esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, we present an area-efficient noise-optimized programmable 4 x 25-to-28.9 Gb/s optical receiver. Both high- and low-power modes are available for the receiver to meet different requirements. Emitter degeneration provides the input transimpedance amplifier (TIA) stage with improved stability. The noise of the TIA with emitter degeneration is analyzed, and an improved noise optimization method for the TIA is proposed. A sink current source with emitter degeneration in a DC offset cancellation (DCOC) loop reduces the noise introduced by the DCOC circuit. Moreover, with parasitic capacitor utilization in the DCOC loop and capacitive emitter degeneration in the variable-gain amplifier (VGA) stage, the chip area is minimized. Fabricated in a 0.13 um SiGe BiCMOS technology, the receiver achieved a small area of 0.54 mm\({}^{2}\) per lane. The measured bit error rate (BER) is \(10^{-12}\) with input signal varying from 110 umApp to 1150 umApp. The one-lane power dissipation values in the low-power and high-power modes are 84.97 mW and 123.75 mW, respectively.  
Keywords:4 x 25-to-28.9 Gb/s; parasitic capacitors utilization; programmable; area-efficient; noise optimization; improved stability +
Footnote †: journal:
## 5 Conclusions  
From this research, it was possible to conclude that (i) the chemical nature related to fatty acids (composition and relative amount of FAs) of RM was preserved in all SC samples analyzed; moreover, this food was a source of MUFAs, n-3, and n-6 and had acceptable values of AI and TI; (ii) the manufacturing process of SC influenced the composition of the volatile fractions as demonstrated by the variability of some of its constituents; however, the chemical volatile profiles with negligible variabilities were found in the SC prepared using raw milk and spontaneous fermentation and, in consequence, it is advisable to standardize the process (without affecting the characteristic of the artisanal product), as well as to implement good manufacturing practices; and last of all, (iii) the principal types of molecules (differentiators) that would support the possible establishment of chemical identity for this food product (for 75-88% of the SC samples) were ethyl esters [C\({}_{2}\)-C\({}_{12}\) and C\({}_{10:1}\)(\(\Lambda^{9}\))-C\({}_{18:1}\)(_cis_-\(\Lambda^{9}\))], aliphatic alcohols [(\(2E\))-tridecen-1-ol, ethanol, and isoamyl alcohol], aldehydes (palmitaldehyde and myristylaldehyde), methyl alkyl ketones (2-pentadecanone, 2-tridecanone, 2-undecanone, and 2-nonanone), sesquiterpenes (\(\beta\)-caryophyllene, \(\alpha\)-humulene, and selinane), monoterenes (limonene), benzoic acid, and benzenethanol, along with SCFAs and acetoin (resulting from the spontaneous fermentative process).
## 4 Influence of 3D Printing Parameters on the Fatigue Life of FFF Prints
### Infill Pattern
The best tensile fatigue performance was observed in (\(45/-45^{\circ}\)) grid specimens, followed by \(30/-60^{\circ}\), \(15/-75^{\circ}\), and \(0/-90^{\circ}\) samples. Although the tensile and bending fatigue performance of different infill patterns have been studied in a decent number of studies, other patterns can be subjected to further investigations. Cross-over patterns, such as hexagonal, honeycomb, and gyroid, are of particular interest due to their excellent performance under static loading [22; 77]. In contrast to the infill pattern, pore patterns are used to fabricate porous FFF prints. In other words, the hole pattern determines the shape and arrangement of the holes (void spaces), while the infill pattern denotes the pattern of solid (infill) sections, i.e., extruded filaments. Similar to the infill pattern, pore patterns can substantially affect the fatigue behaviour of prints. [PERSON] and [PERSON] [38; 61] fabricated a series of cylindrical PLA prints with grid (\(0/90^{\circ}\)) and hexagonal (\(0/60/120\)) hole patterns and studied their compressive fatigue properties. They observed that the grid (\(0/90^{\circ}\)) pattern