esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Leading
Under the optimal conditions, more than 99.99% of Ga, In, and Zn were leached and then adsorbed and eluted with a CL-P204 solvent-impregnated resin [52]. It is possible to use mild leaching conditions to recover mainly silver and indium from flexible CIGS solar cells. The results show an increase in the leaching efficiency for Ag and In when acid concentration and a material surface-area-to-liquid ratio (A/L) increase. However, attention should be drawn to the fact that a level of contaminants (from other industrial elements) in the after-leaching solutions is also increasing [52]. The solution to this may be selective leaching of elements, aiming to clean the streams of metals and thus reusing them in new products. Total Ag recovery and 85% recovery efficiency for In can be obtained using 2 M HNO\({}_{3}\) and an A/L ratio of 1:3 cm\({}^{2}\)/cm\({}^{3}\) after 24 h of leaching at room temperature. Under the same conditions, leaching with 0.5 M HNO\({}_{3}\) recovers 85% of Ag and 30% of In, but correspondingly reduced impurity levels are achieved. Finally, leaching with 0.1 M HNO\({}_{3}\) can achieve higher Ag purity in the initial Zn selective leaching step for 1 h [53]. Selectively recovering Ga from gallium-bearing waste (MOCVD waste) of the LED industry by using an oxidation process coupled with NaOH leaching is another innovative and green recycling technology. In the process, GaN and In are converted to alkali-soluble Ga\({}_{2}\)O\({}_{3}\) and alkali-insoluble In\({}_{2}\)O\({}_{3}\) via oxidation calcination. Via the selective leaching with NaOH solution under the conditions of 2 mol/dm\({}^{3}\) NaOH, an S/L ratio of 50 g/dm\({}^{3}\), a leaching temperature of 90 \({}^{\circ}\)C and a leaching time of 180 min, nearly 92.65% of Ga can be recovered with a leaching selectivity of 99.3%. As a result, Ga\({}_{2}\)O\({}_{3}\) with a purity of 99.97% can be obtained from the solution [54]. Some combinations of mixtures for the leaching and the additional treatment, that provide the best results, are listed in Table 2.  
\[4\text{GaN}_{(g)}+3\text{O}_{2(g)}\to 2\text{Ga}_{2}\text{O}_{3}+2\text{N}_{2}, \tag{1}\]0 Speciation of In(III) aqueous species as a function of pH at 25 \({}^{\circ}\)C (**a**) and the distribution of chloride (**b**) and sulfate (**c**) complexes of In(III) plotted against chloride and sulfate(VI) concentration, respectively. Adapted from [49; 50].
## 5 Data Simulation
### LRI1B Simulation
#### 5.4.4 Time Tag Error  
The LRI time is realized by the onboard ultrastable oscillator (USO) for each satellite. Since the USO time is different from the GNSS time due to the clock rate difference, a time  
Figure 6: Simulated instantaneous range and noise contributors in terms of ASD.  
tag correction term \(t_{\varepsilon}\) provided in the CLK1B data product is needed to convert the LRI time tags to GNSS time tags (it is more complicated for the LRI in GRACE-FO: the LRI time is first converted to the onboard computer (OBC) time using the data reports; then the TIM1B data product provides the time tag difference between the OBC time and the instrument processing unit (IPU) receiver time; finally, the CLK1B data product is used to convert the IPU receiver time to the GPS time). After applying the time tag correction, the residual time tag error is dominated by the error from the GNSS POD and clock solution. We used the following ASD model to simulate the error of \(t_{\varepsilon}\) for each satellite [32]:  
\[\text{ASD}[\delta t_{\varepsilon}](f)=\frac{1.5\,\text{mm}/\text{Hz}^{1/2}}{c }\times\left(\frac{f}{1\,\text{Hz}}\right)^{-1/2}+\frac{10\,\upmu\text{m}/ \text{Hz}^{1/2}}{c}\times\left(\frac{f}{1\,\text{Hz}}\right)^{-5/4}. \tag{48}\]  
This model is an approximation of the clock measurement noise in the Deep Space Atomic Clock (DSAC) mission [33]. Furthermore, to form a two-way phase observation, the transponder (SAT2) phase, whose time tags already contain the CLK1B time tag error \(\delta t_{\varepsilon,2}\), is required to interpolate the time tag to \(t_{2}=t_{1}-\tau_{21}\), indicating that the error of light travel time \(\tau_{21}\) determined by the POD also induces a time tag error, which was not mentioned in [32]. The time-of-flight \(\tau_{21}\) can be obtained in the calculation process of the TOF effect and LTC mentioned in Section 5.4.1. Based on the discussion, we have  
\[\delta t_{1} =\delta t_{\varepsilon,1}, \tag{49}\] \[\delta t_{2} =\delta t_{\varepsilon,2}-\delta\tau_{21},\]  
where \(\delta t_{\varepsilon,1}\) and \(\delta t_{\varepsilon,2}\) were generated according to Equation (48), and \(\delta\tau_{21}\) represents the TOF (from transponder to master) difference between the calculation results from the POD and forward procedure. The range error caused by the time tag error (cyan trace in Figure 6) can be computed as  
\[\delta\rho_{\text{timetag}}=c\frac{f^{\text{OPLL}}}{2\bar{f_{1}}}(\delta t_{2 }-\delta t_{1}). \tag{50}\]  
where \(f^{\text{OPLL}}\approx 10\,\text{MHz}\).
