esa-sceva/satcom-chunk-collection huggingface dataset:
# An MMIC LNA for Millimeter-Wave Radar and 5G Applications with GaN-on-SiC Technology
###### Abstract
A plethora of existing literature has documented the development of LNAs exhibiting satisfactory performance across various technologies [20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. As for the CMOS process, scholars of City University of Hong Kong [20] have presented an MMIC LNA which achieves a peak gain of 10.2 dB with a minimum NF of 3.3 dB and bandwidth ranging from 15.8 GHz to 30.3 GHz. Furthermore, the National University of Singapore [24] has put forth a groundbreaking proposal for an ultra-wide bandwidth cascode LNA, utilizing GaAs pHEMT technology. This innovative design boasts a peak gain of 14.1 dB and a notable NF below 4.5 dB, effectively covering an extensive frequency range from 2 to 42 GHz. Ho Chi Minh City University of Technology [25] has presented a 35-37 GHz Ka-band MMIC LNA with a typical NF of 3.7 dB and a small signal gain of above 19.7 dB employing GaN-on-SiC HEMT. A K-band MMIC LNA [26] in GaN-on-Si technology has realized a measured average gain of 17.2 dB and an NF of 2.0-2.3 dB. These advancements pave the way for further research and innovation, enabling the design and implementation of LNAs tailored to specific application requirements and operating conditions. In this letter, we realize a cascaded common-source three-stages [30] MMIC LNA covering 24.25-29.5 GHz with a minimum NF of 2.5 dB and a maximum small signal gain of 20.2 dB based on the 0.15 \(\upmu\)m GaN-on-SiC HEMT process developed by WIN foundry. To enhance the stability of a designed LNA, a series of stability enhancement measures including source degeneration, RC series network, and RF choke [30; 31] are put forward. The hybrid-matching networks (MNs) with a co-design strategy are adopted to realize low NF and broadband performance. Due to the different priorities of these hybrid-MNs, distinguished design strategies are employed. In order to meet the testing conditions of the MMIC, an impeccable system for measuring small signals has been built to ensure the accuracy of the measured results.
## 4 Results  
To verify the effectiveness of our SATTI-DCGAN approach, a set of qualitative and quantitative comparison experiments were conducted. The total number of epochs and the learning rates of the generator and discriminator were set to 5000, 0.0002, and 0.0001, respectively. The hardware configuration for the experiments included an AMD (R) Ryzen (TM) 5 5600H @ 3.30 GHz CPU, manufactured by Advanced Micro Devices, Inc., Austin, TX, USA; an NVIDIA GeForce RTX 3050 GPU, and 16 GB of memory, both manufactured by NVIDIA Corporation, Santa Clara, CA, USA. The experiments were conducted within the Python 3.6 operating environment, utilizing the PyTorch platform (version 1.13.1+cu117) for model implementation. The operating system employed was Windows 11.
# Myoepithelioma of the Hand: A Systematic Review
## 4 Discussion  
Myoepitheliomas are infrequent occurrences, comprising merely 1-1.5% of all salivary gland tumors [23; 24]. They were described in soft tissues in 1995 and, ever since, more and more cases have been described in the literature and, although very rare, they can also occur in bones [13; 14; 17; 19; 20]. Although these tumors commonly emerge around the age of 44 on average, instances have been documented across a broad spectrum ranging from as young as 9 to as old as 85 years [25]. As regards hands, we reported a mean age of 41.3 years old, which is comparable to what is described in the literature. While instances among pediatric populations are documented, they remain exceptionally rare [26]. Furthermore, we found two cases of spindle cell myoepithelioma in the fingers of a 15- and 12-year-old and so far; these are the only cases described in children [27]. Our research highlighted 15 cases of hand myoepithelioma--both on soft tissues and bones--but considering that data were not specified for location in some articles, we had to exclude them, so the number is surely higher [28]. Moreover, it has to be considered that since sometimes the clinical presentation suggest a common lesion such as fibromas, many cases could be missing as no histological examination is conducted. In fact, in the majority of cases discussed, the diagnosis of myoepithelioma is made after histological examination as different differential diagnosis are possible, as also reported in Table 1. The rarity of myoepitheliomas in this location necessitates careful consideration in differential diagnosis, often involving imaging studies such as MRI and histopathological examination for definitive diagnosis.  