performed better than the hexagonal pattern in terms of the number of cycles to failure in similar loading conditions. In another study, cylindrical PLA specimens were 3D-printed with grid (\(0/90^{\circ}\)), hexagonal (\(0/60/120\)), and concentric radial hole patterns and were subjected to sinusoidal compressive fatigue loads [62]. The grid (\(0/90^{\circ}\)) pattern exhibited the highest number of cycles to failure (\(4400\) cycles), followed by hexagonal (\(3200\) cycles) and radial concentric (\(2500\) cycles) patterns. [PERSON] et al. [63] analyzed the compressive fatigue resistance of porous PLA prints produced in triangular and circular hole patterns. Both groups were 3D-printed with the same infill density (\(40\%\)) and were subjected to static and compressive fatigue loading. While PLA prints with a triangular pattern possessed a higher compressive strength and elastic modulus, circular patterned specimens exhibited a higher fatigue performance due to less stress concentration. A similar result was achieved for elastomer lattices [78]. In this study, FFF prints with circular pores survived higher tensile fatigue cycles than the cross-hatched specimens due to lacking stress concentration. Figure 12: (**a**) Orientation of infill patterns with respect to the loading direction and (**b**) rectilinear and honeycomb infill patterns (Adapted with permission from [67]. Copyright 2018, Elsevier). Since the fatigue resistance of prints is greatly affected by the arrangement of the rasters against the loading direction, infill patterns may exhibit different influences in different loading conditions. To address this matter, the stress distribution, decay of hysteresis loops, and failure mechanism of certain infill patterns in different fatigue loads can be studied in future research.
## 4 Experimental Results
### Comparison with Related Works
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **[**20**]** & **[**21**] **(m = 2)** & **[**21**] **(m = 3)** & **[**23**]** & **Proposed Method** \\ \hline \multirow{4}{*}{Airplane} & Total BR & 0.495 & 0.446 & 0.455 & 0.416 & 0.666 \\ \cline{2-7}  & Data BR & 0.015 & 0.038 & 0.043 & 0.049 & 0.236 \\ \cline{2-7}  & Image BR & 0.480 & 0.408 & 0.416 & 0.367 & 0.429 \\ \hline \multirow{4}{*}{Baboon} & Total BR & — & 0.510 & 0.509 & — & 0.726 \\ \cline{2-7}  & Data BR & — & 0.014 & 0.020 & — & 0.179 \\ \cline{2-7}  & Image BR & — & 0.496 & 0.489 & — & 0.547 \\ \hline \multirow{4}{*}{Boat} & Total BR & 0.501 & 0.447 & 0.456 & 0.419 & 0.631 \\ \cline{2-7}  & Data BR & 0.015 & 0.038 & 0.041 & 0.048 & 0.245 \\ \cline{2-7}  & Image BR & 0.486 & 0.409 & 0.415 & 0.371 & 0.386 \\ \hline \multirow{4}{*}{Lena} & Total BR & 0.540 & 0.446 & 0.451 & 0.415 & 0.618 \\ \cline{2-7}  & Data BR & 0.015 & 0.038 & 0.043 & 0.048 & 0.241 \\ \cline{2-7}  & Image BR & 0.525 & 0.407 & 0.408 & 0.367 & 0.377 \\ \hline \multirow{4}{*}{Peppers} & Total BR & 0.518 & 0.452 & 0.460 & 0.418 & 0.671 \\ \cline{2-7}  & Data BR & 0.015 & 0.036 & 0.041 & 0.048 & 0.260 \\ \cline{2-7}  & Image BR & 0.503 & 0.416 & 0.419 & 0.370 & 0.410 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Total bitrate, data bitrate, and image bitrate of SOC-based schemes.
# Adaptive Deployable Thin Spherical Shell Reflectors
## 1 Introduction
### Small Satellites  
In recent years, low-cost small satellites and cubesats have become increasingly popular for Earth observation, inter-satellite laser communication and LIDAR (e.g., [11; 12]). The performance of these systems depends critically on the reflector size, and they can greatly benefit from using deployable light collectors much larger than the satellite itself, provided however that the imaging quality can be maintained with a deployable mirror. A passive Ka-band radar deployable antenna of 0.5 m has been demonstrated at JPL on a 6U cubesat [13]. Attempts to use deployable reflectors made of four rigid petals for optical applications have also been reported (e.g., [14; 15]). In contrast, our goal is an actively controlled flexible deployable reflector.  
