esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 6 Instrument Noises and Corrections
### Ionospheric Correction  
The propagation of electromagnetic waves such as microwaves or laser light is affected by the non-ideal vacuum, in particular, by charged particles forming the ionosphere. The KBR system uses the dual-band observations to measure and correct the effect (cf. Equation (49)). The magnitude of this correction, as provided in the KBR1 Bv04 data, is shown as red trace in the right plot of Figure 4. The correction is well above the KBR-LRI residuals and the accuracy of the correction could in principle cause some error. However, we assessed the second-order ionospheric effect [50] and concluded that the ionosphere is likely not limiting KBR-LRI residuals, since the second-order effect is direction-dependent and highly rejected in the DOWR combination, in which the contribution of the path from satellite A to B is counteracted by the contribution of the path from B to A.  
Figure 3: Typical LRI and KBR ranging data shown in the spectral domain (**top**) and in the time-domain (**bottom**). The left plots use the official LRI1 Bv04 data product, while the right plots show an in-house-derived alternative product (LRI1 Bv50). The spectral plots show the difference traces (blue) for each day in January 2019.  
The ionospheric effect as measured by the KBR can be rescaled to the LRI optical wavelength of 1064.5 nm, see red trace on left plot of Figure 4. For such near-infrared wavelengths, the ionospheric delay is a negligible error contributor [18].
## 1 Introduction
Secondly, the CVAE incorporates item categories as auxiliary information for supervising the encoding and decoding of item data. By learning the distribution characteristics of data, it reconstructs missing values in rating data, effectively reducing data sparsity. Subsequently, the CPMF algorithm constrains user features, optimizing the feature extraction process. The stochastic gradient descent algorithm is employed for parameter updates. Finally, the product of user and item feature matrices yields predicted ratings. The experimental results indicate that the proposed algorithm effectively addresses the challenges of data sparsity and suboptimal feature extraction, leading to a significant improvement in recommendation performance. The main contributions of this paper are as follows:  
1. Proposing a recommendation algorithm based on an improved CVAE and CPMF, which effectively addresses the issues of poor recommendation performance in traditional user-based collaborative filtering recommendation algorithms due to data rating sparsity and suboptimal feature extraction. 2. Enhancements are made to the CVAE. In addition to the existing structure, latent layers are incorporated, and random noise is introduced into these layers. Processing input data through these latent layers yields more precise implicit features, thereby enhancing the model's resilience to interference and its generative capabilities. Furthermore, the CVAE incorporates item categories as auxiliary information for supervising the encoding and decoding of item data. By learning the distribution characteristics of data, it effectively reconstructs missing values in rating data, reducing data sparsity. The reconstructed data is subsequently subjected to CPMF to extract implicit rating features of users and items, optimizing the feature extraction process. 3. MovieLens-100K and MovieLens-1M datasets are selected for experimental evaluation. Four comparative algorithms are chosen, and the proposed algorithm is compared to them using the same dataset. The comparison results reveal that the proposed algorithm reduces the Root Mean Square Error (RMSE) by 2.26%, 3.30%, 2.57%, and 5.12%, and the Mean Absolute Error (MAE) by 2.63%, 3.66%, 2.53%, and 5.33% respectively. In the MovieLens-1M dataset, RMSE is reduced by 4.39%, 5.49%, 4.22%, and 5.99%, and MAE is reduced by 3.28%, 4.17%, 3.08%, and 5.97%, respectively.
## 1 Introduction  
In order to realize the objectives of continuous return to the lunar surface, work in low-Earth on-orbit, and paving the way for the deployment of missions to the Moon and Mars, a new item called \"Autonomous Systems\" has been revealed in the NASA technical classification published in 2020 [1]. Built-in autonomous stereo vision devices play a vital role in position sensing and perception or pose estimation to facilitate the autonomous docking of multiple daughter-mother space vehicles, so as to expand their versatile applications in planetary science exploration. The final docking phase is the most challenging, particularly for re-docking the daughter space vehicles to the mother space vehicles [2].  