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 2 Related Works
Some researchers have been inspired by biological behaviors and have applied these to clustering the FANETs. In [23], an ant colony optimization (ACO) clustering scheme was proposed for optimal clustering and data transmission in FANETs. CHs are vertices in the search space, and each round provides the set of CHs from specific environments. The algorithm uses two objective functions to evaluate the fitness value of each cycle, namely the Euclidean distance and the delta difference. In [24], the grey wolf optimization (GWO) algorithm was proposed for an energy-efficient routing protocol. In [25; 26], a particle swarm optimization (PSO) algorithm was proposed to perform a clustering strategy, which simulated swarm intelligence to solve optimization problems. In [27], a bio-inspired clustering (BIC) algorithm was proposed for FANETs. The algorithm is a hybrid scheme of glowworm swarm optimization (GSO) and the krill herd (KH) algorithm to achieve efficient clustering management. The UAV node with the highest fitness value is selected as the CH in a cluster, while the remainders are CMs, which can improve adaptability and reliability in FANETs. Table 1 summarizes the performance positioning of the discussed clustering schemes in FANETs. From Table 1, we can observe that the proposed IWLC scheme comprehensively considers the stability, mobility, energy efficiency, clustering overhead, and effective data transmission in the clustering network.
## 3 Results and Discussion
### Comparison between Reference Method and Analysers  
The analysers exhibited reasonable precisions, but the accuracy varied at different NH3 levels (Figures 2 and 3). Over the complete observed concentration range, INNOVA had the lowest RMSE (0.46 ppmv) as compared to the wet-chemistry method. In the poultry barn and pig barn tests, the lowest RMSE was, respectively, 0.24 ppmv with Picarro and 0.56 ppmv with INNOVA. At 0.8 ppmv in the poultry barn without chickens the analyser RMSE varied between 0.14-0.21 ppmv, whereas Gasmet was the lowest and INNOVA the highest. For 2.4-3.4 ppmv in the poultry barn with chickens, Picarro, Rosemount, and INNOVA had RMSE <0.29 ppmv. Gasmet overestimated the NH3 concentration by 0.64 ppmv. For 14-19 ppmv NH3 in the pig barn, all of the analysers, except INNOVA tended to overestimate the concentrations by 1-2 ppmv.  
Figure 1: Time series of the measured ammonia (NH3) by five gas analysers and the temperature in the pig barn. For better comparability and clarity, the data from Picarro, Rosemount and Axetris are downsampled to every 130 s based on moving averages. INNOVA and Gasmet are presented at 130 and 60 s measurement interval, respectively.  
Table 3 provides a summary of the criteria and results of the equivalence assessment. The minimum acceptance level of the correlation coefficients is \(r>0.97\) for the method equivalence assessment. This criterium was compliant by all analysers, including Axetris after correcting for the expended observation range. The acceptable range of the regression intercept (\(|\beta_{0}|<0.535\)) and slope (\(|1-\beta_{1}|<0.056\)) were determined based on the 11.2% maximum uncertainty of the wet-chemistry method found under laboratory conditions [26]. For these two parameters, INNOVA and Rosemount were both below the acceptance threshold. Picarro, Axetris, and Gasmet did not pass the slope criterium likely due to suboptimally executed calibrations or a lack of maintenance. For Axetris, although the device tended to underestimate the NH\({}_{3}\) concentration under sub-atmospheric pressures and was compensated accordingly, the correction was insufficient to explain the 12.2% positive bias  
Figure 3: The scatter plot (**left**) and relative bias (**right**) between the wet-chemistry method and five gas analyser (\(\circ\) INNOVA, \(\triangle\) Picarro, \(+\) Rosemount, \(\times\) Axetris, \(\diamond\) Gasmet) measurements on ammonia (NH\({}_{3}\)) in a pig barn. The dashed line denotes 1:1 regression.  