Optical communication allows wider data transfer bandwidth, but requires greater precision. The transmission gain \(G\) of laser communication (e.g., [16]) is directly proportional to  
\[G\propto(\frac{\pi D_{t}}{\lambda})^{2}\times\eta_{t}\times(\frac{\pi D_{r}}{ \lambda})^{2}\times\eta_{r} \tag{2}\]  
where \((\pi D_{t}/\lambda)^{2}\) is the gain of the transmitting aperture and \((\pi D_{r}/\lambda)^{2}\) that of the receiver. \(\eta_{t}\) accounts for the imperfection of the transmitter if not perfectly diffraction limited (the imperfection in the wavefront is usually accounted for by the Strehl ratio \(Str=exp(-\sigma^{2})\), where \(\sigma\) is the RMS wavefront error expressed in radian). Similarly, \(\eta_{r}\) accounts for the imperfection of the receiver. Assuming the same aperture for the transmitter and the receiver, Equation (2) suggests that, providing the optical quality of the telescopes can be maintained, the transmission gain is a function of the fourth power of the aperture.  
Figure 1 shows a conceptual view of a deployable reflector with flexible petals, in folded (left) and deployed (right) configurations. The Figure illustrates the deployment with a simple translation mechanism moving the reflector outside the cubesat (upwards in the figure); the reflector, initially constrained inside the cubesat is freed gradually as its base support moves and the petals return elastically to their original position thanks to their own elastic strain energy. The final shape of the petals is achieved using in-plane piezoelectric strain actuators distributed over their back surface as discussed later in this paper, compensating for misalignment, manufacturing errors, creep, etc. The thermal behavior of the multilayer material necessary to manufacture such a reflector is the main concern of this paper. The deployment of the secondary mirror is not the focus of this research, but an example is shown. In this deployment scheme, the secondary mirror is attached to a sliding stage, which deploys along the base using a spring. The base of the stage doubles in function as a stray light baffle.  
It is clear that, if the image quality in the deployed configuration can be achieved at a reasonable cost, the significant increase of the light collecting area will tremendously improve the system performance.
## 1 Introduction
The aggregation mechanism will reduce the overheads, i.e., the waiting time before a successful channel access and underutilization of the channel capacity. A successful channel access transmits multiple frames which mean we not only send multiple frames in cost of a single frame overhead but also with a lesser PHY header overhead. The aggregation mechanism significantly reduces the delay and increases the throughput for patient monitoring systems where many small packets are generated. Moreover, the overall network lifetime is increased due to overhead avoidance of aggregated packets. This research \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Physiological** & **Sampling Rate** & **Sampling Resolution** & **Type of Sensing Device** & **Location** \\
**Parameter** & **(Hz) (Min-Max)** & **(Min-Max)** & **Type of Sensing Device** & **Location** \\ \hline ECG (per channel) & (100–1000) & (12–24) & Electrodes & Chest \\ EMG & (125–1000) & (12–24) & Electrodes & Muscles \\ EEG & (125–1000) & (12–24) & Electrodes & Head \\ Pulse oximeter & (100–1000) & (12–16) & Photodiode & Ear or finger \\ Blood pressure & (100–1000) & (12–24) & Pressure cuff & Arm or finger \\ Respiration & (25–100) & (8–16) & Elastic chest belt or Electrodes & Chest \\ Blood glucose & \textless{}0.01 & (8–16) & Chemical & Skin \\ Skin temperature & \textless{}1 in 60 s & (16–24) & Thermistor probe & Wrist/arm \\ Activity & (25–100) & (12–24) & Accelerometers & Chest \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physiological signals: sampling rate, resolution, type and location [30]. initially defines the traffic patterns for such biomedical sensor networks for patient monitoring systems. These traffic patterns provide the requirements on how much and how frequent data is generated. These requirements elaborate about the channel access necessities for such applications. Then based on obtained information a channel modeling is conducted to define the problem statement of the research work. The proposed frame aggregation solution uses the information achieved from the traffic patterns to clearly define the aggregation limits. In the later section, a detailed discussion is provided on how the proposed frame aggregation mechanism can be incorporated with the superframe structure of IEEE 802.15.4. The evaluation is conducted based on mathematical modeling and simulations.