Cases of myoepitheliomas in the hand and wrist more often present as painless, slow-growing masses, mimicking more common soft tissue tumors. Despite that, as already said, sometimes the presentation can be extremely variable, leading the physician to other differential diagnosis such as enchondroma, lipoma, and even malignant tumors. Primary intra-osseous myoepithelioma are rare and occur predominantly in the axial and proximal appendicular skeleton in middle-aged patients [14].  
Considering such variety of features, the focus of our discussion is to highlight the main and characteristic features of myoepithelioma as emerged from our review.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**N\({}^{\circ}\) of Patients**} & **15** \\ \hline
**Gender** & \\ Male & 9 (60\%) \\ Female & 6 (40\%) \\ \hline
**Age** & 41.3 y.o. (12–69) \\ \hline
**Location** & \\ Third finger & 5 (33.3\%) \\ Second finger & 3 (20\%) \\ Fourth finger & 1 (6.7\%) \\ Thenar eminence & 3 (20\%) \\ Hypothenar eminence & 3 (20\%) \\ \hline
**Imaging** & \\ None & 4 \\ XRAY & 6 \\ MRI & 1 \\ CT & 2 \\ PET & 3 \\ US & 3 \\ \hline
**Recurrence** & \\ Yes & 1 (6.7\%) \\ No & 14 (93.3\%) \\ \hline \hline \end{tabular} y.o. = years old; MRI = magnetic resonance imaging; CT = computer tomography; PET = positron emission tomography; US = ultrasound.  
\end{table}
Table 2: Demographic and diagnostic data.
## 2 Performance Assessment
### Signal-to-Noise Ratio Simulations
#### 2.2.4 Signal-to-Noise Ratio Simulations at 1064 nm  
The same photodetector modules, transmission factors and background-radiance system constant as for the 905 nm simulations were considered in the simulations at 1064 nm. It should be noted that at 1064 nm, the intrinsic responsivity \(R_{io}\) of the photodiodes is 0.34 A/W. Applying Equation (10), a \(NEP_{m}\) of 0.19 and 19.12 \(\,\mathrm{pW}/\sqrt{\mathrm{Hz}}\) is obtained for the APD and PIN modules, respectively.  
Figure 2: Signal-to-noise ratio _vs._ system constant due to a spray drift cloud located at 500 m for variants 1 to 6 (Table 2).  
Figure 3 shows the signal-to-noise ratio _vs._ the system constant for variants 7 to 12. As at 905 nm, it can be seen that using APD modules system constants are required (\(K_{s}\) between 28.95 and 144.7 W\(\cdot\)m\({}^{3}\) in Table 3) some two orders of magnitude lower than for a PIN module (\(K_{s}\) between 1513 and 7568 W\(\cdot\)m\({}^{3}\)).  
It is concluded that at a wavelength of 1064 nm a system constant is required to be between 1.2 and 1.8 times higher than for 905 nm. This is due to the fall of the intrinsic responsivity of silicon photodiodes at this wavelength, which is due to a reduction in quantum efficiency from 85% to 40%. Using an APD module entails pulse energies (assuming \(D_{r}=50\)-100 mm) of between 25 and 492 \(\upmu\)J.
## 2 Materials and Methods
### Physico-Chemical Parameters of Sediments and Pore Water  
Sediment samples were oven-dried at 45 \({}^{\circ}\)C to remove residual moisture. Dry sediments were disaggregated and sieved, and the <2 mm fraction was used for further analyses. Sediment organic matter (SOM) content was estimated using loss-on-ignition (LOI). The SOM percent was calculated using the following equation: SOM = ((sediment weight after combustion \(-\) oven-dry sediment weight)/oven-dry sediment weight) \(\times\) 100 [31].  