In the final docking stage, fiducial targets are employed for the algorithms of autonomous docking navigation to improve the reliability of lateral control; within the operational range of 10 m and \(\pm\)30\({}^{\circ}\) field of view (FOV), the required accuracy is 1 cm [3]. The space docking cameras play the chief role of image capturing in autonomous stereo vision-guided docking devices. The captured image will be integrated into the algorithm for the autonomous docking system [3; 4; 5]. Currently, those space docking cameras are often equipped with charge-coupled device (CCD) image sensors [3; 6]. Due to the technical issue in further reducing the size of the CCD device, it, in turn, becomes difficult to make the overall size of the camera more compact. Complementary metal-oxide semiconductor (CMOS) image sensors are widely applied in image capturing devices due to the features of small package size, low power consumption, and high readout speed. Using CMOS to replace CCD would be the most effective solution to reduce the form factor of the camera [7; 8; 9]. The higher readout speed of the CMOS device is also favorable for the autonomous docking algorithm, which takes time to calculate the accurate position in a dynamic situation. In addition, current space docking cameras employ radiation resistant material only for the outermost few lens elements. The lens elements without radiation resistance could degrade quickly, and, in turn, influence the optical performance of the camera. The solution would be introducing radiation resistant material for all lens elements to increase the radiation resistance of the overall system and make a space grade imager instrument.  
This paper presents the optical design process of a compact radiation-resistant camera lens module for space autonomous docking instruments equipped with the MT9P031 CMOS image sensor (CIS). The design result of this camera lens module will be introduced to the National Space Organization (NSPO) pilot program for space exploration application. Section 2 derives the optical specification of the camera based on the requirements of the autonomous docking operation, and gives a review on the available radiation resistant material. Section 3 describes the optimization and simulation of the camera module, with the radiation resistant material meeting all the required specifications. Finally, Section 4 gives the conclusion.
## 7 Results of Calculations and Their Analysis  
The electric energy cost is shown in Figure 3a,b for options without a CO2 emission charge and with a charge of $ 30/t CO2. For the last option Figures 4 and 5 shows the structure of cost components (capital, operational, fuel, and associated with the payment for emissions) in absolute and relative units, respectively.  
Calculations show that the cost of electricity mini-CHP on wood fuel (wood chips) is significantly less than the cost of electricity of a diesel power plant. In this regard, for autonomous energy systems  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fuel** & **k, \$/kW** & \(\delta\)**, 1/year** & \(\eta_{e}\) & \(\eta_{h}\) & \(\Delta T\)**, year** & **Fuel Price, \$/toe** \\ \hline \multicolumn{7}{c}{**Fossil Fuel**} \\ \hline Coal & 1000–1250 & 0.07 & 0.28–0.30 & 0.52–0.55 & 20 & 36–57 \\ Natural gas & 650–700 & 0.05 & 0.30–0.32 & 0.53–0.56 & 15 & 93–100 \\ Diesel fuel & 450–550 & 0.07 & 0.30–0.32 & 0.40–0.48 & 15 & 890–990 \\ \hline \multicolumn{7}{c}{**Biomass**} \\ \hline Wood chips & 1100–1350 & 0.10 & 0.25–0.27 & 0.48–0.53 & 20 & 29–72 \\ Pellets & 900–1000 & 0.07 & 0.27–0.30 & 0.50–0.55 & 20 & 150–200 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Technical and economic indicators of power plants on different types of fuel (cogeneration of electricity and heat).  
Figure 3: The cost of electric energy from energy sources using different types of fuel (**a**)–without carbon tax, (**b**)–with carbon tax, min and max–minimum and maximum energy cost taking into account the uncertainly interval).  
of small power, especially near the logging points, energy supply from gasification-based mini-CHP on biomass is more preferable than the use of diesel plants with a predominance of the fuel component in the energy cost.  