Figure 2: The scatter plot (**left**) and relative bias (**right**) between the wet-chemistry method and five gas analyser (\(\circ\) INNOVA, \(\triangle\) Picarro, \(+\) Rosemount, \(\diamond\) Gasmet) measurements on ammonia (NH\({}_{3}\)) in a poultry barn. The dashed line denotes 1:1 regression.  
with respect to the wet-chemistry method; the observed pressure remained at 846 \(\pm\) 3 mbar throughout the test period, which only accounted for \(-\)1.8% expected bias.
## 4 Pregrasp Strategy for the Robotic Hand: Placement of the Fingers  
In this section, we present the pregrasp strategy for reaching the three contact points of the initial grasp computed in Section 3 with a three-fingered robotic hand (e.g., the Barrett hand [50] in our experiments). In order to describe the kinematic model of the Barrett hand, we establish the joint frames of Figure 4. The movement of the fingers of the Barrett hand is controlled by defining 4 encoder values \([q_{M1}\ldots q_{M4}]\) that are connected to the joint values \(\varphi_{i}\) of the fingers (Figure 4) by the following coupling relationships:  
\[\begin{bmatrix}\phi_{98}\\ \phi_{109}\\ \phi_{1211}\\ \phi_{1312}\\ \phi_{147}\\ \phi_{1514}\\ \phi_{87}\\ \phi_{117}\end{bmatrix}=\begin{bmatrix}1/125&0&0&0\\ 1/375&0&0&0\\ 0&1/125&0&0\\ 0&1/375&0&0\\ 0&0&1/125&0\\ 0&0&1/375&0\\ 0&0&0&2/35\\ 0&0&0&-2/35\end{bmatrix}*\begin{bmatrix}q_{M1}\\ q_{M2}\\ q_{M3}\\ q_{M4}\end{bmatrix}, \tag{3}\]  
where \(q_{Mk}=\begin{bmatrix}0&17,500\end{bmatrix}\) for \(k=\begin{bmatrix}1&2&3\end{bmatrix}\) (i.e., the range of variation of the encoders of the motors for closing/opening the three fingers), and \(q_{M4}=\begin{bmatrix}0&3150\end{bmatrix}\) (i.e., the range of variation of the encoder of the motor 4 for separating fingers 1 and 2 around the palm).  
The pregrasp strategy for the three-fingered robotic hand involves two steps: the first one aligns the orientation of the hand with the initial grasping triangle computed in Section 3; the second one adjusts this initial estimation of the grasp by taking into consideration the kinematic constraints of the hand. The first step consists of orientating the hand so that the TCP (Tool Center Point, defined along the axis perpendicular to the palm of the hand) coincides with the \(C_{T}\) grasp triangle center (as shown in Figure 5). The second step involves the resolution of the IK of the fingers in order to estimate the joint values to reach the three grasp points (\(P_{1}\), \(P_{2}\), and \(P_{3}\)) of the initial grasp triangle. If no IK solution is found, the length of the TCP line is changed and the IK resolution is recomputed. When an IK solution is found, the motor commands \(q_{Mk}\) are computed in order to attend to these joint values, by applying the inverse expression of (3). The implemented algorithm is shown in the diagram \"Algorithm 2\". In the context of this iterative solution search, we consider that the solution is validated for a value of joint angle difference for each of the fingers smaller than 2 degrees.  
Figure 4: Barrett hand: dimensions, frames, and joints.
# An Automatic Geometric Registration Method for Multi Temporal 3D Models
## 4 Experiment Results and Discussions
### Accuracy Analysis of 3D Model after Registration  
In order to further evaluate the positioning error between the registered 3D model and the base 3D model, we accurately measured the displacement of seven spray-painted stones between the two sequential 3D models.  