The pH of sediments was determined both in water and in CaCl\({}_{2}\) suspensions (0.01 M), based on the ISO 103090/2005 (ISO 2005) method. For sediments, the relation between mass and volume of suspension was 1:5 (5 g of sediment and 25 mL of water or CaCl\({}_{2}\) solution). The suspensions were stirred for 10 min and then left resting for 24 h. The measurement was conducted with a glass electrode and pH meter (Hanna HI 9025). To determine the electric conductivity (EC), a mixture of 1:2 (10 g of sediment and 20 mL of water) was prepared and stirred for 1 min. The mixture was left resting for 1 h before measurements were made (Hanna Instruments HI 9033 Conductivity Meter). The pH and electrical conductivity of pore water were determined directly in 30-40 mL aliquots. The exchangeable cations sodium (Na\({}^{+}\)), calcium (Ca\({}^{+2}\)), magnesium (Mg\({}^{+2}\)) and potassium (K\({}^{+}\)) were determined in ammonia acetate (1 M; pH = 7) sediment extracts using inductively coupled plasma-mass spectrometry (ICP-MS). Sodium absorption ratio (SAR) and exchangeable sodium percentage (ESP) were determined following the guideline for salinity assessment from the Joint FAO/IAEA (Food and Agriculture Organization/International Atomic Energy Agency) Program [32]. The EC of sediment saturation extract (ECe) was derived from EC values, using the conversion factor described in [PERSON] et al. [33] for Turkey clay soil. Precision was estimated by the relative standard deviation (RSD) of triplicate samples and was \(\leq\)10%.
# Robotic Systems and Navigation Techniques in Orthopedics: A Historical Review
## 3 Computer-Aided Navigation Systems (CANSs)
### Three Basic Elements
#### 3.1.2 Registration  
Registration is a core and compulsory procedure for any CANS system or RS system being used in orthopedic surgeries. The quality of registration fundamentally and directly determines the accuracy and precision of the surgical outcomes [17]. It is worth mentioning that both RS and CANS are heavily reliant on registration, with RS being more so than an independent CANS due to the fact that the surgical work is performed by the robotic-attached tool in RS rather than the surgeon alone [17].  
Before the surgery in the operating room, registrations between the patient's true bone, the corresponding 3D model, the robotic system (if applicable), and the surgical tool need to be conducted first. Some methods may be needed to ensure accurate registration. There are three typical registration methods, including fiducial-based paired-point matching, surface-based, and 2D/3D-3D registration [25]. ROBODOC initially used fiducial-based registration and changed to surface-based registration in 1999; studies have proved that surface-based registration is as accurate as fiducial-based registration [25].  
The fiducial-based paired-point matching method is a modified version of the paired-point matching method [25]. To obtain high-accuracy and reproducible registration, fiducials need to be placed into the target bone before the preoperative CT scanning such that these fiducials appear in the CT images. These fiducials are used for registration by using the paired-point matching method. During the surgery, these fiducials are used as the reference points for the patient's bone, and related to the preoperative plan. However, fiducial-based registration requires an additional minor operation of placing the fiducials into the bone before the preoperative CT scanning, which brings fiducial site pain or inflammation to the patients [25].  
Surface-based (also known as shape-based) registration does not need fiducials. It employs the widely used iterative closest point (ICP) algorithm and the least-squares method to match the points on the 3D model surface with those on the patient's bone surface [70; 71]. To avoid local minima, the paired-point method is used first to perform baseline registration, then a certain number of points is used to perform a refinement of the surface-based registration [25]. An advantage of surface-based registration is that it can be conducted and updated intraoperatively and in real time, which makes it more robust than other registration methods.  
The 2D-3D or 3D-3D registration method makes use of intraoperative fluoroscopic images [25]. Although this method has been shown to be accurate for use in robot-assisted THA in a lab setting, it has not yet been widely accepted by clinical robotic surgeries [25].