###### Abstract
However, they predict the 2- and 5-year risk of progression to late AMDrather than generating prediction masks of regions of atrophy [45]. In [PERSON] et al. 's study, an RNN model is used with OCT and demographic data as input to predict progression to exudative AMD with certain timeframes but again does not attempt to generate prediction masks of atrophy [48]. In [PERSON] et al. 's study, a bi-directional LSTM prediction module is used with a 3D U-Net refinement module to predict locations of future GA growth on OCT imaging [49]. However, as with [PERSON] et al., each timepoint is sent to a separate model which does not concurrently take advantage of ensemble features from rich 3D OCT retinal layer information. Thus, in this study, we first develop an efficient predictive model for the generation of future FAF images and the prediction of future Stargardt atrophic (as well as GA) regions and then develop an efficient ensemble predictive model that concurrently utilizes advanced 3D OCT features at the retinal layer of ellipsoid zones (EZ) (where the atrophy occurs) for the comparative performance in the generation of future enface EZ maps and prediction of future Stargardt atrophic regions on OCT as on FAF. To achieve these goals, we propose a set of novel deep convolutional neural units that are enhanced with recurrent network (i.e., LSTM in this project) units and the concurrent learning of ensemble network unit networks (termed ReConNet) for the prediction of the future progression of atrophy in Stargardt and GA based on longitudinal patients' data. Our major contributions include the following: (1) The ReConNet (recurrent and concurrent neural network) architecture incorporates LSTM connections with encoder and decoder pathways for use on longitudinal data on FAF. (2) On OCT, the ReConNet further takes advantage of the enhanced OCT enface EZ feature maps in ensemble networks (termed ReConNet-Ensemble) to capture higher-order inherent EZ retinal layer features associated with atrophy for an enhanced algorithm performance. (3) The ReConNet can be used for the generation or prediction for both future FAF images and future Stargardt atrophy and geographic atrophy. (4) While the OCT suffers from lower image quality, our prediction performance on OCT images with ensemble EZ features is comparably good to that on FAF with the available data of Stargardt disease.
# Deep Residual Network with Sparse Feedback for Image Restoration
## 1 Introduction  
In recent years, many hard problems in computer vision have been well solved, especially in the fields of image classification, object detection [1], and identification [2]. By using the deep learning method, the accuracy and robustness of these issues have been greatly improved.  
Until now, many kinds of neural network structures have been proposed, such as five layers LeNet [3], eight layers AlexNet [4], 19 layers VGG [5], 22 layers GoogleNet [6], 152 layers ResNet [7], GAN [8], and so on. These networks are gradually deepened, and the training data set is also getting much larger. Although the technology of deep learning is especially effective for issues of classification, it still includes many problems that cannot be solved in image restoration. The main reason includes two aspects: First, to collect a large number of training data about image degradation is not easy; second, the degradation reason is various, and it is exceptionally difficult to enumerate all cases of image degradation. Therefore, when using a deep neural network to improve these problems, the training set is not usually enough to train a deep neural network model.  
In this paper, we improve the network structure of ResNet [7] and propose a sparse feedback residual network, which is called FbResNet. It includes 10 layers with sparse \"shortcut connections\" (in this article we call the \"shortcut connections\" forward feedback). Figure 1 shows the structure of FbResNet. It can be seen that FbResNet only includes two forward feedbacks, which are derived from the input layer. One feedback is connected to the middle layer, while the other is connected to the last layer. The two feedbacks can provide an effective constraint of the loss function and help to train reasonable network parameters. Experimental results show FbResNet has a fast convergence speed and effective denoising performance.