Figure 12 shows the screenshots of the sprayed stones numbered C1 and C5 in the two sequential 3D models. The GNSS-RTK equipment was used to measure the spatial position of the sprayed stones before the two UAV data collections, and their spatial displacement was calculated and used as the true value. Using the same three 3D model data as in Figure 11, the displacement of the sprayed stones in the sequential 3D model was calculated after manually measuring their spatial position in the 3D model. The actual displacement obtained from GNSS-RTK equipment was subtracted from the model displacement measured manually to obtain the difference in displacement of the seven sprayed stones, and the results are shown in Table 3.  
Figure 12: The sprayed stones in the two sequential 3D model. P1 represents the 3D of the first period, and P2 represents the second period.  
The GPS column in Table 3 shows the actual spatial displacement of the sprayed stones. Based on analysis of the results in Table 3, the sequential 3D model generated by conventional POS data shows that the average value of the difference in spatial displacements was \(-2.47\) m, far exceeding the actual spatial displacements of the sprayed stones. So, 3D models generated based on conventional POS data cannot be directly used for ground object change detection. The average value of the spatial-displacement difference was reduced to \(0.08\) m after using PPK data, which is about three times the GSD. The results show that the multi-period 3D model generated by PPK data can be localized and discovered when the spatial-location-change value of ground objects exceeds three times the GSD. In contrast, the average of the spatial displacement difference was as low as \(0.01\)m for the model-registration method in this paper. The results in Table 3 show that the relative distance measurement error of the aligned 3D model was less than \(0.01\) m. The results show that the 3D model generated based on the method in this paper has a highly uniform spatial reference for the multi-temporal 3D models and can be directly applied to the change detection of the multi-temporal 3D models.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 4 Experimental Results and Discussion  
### Experimental Settings  
**Experimental Environment:** In our experiments, we use a Ubuntu 20.04 server (created by Canonical Ltd, in Isle of Man, UK) with Intel Xeon E5-2650 V2 CPU and a GeForce Tesla P40 GPU for simulations.  
**Dataset:** In terms of datasets, we conduct experiments on two datasets, indoor scenarios, and urban canyon scenarios. The indoor scenario dataset contains 14,448 CSI matrices, of which 11,559 are used for training and 2889 are used for testing, with a split rate of 0.8. The urban canyon scenario dataset contains 11,628 CSI matrices, which are also split into training and test sets with a ratio of 0.8. The datasets used by us can be obtained at [[https://doi.org/10.21227/jsat-pb50](https://doi.org/10.21227/jsat-pb50)]([https://doi.org/10.21227/jsat-pb50](https://doi.org/10.21227/jsat-pb50)), we have accessed it on 3 May 2020. This two different datasets come from [20]. We describe the extraction process of the dataset as follows. Step 1: Each point's ray information is obtained with a method that through the aid of the ray-tracing models and completes the electromagnetic analysis of the physical scenario. Step 2: Use a multipath channel filter to package the multipath propagation information into a container with the unit of the user device (UE). Finally, running a system-level simulation of the 5G NR air interface which conforming to the 3 GPP standard to obtain the CSI matrices which is the DECC's input. In deep learning terms, the input features are the CSI matrices, and the output target is the UE's position in the three-dimensional space.  
**Training Details:** In all training sessions, we use the same parameters for quantitative and qualitative comparisons. During 300 training epochs, the learning rate is set to \(2\times 10^{-3}\), and the periodic learning rate drop schedule is 50/0.5 (epoch/drop-factor). The optimizer we used is same as that in [38]. During each training process, the testing set is used to evaluate the validation accuracy of the current model. At the same time, the accuracy of the training set is recorded to measure the generalization performance of the model. The training process will be analyzed in the following parts of this section.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 4 Vehicular Ad-Hoc Networks (VANETs)
### Vehicle-To-Everything (V2X) Communication  
The V2X communication allows the vehicle to communicate with other entities such as pedestrians (V2P), roadside (V2R), devices (V2D) and the Grid (V2G) [133]. This communication is used to prevent road accidents with vulnerable pedestrians, cyclists and motorcyclists [20]. The V2X communication allows the Pedestrian Collision Warning (PCW) mechanism to alert the roadside passenger before any serious accident takes place. The  
Figure 5: Vehicular communication (VC) system.  
PCW can access the Bluetooth or Near Field Communication (NFC) of the smartphone and may use beacon stuffing to deliver critical messages to the pedestrian [4].