The response of ecological units (including ecosystems, communities, populations, and even individuals) to drought is a process of resisting and recovering during and after disturbance [27]. In addition to the resistance of plants to drought, the recovery of plants to drought has also been a concern. Resilience can reduce the negative effects of drought on the ecosystem [28]. The different recovery rates of the co-existence species could alter the plant community after drought disturbance [29]. Maintaining live roots under drought conditions played an important role in the plant recovery process [30]. Leaf traits were also tightly correlated with the resilience of plants to drought [30]. Evaluation of the direct role of species differential drought resilience is crucial for evaluating their distributions in grasslands, which is necessary for projecting the consequence of changes in drought regime; however, species with what kind of functional trait recover more rapidly after a period of drought is still unclear. Considering grasslands occupy 40% of global land area and their dominant role in the variability of land carbon sink, the semi-arid grassland of Inner Mongolia, Northern China, has a high biodiversity with important ecological functions and is an integral part of the Eurasian steppe [31; 32; 33; 34]. Exploring the effects of drought on grasslands is thus critical for better understanding terrestrial biosphere response to climate change. In this study, a field experiment was conducted to better understand the responses of dominant plant coverage and abundance to extreme experimental drought in a semi-arid steppe on the Mongolian Plateau. Two precipitation levels were set in the experiment, including the ambient precipitation as a control and the 100% removal of precipitation as a treatment. We tested the two scientific questions: (i) how do community coverage and abundance respond to precipitation changes? (ii) which biotic and abiotic factors determine these response patterns?
## 5 Discussion
### Potential Benefits and Applications of the Product  
This study showed that the NI-LUE GPP had a high consistency with the FLUXNET GPP. The comparison of NI-LUE GPP with other GPP products also reflected the rationality of the interannual variation and spatial pattern of NI-LUE GPP. The above results suggest that NI-LUE GPP has the potential to complement current global GPP products.  
In terms of analyzing global or regional GPP spatial patterns, NI-LUE GPP may discover valuable insights that have not been found in other products due to the consideration of the spatial heterogeneity of optimal temperature and \(\varepsilon_{max}\).  
Since the NI-LUE model simultaneously considered temperature, water, radiation components, atmospheric CO\({}_{2}\) concentrations, and NI, NI-LUE GPP could be used in both long-term GPP trend analysis and change detection. It may have great potential and may help us to identify and quantify the drivers affecting the long-term GPP trends.  
In addition, there are significant differences among the many current global GPP products (Figure 13a). Like these global GPP products, NI-LUE GPP cannot be regarded as the actual value of GPP, but it can be used as a new global GPP dataset for comparison with other products or models, which helps to understand the performance of the dataset comprehensively.
## 4 Error Analysis of the Laser Processing Platform for MCR Frequency Modulation
### Error Analysis of the Laser Processing Platform for MCR Frequency Modulation  
The positioning errors of the end of the frequency test probe in the X, Y, and Z directions are basically subject to the normal distribution characteristics. Most of the error data fit the normal distribution curve. The average of Ex is the largest, the standard deviation of Ey is the largest, and the average error and standard deviation of Ez are the smallest.  
According to the requirements in Table 1, the positioning accuracy of the frequency test probe in the X direction is required to be 300 \(\upmu\)m, so the requirements can be relaxed in this direction during tuning. If there is an initial positioning error, it can be adjusted by changing the initial positioning value of the \(X\)-axis in the tuning program. The end positioning accuracy of the frequency test probe in the Y direction is required to be 100 \(\upmu\)m. The simulation results show that the standard deviation of the \(Y\)-axis positioning error is large, and there is a risk of shedding. During the tuning of the laser processing platform for MCR frequency modulation, the Y-side should be adopted as the baseline, thus initial end positioning of the frequency test probe in the Y direction should be paid attention. If there is an initial positioning deviation, it can be corrected by adjusting the relative position between the stage and the \(Z\)-axis lifting motor or adjusting the Y-side positioning reference of the stage. The end positioning accuracy of the frequency test probe in the Z direction is required to be 100 \(\upmu\)m. The simulation results show that there is a risk of losing the contact between the frequency test probe and the MCR. In order to achieve accurate frequency measurement and avoid damage to the circuit of the MCR, the frequency test probe of the laser processing platform should retract 30 \(\upmu\)m in the Z direction.  
Figure 11: Positioning error of probe end. (**a**) Ex distribution at the end of the probe, (**b**) Ey distribution at the end of the probe, and (**c**) Ez distribution at the end of the probe.  