# Calculation of 1/f Fluctuation from Sound Signal and Comfort Evaluation
## 2 Calculation Method for Fluctuation Coefficient of the Sound Signal
### Fluctuation Coefficient Calculation  
Figure 5 shows the relationship between the frequency and amplitude of the fluctuation coefficient for 1500 pieces of music. In this figure, the vertical and horizontal axes represent the amplitude and frequency of the fluctuation coefficient, respectively. The results were plotted for each fluctuation coefficient in one song. The red line indicates a fluctuation coefficient of \(-1\). From the result, the average frequency fluctuation coefficient was \(-0.793\), and the average amplitude fluctuation coefficient was \(-1.083\). Furthermore, the range of the frequency fluctuation coefficient was \(-3.159\) to \(0.802\), and the range of the amplitude fluctuation coefficient was \(-2.279\) to \(0.165\). Moreover, the standard deviation of the frequency fluctuation coefficient was \(0.502\), and the standard deviation of the amplitude fluctuation coefficient was \(0.369\). Therefore, there was no correlation between the frequency and amplitude fluctuation coefficients. In the 1500 pieces of music, the frequency coefficient \(-0.99926\) had the closest value to \(-1\). However, there was no music for which all fluctuation coefficients, including the sound signal, frequency, and amplitude, became almost \(-1\). Additionally, the range of the frequency fluctuation was wider than that of the amplitude fluctuation. We considered that the fluctuation coefficient for frequency changes according to various factors, such as the musical instrument, the singing voice, and the melody of the music. However, the amplitude fluctuation coefficient was detected for the same type of sounds, such as the sound pressure of the volume of the voice and the musical instrument. Therefore, the range of the amplitude fluctuation was narrower than that of the frequency fluctuation.
# Co-Production of Hydrogen and Methanol Using Fuel Mix Systems: Technical and Economic Assessment
## 3 Results and Discussion
### Equations Used for Comparative Analysis  
#### 3.1.1 Process Efficiency  
The overall process efficiency for the poly-generation process is an important process performance indicator, which represents the fuel and electricity production per unit of energy consumption, as shown in Equation (1).  
\[\text{Process Efficiency}\left(\eta_{net}\right)=\frac{\text{Methanol thermal energy [MWh]}+\text{H}_{2}\text{thermal energy [MWh]}+\text{Electricity [MWe]}}{\text{Feed stock thermal energy [MWh]}+\text{Energy consumed [MWh]}} \tag{1}\]
## 7 Issues and Challenges
### DSM with EV and ESS  
The automobile sector is presently witnessing a surge in sales of EVs with the transition to battery-based power delivery from conventional gasoline and diesel as sources of transportation fuel being the prime focus. This has seen a rise in the adoption of more efficient and high power density battery systems to be implemented at the EV end-user side. Higher capacity batteries can be configured from a backup or buffer storage system standpoint. A bidirectional implementation of these battery-enabled mobile EVs can allow for dispatch strategies to be collectively aggregated and disbursed by DNOs as a virtual power plant system through DSM strategies. Similarly, DSM can allow intelligent control of EVs, charging and discharging according to adaptive schedules, which will benefit the power system. Nonetheless, issues pertaining to EV adoption prevent the large-scale integration of DSM in EV-based programs due to:  
* High investment costs and costs of ownership from the perspective of the manufacturers, grid utility operators, and consumers. The careful economic and technical planning of charging stations, the grid's capacity to accept increased loading, and the minimization of losses along the transmission and distribution systems to allow for efficient use of available energy are the primary concerns from the maintenance and setting-up perspective.
* Consumer acceptance is still low in developing countries due to a range of anxieties about reliability and initial expenditure during purchase as compared to fossil fuel-based automotives [46].
* Battery degradation and better health are also major concerns; the periodic maintenance of fossil fuel vehicles is a fuss-free ownership experience in the long run, as battery degradation does not hinder the performance of the vehicle to a great extent. In contrast, in the case of EVs, the batteries eventually require replacement at their end-of-life stage unless they find use as second-life batteries.  
These issues and challenges must be addressed to make EMS architecture more robust and reliable. After resolving these challenges, the optimization techniques can be integrated seamlessly into the EMS architecture.