Compared to coal and gas power plants, wood-fired mini-CHPs can be both more and less efficient, depending on specific conditions. If there is no charge for carbon dioxide emissions, then wood-based energy sources are on average inferior to competing mini-CHP plants on coal and gas, except for the option of using cheap fuel chips at a price of $ 30-40/toe (see Figure 3a).  
With the introduction of an emission charge of $ 30/t CO\({}_{2}\), the competitiveness area of wood-fired power plants expands. In this case, wood firing mini-CHP plants on wood chips are more efficient than coal-fired and, in some cases, gas-fired power plants (see Figure 3b). This is because the cost of energy increases significantly when using fossil fuels, especially coal, due to the component associated with the payment for emissions (see Figure 4).  
Pellet power plants will be competitive with diesel power plants over the entire range of parameter changes, and with power plants on coal and gas-with the introduction of emission charges and with relatively cheap pellets (for about 140-150 $/toe). This makes it possible to exclude rigid binding of the CHP to the place of logging and to transport fuel over considerable distances.
## 4 Experimental Evaluations  
### Experimental Setup  
The experimental data were acquired by the Center for Intelligent Maintenance Systems (IMS) [28; 29]. The experimental setup and its schematic diagram are displayed in Figure 3a,b, respectively. The vibration acceleration signals were collected every 10 min while input shaft speed was 2000 rpm (33.33 Hz) and the sampling frequency was 20 kHz. Two kinds of accelerated life test experiments were investigated: (1) the first test was carried out successively for 34 days (about 21,560 min), and (2) the second test was carried out successively for 8 days (about 9840 min). In this experiment, the severe wear failure in the inner race of bearing 3 and severe wear failure in the outer race of bearing 1 were respectively detected after dismantling. More details about the experimental description and experiment parameters can be found at the following website: [[http://www.imscenter.net/](http://www.imscenter.net/)]([http://www.imscenter.net/](http://www.imscenter.net/)).  
Figure 3: Experimental setup. (**a**) Physical experimental platform; (**b**) Schematic diagram [28; 29].  
Figure 2: The flow chart of the proposed method for degeneration trend prognostics of rotating machinery.  
The health indicators time series; i.e., the EVI degeneration curve of bearing 3, and the Kurtosis degeneration curve of bearing 1--are illustrated in Figure 4a,b, respectively. The detailed calculation process of health indicator EVI can be found in ref. [15]. The kurtosis waveform presented in Figure 4b can be divided into running status based on vibration amplitude; i.e., (1) normal operation, (2) incipient fault, and (3) severe fault status. It also should be noted in Figure 6b that some sharp transition points (STPs) are displayed in the incipient and severe fault stages of bearing 1 degeneration.  
To demonstrate the validity and superiority of the proposed approach, the EVI and kurtosis degeneration curves are presented as test cases. As a first test case, the EVI time series with weak LRD property has been measured. The second test case is a kurtosis time series with some STPs. Meanwhile, some state-of-the-art prediction methods including the ARMA and FOC methods (_f_-ARIMA model [15] and FBM model [16]) are also employed for comparison.
###### Abstract  
Memory nonlinear error greatly reduces the performance of analog-to-digital converters (ADCs), and this effect is more serious in a time-interleaved analog-to-digital converter (TIADC) system. In this study, the sinusoidal wave fitting method was adopted and a joint error estimation method was proposed to address the memory nonlinear mismatch problem of the current TIADC system. This method divides the nonlinear error estimation method into two steps: the nonlinear mismatch error is coarsely estimated offline using the least squares (LS) method, and then accurately estimated online using the recursive least squares (RLS) method. After the estimation, digital post-compensation method is adopted. The obtained error parameters are used to reconstruct the error and then the reconstructed error is reduced at the output. This study used a four-channel 16-bit TIADC system with an effective number of bits (ENOB) value of 10.06 bits after the introduction of a memory nonlinearity error, which was increased to 15.42 bits after calibration by the joint error estimation method. As a result, the spurious-free dynamic range (SFDR) increased by 36.22 dB. This error estimation method can improve the error estimation accuracy and reduce the hardware complexity of implementing the error estimation system using a field programmable gate array (FPGA).  