# Identification and Optimization Study of Cavitation in High Power Torque Converter
## 4 Optimized Design of Cavitation in High Power Torque Converter
### Optimized Design of Torque Converter Stator  
The design parameters of the stator are mainly divided into mean camber line parameters and thickness parameters. The mean camber line parameters are inlet angle \(\alpha_{inD}\), outlet angle \(\alpha_{outD}\), blade camber line deflection angle \(\gamma_{D}\), peak position coefficient position coefficient \(x_{hD}\), peak height coefficient \(y_{hD}\), and chord length \(L_{D}\)[22]. The thickness and mean camber line parameters are similar and will not be repeated, as shown in Figure 9.  
Currently, there are more studies on the relationship between the mean camber line parameters of the stator and the torque converter performance, but fewer studies on their relationship with cavitation. To investigate the impact of stator nose characteristics on cavitation, the \(\alpha_{inD}\), \(\gamma_{D}\), \(x_{hD}\), \(y_{hD}\), and \(L_{D}\) were varied to achieve different \(\Delta\beta_{Dif}\). The \(\alpha_{outD}\) was kept constant and the shape of the back half of the blade peak remained unchanged. Six schemes (1-6) were formed as shown in Table 5.  
Figure 8: Schematic diagram of liquid flow injection deviation angle.  
Figure 9: Parameterized 2D blade profile.  
The 2-D blades of different schemes are shown in Figure 10, from which it can be seen that by adjusting the parameters, the \(\alpha_{outD}\) of the blade, the shape of the second half of the peak of the blade is basically unchanged, while the shape of the head of the stator blades has undergone a major change, and the turbine exit angle of the liquid flow forms a different degree of \(\Delta\beta_{Dif}\).
# A Distributed Terahertz Metasurface with Cold-Electron Bolometers for Cosmology Missions
###### Abstract  
We developed and tested a 2D periodic array of cold-electron bolometers arranged into a wideband frequency selective metasurface that absorbs more than 70% of the incident power in the frequency range 100-800 GHz. The array had \(10\times 10\) unit cells, each containing four bolometers incorporated into a ring. The chip with bolometers was mounted on the back side of the silicon lens without a back-reflector. Preliminary experiments demonstrated voltage responsivity as high as \(10^{9}\) V/W for the current-biased series array. Simulation of the noise performance shows realization of background noise-limited performance with \(NEP_{tot}<NEP_{phot}\) for the optical power load \(P_{0}>15\) pW. Results of numerical simulation made for the unit cell of the array are presented together with the equivalent diagram based on lumped network elements. The unit cell also was developed numerically to operate in two radiation modes.  
cold-electron bolometer; frequency selective metasurface; terahertz detector; cryogenic device +
Footnote †: journal: Applied Sciences
###### Abstract  
An analytical method to design a power amplifier (PA) with an optimized power added efficiency (PAE) trajectory for envelope tracking (ET) architecture is proposed. To obtain feasible matching solutions for high-efficiency performance of the PA in the dynamic supply operation, hybrid continuous modes (HCM) architecture is introduced. The design space for load impedances of the HCM PAs with nonlinear capacitance is deduced mathematically using the device's embedding transfer network, without the necessity of using load-pull. The proposed design strategy is verified with the implementation of a GaN PA operating over the frequency range of 1.9 GHz to 2.2 GHz with PAE between 67.8% and 72.4% in the 6.7 dB back-off power region of the ET mode. The ET experimental system was set up to evaluate the application of the PA circuit. Measurement results show that the ET PA at 2.1 GHz reaches the efficiency of 61%, 54%, 44% and an error vector magnitude (EVM) of 0.32%, 0.60%, 0.67% at an average output power of 34.4 dBm, 34.2 dBm, 34.1 dBm for 6.7 dB peak-to-average power ratios (PAPR) signals with 5 MHz, 10 MHz, and 20 MHz bandwidths, respectively. Additionally, tested by a 20 MHz bandwidth 16 quadrature amplitude modulation (QAM) signal, 41.8% to 49.2% efficiency of ET PA is achieved at an average output power of 33.5 dBm to 35.1 dBm from 1.9 GHz to 2.2 GHz.  
envelope tracking (ET); power amplifier (PA); continuous modes; digital pre-distortion (DPD); high efficiency  
[PERSON] 1,*2, [PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 1