### Fabrication of SMFH and Experimental Setups  
Due to the high resolution of displacement control, the proposed SMFH can be controlled at the sub-micrometer level. However, the precision and the repeatability of these adjustments could be diminished due to rough machining tolerances in the fabrication phase. Also, the backlash that occurs between the teeth of different stages within the gearbox and the linear screw could degrade the control precision. Accordingly, we first employed restrictive machining tolerances for each component and polished the surfaces where the sensors contact the secondary mirror supporter, in order to minimize the measurement error. Then, it was integrated with the linear screw and the geared DC motor, as shown in Figure 7.  
As shown in Figure 7b, five LVDT sensors were used to coordinate the geometry of the secondary mirror due to the deformation of FlexHes. First, to calculate the orthonormal vector of the plane (De-space and Tilt), three LVDT sensors were arranged at 120\({}^{\circ}\) from each other and were attached at the secondary mirror supporter. By using the orthonormal vectors obtained by these sensors, the initial  
Figure 7: Photographs of the experimental setups; (**a**) Characterization of the rotation body, (**b**) Characterization of SMFH with five LVDT sensors for measuring a. vertical, b. horizontal displacements, and c. one-geared DC motor.  
position of the secondary mirror was identified. The other two LVDT sensors were used to obtain the De-center, which was placed at both the \(x\) and \(y\)-axis of the secondary mirror supporter, respectively. The description of data obtaining spots and vector directions are shown in Figure S1. The employed LVDT sensor provides a high resolution of 0.031 \(\upmu\)m with a fast sampling time of 1 ms. Secondly, the DC motor (Maxon Motor(r), A-Max 16, customized, Sachseln, Switzerland), having a reduction ratio of 108:1, was integrated. The geared DC motor included a rotary encoder, and each encoder pulse could precisely measure the angle of the motor shaft down to 0.00637\({}^{\circ}\). The motor's speed was fixed to 50 RPM, and the applied PID values are summarized in Table 4. Then, by converting the encoder data into the displacement of the secondary mirror supporter, input control functions were determined. Here, to reduce the errors due to the backlash, the EPOS2 controller (Maxon Motor(r), EPOS2 24/2, Sachseln, Switzerland) that provides a dual loop position control was employed. The control architecture features a proportional controller, and the gain scheduler makes a P gain for the main loop controller to track the errors that could occur between the current and the desired load position. Accordingly, the embedded feedback control enables the system to avoid chattering or hunting due to the backlash. Furthermore, the obtained data from the LVDT sensors, De-space, De-center, and Tilt were compiled by means of MATLAB (MathWorks(r), R2018a, Natick, MA, USA).
## 4 Manipulability Maximization and Constraints Satisfaction
### Joint Velocity Constraints  
In a task space trajectory tracking problem, joint constraints at the velocity level are as important as joint limits and self-collision avoidance. These constraints include joint velocity boundary constraints and joint velocity limits. Satisfying joint velocity boundary constraints is a requirement for the end-effector to stop at the beginning and end of the task, i.e., the initial and final joint velocities must be zero. Joint velocity limits must be considered because the motion planning algorithm might generate joint velocity commands that are out of bounds in order to follow the task space velocity profile. These unfeasible velocities cannot be achieved by the real joints. Therefore, the trajectory tracking will fail if the joint velocity limits are not respected.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 4 Gravity Measurements
### Adjustment of Gravity Measurements  
After all the errors have been removed from the relative gravity measurements using the mentioned corrections, the network adjustment can begin to obtain absolute values of gravity at all the points. In this article, the adjustment was performed using the least squares method for indirect measurements, where the measurement functions were the measured gravity differences between points. Based on the measurement functions, the design matrix (\(A\)) and the observation vector (\(I\)) were created, while the weight matrix (\(P\)) was generated based on the mutual distances between the points. The adjustment was carried out using the Gauss-Markov model based on the least square's method [35]:  
\[\mathbf{v}^{t}\mathbf{P}=min, \tag{40}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measuring Day** & \(\mathbf{\Delta(n-1)\) **[mGal]** & **Model** & \(\mathbf{\Delta_{d}(n-1)\) **[mGal]** \\ \hline
15 July 2023 & 0.078 & \(y=0.000005x+4343.606\) & 0.001 \\
22 July 2023 & \(-0.042\) & \(y=-0.000001x+4343.849\) & 0.006 \\
24 July 2023 & \(-0.013\) & \(y=-0.000001x+4343.872\) & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical procedure of gravimeter drift correction.  