time-interleaved analog-to-digital converter (TIADC); memory nonlinear effect; joint estimation; Volterra series; mismatch error +
Footnote †: journal: Sensors
# Channel Modeling for In-Body Optical Wireless Communications +
## 2 Channel Model
### Scattering  
The analysis so far neglects the effects of scattering on the propagation of the optical radiation through the human tissue. So far, the transmission distance, \(\delta\), was regarded as a single linear path through the material. However, when taking into account the optical scattering, it became the sum of all the paths between scattering events. The analytical evaluation of scattering is not well defined in the literature due to its stochastic nature, especially in diffuse reflectance geometries, such as the majority of tissues found inside the human body. In more detail, the scattering phenomenon can be decomposed into two factors, namely mean free path and phase function. The former represents the mean distance between two scattering instances, while the latter quantifies the stochastic change in direction of a photon when scattering occurs. Throughout the literature, the mean free path is represented as the inverted scattering coefficient, i.e., \(\frac{1}{\mu_{s}}\), which represents the probability of scattering as a function of the transmission distance. Furthermore, the scattering coefficient can be expressed in terms of the more tractable reduced scattering coefficient as  
\[\mu_{s}^{\prime}=(1-g)\mu_{s}, \tag{10}\]  
where \(g\) represents the anisotropy factor of the generic tissue. On the other hand, the stochastic behavior of the phase function can be mathematically defined based on Mie scattering theory.  
Unlike absorption, there is no well-defined analytical relationship that can be used to define the spectral dependence of volume scattering. However, based on experimental observations and simplifying analysis of Mie scattering theory, there is a consensus that the reduced scattering coefficient can be modeled using a power law relationship, as in [19].  
\[\mu_{s}^{\prime}=\mu_{s}^{\prime}(\delta_{0})\bigg{(}\frac{\delta}{\delta_{0} }\bigg{)}^{-\beta}, \tag{11}\]  
where \(\mu_{s}^{\prime}(\delta_{0})\) denotes the reduced scattering coefficient at distance \(\delta_{0}\) and \(\beta\) is a dimensionless variable that provides an estimation of the average size of particles in the medium, for instance, if the tissue is composed of small sized particles, \(\beta\approx 4\), which can be modeled as Rayleigh scattering. On the contrary, if larger particles exist in the tissue, \(\beta\) approaches0.37 [35; 36]. However, when a generic tissue is comprised of variable size particles, which is the most common case, the reduced scattering coefficient can be expressed, as in [21; 32; 37],  
\[\mu_{s}^{\prime}=\mu_{s}^{\prime}(\delta_{0})\left(f_{\text{Ray}}\left(\frac{ \delta}{\delta_{0}}\right)^{-4}+\left(1-f_{\text{Ray}}\right)\left(\frac{\delta }{\delta_{0}}\right)^{-\beta}\right), \tag{12}\]  
which corresponds to the combination of the two limiting cases, namely Rayleigh and Mie scattering, with \(f_{\text{Ray}}\) denoting the fraction of Rayleigh scattering due to the existence of small sized particles.
## 5 Discussion  
### Comparison with Existing Methods  
The comparison between the proposed method and the existing WPT control methods is summarized in Table 3. Here, five aspects are compared including the number of DC-DC converters, controller, number of samples, response time, and maximum efficiency [25; 26].  
Figure 12: Comparisons of experimental system transmission efficiencies with and without the maximum efficiency tracking control. (**a**) The conditions of load resistance variation. (**b**) The conditions of mutual inductance variation.