Experiment on Precision and Frequency Modulation Characteristics of the Laser Processing Platform for MCR
## 3 Experimental Investigation
### Experimental Set-Ups
The evaluation of the surface temperature sensors is done by two Wheatstone half bridges, shown in Figure 4. The first half bridge, which consists of the two temperature sensors \(R_{A,1}\) and \(R_{A,2}\) is located at the undamaged structure, whereas temperature sensor \(R_{B,2}\) of the second half bridge,which consists of \(R_{B,1}\) and \(R_{B,2}\), is directly located at the damage. The measurement signal acquisition of the two Wheatstone bridges is done by a HBM Quantum X MX840A. Applying these half bridge circuits enables to measure only the temperature difference between the connected temperature sensors, and thus allows compensating global temperature fluctuations of the structure. Global temperature changes of the structure can result from changes of the surrounding temperature, thermal radiation, warming of the structure due to loading, and so forth. Figure 4: Experimental set-up of the temperature-based damage identification experiment (detail B is a scaled magnification of the area of interest). Figure 3: Experimental set-up of the scanning laser Doppler vibrometer measurements (detail A is a scaled magnification of the area of interest).
## 3 Solving Method
### Testing and Local Search  
The output of the ADN is the probability distribution for the task selection. During the testing process, the trained ADN was used to determine the task scheduling sequence,  
Figure 5: Structure of the critic network.  
and the action with the maximum probability was selected. Algorithm 2 describes the testing process.  
```
Input: Trained ADN, \(S_{t+0}\), \(done\)-False, total profits\(\leftarrow\)0, task profit \(P_{a}\) Output: The start time of scheduled tasks, total profits
1:while not done do
2: Input \(S_{t}\) into ADN, get the probability distribution for the tasks
3: Select task \(a\) with the maximum probability
4: Determine the start time of \(a\), start \(a\) at the earliest feasible time
5:\(S_{t}\gets S_{t+1}\), update \(done\), total profits = total profits + \(P_{a}\)
```  
**Algorithm 2** Testing process  
During the test process, the task selected at each time step starts at the earliest feasible time point. The process terminates when all tasks are scheduled or when there are no feasible tasks, resulting in an observation plan. For a scheduled task, we define the feasible shifts in the start time according to Algorithm 3. In Algorithm 3, \(FTI=\{interval_{1},interval_{2},\cdots interval_{k},\cdots\}\), \(interval_{k}=\{start_{k},end_{k}\}\) (refer to Section 2.2), and length_FTI represents the number of intervals in the \(FTI\).  
```
Input: Observation plan \(\sigma\), initial feasible time intervals of task \(a\): \(FTI\) Output: The feasible shifts of start time for a scheduled task
1: Feasible shifts: \(FS\leftarrow\varnothing\)
2:for task \(i\) (\(i\
eq a\)) in \(\sigma\)do
3: update \(FTI\) according to the time interval occupied by \(i\) and the transition time between \(i\) and \(a\)
4:endfor
5:for\(k=1,2,\ldots,\)length_FTIdo
6:if\(k==1\)then
7:\(FS\cup(end_{k}-d_{a})\)
8:else
9:\(FS\cup start_{k}\), \(FS\cup(end_{k}-d_{a})\)
10:endif
11:endfor
```  
**Algorithm 3** Compute feasible shifts of start time for scheduled task \(a\)  
Based on Algorithm 3, we propose a start-time-shift-based local search to improve the observation plan. The local search process is presented in Algorithm 4. Start-time-shift-based local search aims to insert feasible unscheduled tasks into the observation plan by shifting the start times of the scheduled tasks (where length_FS\({}_{i}\) represents the number of elements in \(FS_{i}\)). We denote the ADN combined with the start-time-shift-based local search as ADN_LS. Obviously, the result of ADN_LS is not inferior to that of ADN.  
```
Input: Observation plan \(\sigma\), scheduled task set \(ST\), unscheduled task set \(UT\) Output: Improved observation plan
1:for task \(i\) in \(ST\)do
2: Compute \(FS_{i}\) using Algorithm 3
3:for\(k=1,2,\ldots,\)length_FS\({}_{i}\)do
4: Set the start time of \(i\) as \(FS_{ik}\)
5:for\(j\) in \(UT\)do
6:if\(j\) is feasible then
7: Insert \(j\) into \(\sigma\), \(ST\cup j\)
8: Set the start time of \(/\) as the earliest feasible time
9:endif
10:endfor
11:endfor
12:endfor
```  
**Algorithm 4** Start-time-shift-based local search