Figure 12: Procedure of gravimeter drift calculation.  
Whose stochastic model includes the weight matrix and empirical part is defined by the following equation:  
\[\mathbf{v}=\mathbf{Ax}-\mathbf{I}. \tag{41}\]  
The result of the adjustment is the absolute gravity values for all the points in the leveling line. In accordance with the _Regulation on the Confidentiality of Defense Data_ of the Republic of Croatia [36], gravimetric survey data must not be disclosed without authorization. Therefore, the absolute gravity values for all the points in the leveling line are not provided in this article. However, to illustrate the variation in gravity along the leveling line, Figure 13 presents a gravimetric map of the survey area, where the gravity values across the entire displayed region were obtained through Kriging interpolation.
## 4 Result  
### TOC and Mineral Composition  
Table 1 displays the organic geochemical characteristics and mineral composition of all the samples. The TOC content of six shale samples from the Funing Formation ranges from 0.55% to 1.81%, with an average of 1.11%. The range of the R\({}_{\text{O}}\) is from 0.81% to 0.91%, with an average value of 0.88%. It has been found that clay minerals do not dominate the composition of some shale reservoirs but contain a large quantity of quartz, dolomite, calcite, feldspar minerals, and their mixtures. Based on XRD analysis, the primary mineral constituents in the Funing Formation shale samples consist of quartz (with an average content of 25.53% and a range from 20.20% to 34.70%) and dolomite (with an average content of 24.23% and a range from 2.80% to 51.8%). Additionally, clay minerals (with an average content of 20.77% and a range from 14.60% to 32.70%) and feldspar (with an average content of 13.80% and a range from 2.50% to 30.40%) are also present.
###### Abstract
A systematic review on complications and aftercare along with patient satisfaction of MISODs revealed insignificant differences in radiographic and clinical parameters, patient satisfaction, and prosthetic complications when comparing between MISODs supported by two and four implants [15]. A few studies had contradictory results regarding marginal bone loss and complications. Based on [PERSON] et al. 's review [15], the third EAO Consensus Conference in 2012 also stated that there is no evidence for preferring four implants over two [16]. Notably, however, all of the RCTs selected for review compared two non-splinted ball attachments with two or four splinted bar attachments. None of them compared between two and four non-splinted ball attachments [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. There is clinical logic for preferring an MISOD with four non-splinted implant attachments over splinted implant attachments. It requires less clinical time, and with the direct technique, as described in our former report, it is less expensive and entails fewer prosthetic elements [28]. In addition, mandibular MISOD retained on separate implants shows a lower repair rate as well as easier oral hygiene protocol compared to splinted implant and their attachments [29]. Furthermore, that same prosthesis requires less height in its construction, which makes its esthetical limits less challenging [30; 31]. A more recent meta-analysis by [PERSON] et al. found no compelling preference when comparing splinted and unsplinted implants between two or three implant cohorts in each reviewed article, assisting MISOD [32]. Although the conclusion dealt with implant survival, marginal bone loss, and prosthetic aftercare, none of the studies reviewed compared the same attachment system between different numbers of unsplinted implants. The purpose of this study was to compare the long-term aftercare of two-ball implant vs. four-ball implant attachment MISODs.
## 5 Juno Trajectory Geometry
The many findings from Juno have altered our perception of Jupiter's atmosphere and interior, illuminating a weather layer in Jupiter's atmosphere that reaches far beyond the planet's water clouds as well as a deep interior with a core composed of diluted heavy elements. Juno is now conducting an extended mission, during which it will continue its examination of the biggest planet in the solar system until September 2025 or until the mission reaches the end of its operational lifetime, whichever comes first. This extension gives Juno the mission of becoming an explorer of the Jovian system, including Jupiter and all of its moons and rings. In addition, Juno will have extra meetings with Europa and Io, two of Jupiter's most interesting moons [33].