# An RF-MEMS-Based Digitally Tunable SIW Filter in X-Band for Communication Satellite Applications
## 3 Tunable Filter Design
### Implementation of Tuning Elements  
The challenge then became how to make the filter tunable. So far, we have detailed our tunability solution using wire bondings. This technique was then adapted to our specifications with a combination of electrically controlled devices, using a commercial RF-MEMS switch from Radant. In comparison with a semi-conductor junction, RF-MEMS technology offers low insertion losses, near zero power consumption, large power handling and very high linearity (>60 dBm) [11; 12; 13; 14; 15]. In [11], the authors claimed that RF-MEMS switches have a figure of merit (FOM) of ~10-20 THz, and that a comparable FOM for semiconductor technologies is 250-500 GHz, which is 20-40 times less effective than that of RF-MEMS. A 90 V actuation applied to the gate provides an electrostatic force on the RF-MEMS cantilever, allowing the RF signal to pass through the source-drain path. For high-frequency structure  
Figure 3: Electric current density along the tuning post for four cases: (**a**) four, (**b**) three, (**c**) two and (**d**) one wire bonding connected to the tuning post.  
simulator (HFSS) purposes, RF-MEMS were modeled by a similar 3D package with an incorporated RLC Model (shown in Figure 4) based on S-parameter data supplied by the manufacturer.  
In Table 1, the results show that there are two configurations that can be used to obtain a frequency shift close to the specifications (250 MHz): states S to SE with the tuning post at the edge (Xpost = 1.9 mm), and states NSE to NWSE with the tuning post at the center (Xpost = 0 mm). It is worth noting that the quality factor is less impacted when the tuning post is placed at the edge.  
We chose to use the first configuration (Xpost = 1.9 mm). In order to command the filter electrically, it was necessary to substitute one of the two wire bondings of the SE state with the RF-MEMS switch for each cavity. Figure 5 shows the desired configuration with the RF-MEMS switch and a wire bonding. According to Radant, the equivalent serial inductance of the two wire bondings used to connect the packaged MEMS Switch is 0.16 nH, while the parallel capacitance of the pad is 0.01 pF. The filter was designed to resonate at 10.825 GHz when the four RF-MEMS were not biased (OFF state = S state). Figure 6 and Table 2 present the top view of the tunable filter and its dimensional parameters, respectively.
## 3 Optimizing In-Orbit SAR Operational Parameters for Altitude Variation
### PRF Optimization
#### 3.2.4 PRF Optimization Results  
Using each of the three methods, PRFs are obtained and compared. Figure 15 shows examples for SW#4 and SW#8, respectively. In each subfigure, the solid lines in red represent the PRF values that are calculated at the nominal altitude with the altitude variation neglected. These constant PRFs are located within the Tx interference zone except for PRF of SW#8 at the maximum altitude. The dotted lines in blue represent the PRF values that are calculated using the ratio between the nominal and predicted altitudes. PRFs are remarkably close to the Tx or nadir interference zone. If the actual altitude is deviated from the predicted one, PRF is highly probable to fall into the interference zone, and consequently, the range ambiguity performance is degraded. In Figure 15, the solid lines in purple represent the PRF values that are optimized using the travelling return time according to the proposed method. Contrary to other methods, PRFs are always located at the center of the clear zone.  
Figure 15: _Cont._  
Figure 16 shows the PRF margins at the minimum and maximum altitudes, respectively. The upper margin is the distance to PRF at the maximum look angle from the Tx or nadir interferences. In similar, the lower margin is the distance to PRF at the minimum look angle from the Tx or nadir interference. As shown in Figure 16, the margins for PRF and the slant angle are different when PRF is optimized using the second method, i.e., calculated using the ratio between the nominal and predicted altitudes. Especially, the margins are ridiculously small for several swaths, so the PRF margin is reduced below 5 Hz while the slant range margin below 6 m. The proposed method, however, results in the margins that are the same irrespective of the altitude variation. Also, no significant reduction in the PRF and slant range margins is observed.  
Figure 16: _Cont._  
Figure 15: Example of PRF selection: (**a**) PRF of SW#4 at the minimum altitude; (**b**) PRF of SW#8 at the minimum altitude; (**c**) PRF of SW#4 at the maximum altitude; (**d**) PRF of SW#8 at the maximum altitude.