## 3 Test with simulation  
### Synthetic image pattern  
In order to test the described algorithm in complicated situations, synthetic image patterns are created with a given motion. The sketch in Fig. 3 illuminates the cross-section of the image pattern used in the following tests and the motion in the image plane. The image pattern consists of two asymmetric ellipses of different sizes, and they are separated with a distance of 2d\({}_{0}\). Each ellipse has two halves of different dimensions that are determined with parameters \(R\) and \(D\). The gray value distribution of each half ellipse is determined with an exponential function as below.  
\[g(x,y)=G_{o}\cdot\exp\left(-2\left(\frac{x-x_{c}}{D}\right)^{2}-2\left(\frac{y- y_{c}}{2R}\right)^{2}\right) \tag{5}\]  
Wherein (\(x_{c}\),\(y_{c}\)) is the center of the ellipse. The image pattern rotates at the center point (\(o\)) of the line that connects the two ellipse centers with an angular speed of 2\(\omega\). With the half of the angular speed (i.e. \(\omega\)), the center of the image pattern \(o\) rotates around the origin point \(O\) in the xy-plane with radius \(r\). In the current tests, \(d_{\theta}\) is used as a basic parameter to determine the size of the image pattern, and other related parameters are determined accordingly:  
\[\begin{split}& D_{1}=d_{0}\,\ D_{2}=0.618D_{1}\,\\ & R_{1}=0.5D_{1}\left(1-0.4\frac{\left|y\right|}{r}\right),\ R_{2}=0.5D_{1} \left(1-0.6\frac{\left|y\right|}{r}\right),\\ & R_{3}=0.5D_{2}\left(1-0.4\frac{\left|y\right|}{r}\right),\ R_{4}=0.5D_{2} \left(1-0.6\frac{\left|y\right|}{r}\right).\end{split} \tag{6}\]  
Figure 3: Synthetic image pattern and its motion.  
According to equations (6) the ellipse image pattern shrinks in one axis with increasing \(|y|\), so that the off-plane effect of the insect body part motion is simulated. To simplify the tests, the sample images shown in Fig. 1 are used as a reference for generating the synthetic image patterns, i.e. the radius r is set to 24 pixels to match the termite head-banging rising distance; the rotation circle is resolved with 600 frames (i.e. \(\omega\Delta\)t=0.6 degree) so that the half of the rotation circle have similar fames to that of the termite head-banging; the synthetic images have the same random noise level in the background as those given in Fig. 1, i.e. gray value standard deviation of 9 around average of 230. As an example, Fig. 4 shows the image patterns of \(d_{0}\)=6 at 11 different positions in the image plane.
###### Abstract  
Due to the advantages such as high flexibility, low cost and easy implementation offered by unmanned aerial vehicles (UAVs), a UAV-assisted network is regard as an appealing solution to a seamless coverage, high disaster-tolerant and on-demand wireless system. In this paper, we focus on the downlink transmission in a cache-enabled UAV-assisted wireless communication network, where UAVs cache popular content from a macro base station in advance and cooperatively transfer the content to users. We aim to minimize the average transmission latency of the system and to formulate an optimization problem that jointly optimizes the UAV location, beamforming and caching strategy. However, the formulated problem is very challenging because of its non-convexity and the highly coupled optimization variables. To solve this resulting problem efficiently, we decompose it into two subproblems, namely UAV location and beamforming optimization, and UAV caching strategy optimization. The first subproblem is an NP-hard joint optimization problem, while the second one is a linear programing problem. By adopting the first-order Taylor expansion, we propose a convex optimization algorithm based on the difference-of-convex (DC) method. Specifically, we bring out a method to apply linear approximation in the DC-based algorithm, which is particularly suitable to the problems involving complicated summations. The numerical results demonstrate that the proposed DC-based iterative optimization algorithm can efficiently reduce the average transmission latency of the system.  
c pacs: 12 3438 12  
to UAV-assisted communication systems, in which the requirements [4] and architecture [5] of Uncrewed Aerial Systems are defined.