esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs +
###### Abstract  
Convolution is the most computationally intensive task of the Convolutional Neural Network (CNN). It requires a lot of memory storage and computational power. There are different approaches to compute the solution of convolution and reduce its computational complexity. In this paper, a matrix multiplication-based convolution (ConvMM) approach is fully parallelized using concurrent resources of GPU (Graphics Processing Unit) and optimized, considerably improving the performance of the image classifiers and making them applicable to real-time embedded applications. The flow of this CUDA (Compute Unified Device Architecture)-based scheme is optimized using unified memory and hardware-dependent acceleration of matrix multiplication. Proposed flow is evaluated on two different embedded platforms: first on an Nvidia Jetson TX1 embedded board and then on a Tegra K1 GPU of an Nvidia Shield K1 Tablet. The performance of this optimized and accelerated convolutional layer is compared with its sequential and heterogeneous versions. Results show that the proposed scheme significantly improves the overall results including energy efficiency, storage requirement and inference performance. In particular, the proposed scheme on embedded GPUs is hundreds of times faster than the sequential version and delivers tens of times higher performance than the heterogeneous approach.  
Keywords:concurrent computing; general purpose GPU; unified memory; convolutional neural networks; heterogeneous; matrix multiplication; CUDA basic linear algebra subroutines (cuBLAS); embedded platform +
Footnote †: _Appl. Sci._**2017**, 7, 826; doi:10.3390/app7080826
## 3 Datasets and Methods Used in SATIDS
### Energy Harvesting Real-Time Routing Protocol (Ehrt)  
Ehrt depends on the principle of clustering using LEACH routing protocol which is optimized by a modified AFSA that modifies the fitness function of AFSA [9; 25; 26; 27]. Ehrt protocol consists of the following two phases:  
* Phase I: Cluster Formation and CH Election In this step, clusters are established and cluster heads (CHs) are selected depending on the clustering algorithm's guiding principle. Equal segmentation of the area space is utilized to form clusters. Each cluster's CH is selected based  
Figure 3: Flowchart of EHRT.  
Figure 4: Flowchart of CHs selection using AFSA “Reprinted with permission from Ref. [26]”.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 5 Physical Layer Proposal
### Frame Structure
#### 5.3.2 Daytime  
The High Robustness Mode (HRM) is defined during the daytime because it shows the worst performance in terms of SNR and availability (see previous studies in [7,12,18]). Spread Spectrum modulations are the best solution in cases of low availability and, in order to increase the throughput, instead of using plain DS-SS, Signaling Spread Spectrum modulation is proposed [8], as previously detailed in Section 4.4.1.  
Figure 12: Definition of the four different frame structures. (**a**) Narrowband Sounding Frame (NS); (**b**) Wideband Sounding Frame (WS); (**c**) High Robustness Mode Frame (HRM); (**d**) High Throughput Mode Frame (HTM).  
The chosen configuration fits Figure 12c, and consists in a synchronization triplet of PN m-sequences length 255 with an upfactor of 6 samples/chip, using 16.6 kHz of bandwidth. Each frame contains 10 symbols, m-sequences length 2047 with an upfactor of 6 samples/chip, working at 16.6 kHz. Each symbol, working with Signaling, contains 11 bits of information. So, each frame lasts 1.26 s and contains 110 bits, which enables us to obtain a bitrate of real 71.4 bps taking into account the synchronization triplet (90 bps without synchronization words). This means a total of 267 kbits in an hour, taking into account the synchronization time.  
No channel coding is applied in this case; the chosen modulation is robust, so if the channel is available, the probability of a good reception is high. In addition, we assume that retransmission of data will be done during the daytime.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 3 Trajectory Data Sparsity and Anomaly Detection
### Sparse and Abnormal Trajectory Points Detection
#### 3.2.2 Detection Method Based on Speed Elements  
During the actual voyage of the vessel, according to different marine environments, there is often a limitation on the speed over ground (SOG) of the vessel, and it is necessary to utilize the AIS data to count the actual SOC distribution of the vessel and identify abnormal data through statistical analysis. This research focuses on restoring vessel trajectories under voyage status and does not consider vessel trajectories under anchored status. Therefore, we screened out the data with a sailing speed of less than 1 knot. The distribution of SOC obtained from the statistical dataset is shown in Figure 3, and based on this distribution, the SOG threshold is set to 24 knots, i.e., when the SOC is greater than 24 knots, it is inferred that there are data anomalies in this section of the trajectory, and it is marked as a trajectory restoration point.
# Relay Selection Schemes for FSO Communications over Turbulent Channels
## 1 Introduction
Moreover, different turbulence schemes are considered as the weak-moderate turbulence, which is modelled by log-normal channels, and strong turbulence, which is modelled by the Gamma-Gamma channels. In addition, the pointing error effect on the link is considered for the different beam waist effect. The obtained results show that there is no optimum technique under different conditions, since the optimum technique depends on several factors, such as the number of relays, different distances, turbulence, and pointing error effects. The rest of this paper is organized, as follows. Section 2 explains the system and channel models. Section 3 presents and discusses the average bit error rate analysis. Section 4 is devoted to the obtained results. Finally, Section 5 concludes the paper.
# Compact Trap-Assisted-Tunneling Model for Line Tunneling Field-Effect-Transistor Devices
###### Abstract  
Trap-assisted-tunneling (TAT) is a well-documented source of severe subthreshold degradation in tunneling field-effect-transistors (TFET). However, the literature lacks in numerical or compact TAT models applied to TFET devices. This work presents a compact formulation of the Schenk TAT model that is used to fit experimental drain-source current (\(I_{\text{ds}}\)) versus gate-source voltage (\(V_{\text{gs}}\)) data of an L-shaped and line tunneling type TFET. The Schenk model incorporates material-dependent fundamental physical constants that play an important role in influencing the TAT generation (\(G_{\text{TAT}}\)) including the lattice relaxation energy, Huang-Rhys factor, and the electro-optical frequency. This makes fitting any experimental data using the Schenk model physically relevant. The compact formulation of the Schenk TAT model involved solving the potential profile in the TFET and using that potential profile to calculate \(G_{\text{TAT}}\) using the standard Schenk model. The \(G_{\text{TAT}}\) was then approximated by the Gaussian distribution function for compact implementation. The model was compared against technology computer-aided design (TCAD) results and was found in reasonable agreement. The model was also used to fit an experimental device's \(I_{\text{ds}}\)-\(V_{\text{gs}}\) characteristics. The results, while not exactly fitting the experimental data, follow the general experimental \(I_{\text{ds}}\)-\(V_{\text{gs}}\) trend reasonably well; the subthreshold slope was loosely similar to the experimental device. Additionally, the ON-current, especially to make a high drain-source bias model accurate, can be further improved by including effects such as electrostatic degradation and series resistance.  
tunneling field-effect-transistor; trap-assisted-tunneling; Schenk model; line tunneling +
Footnote †: journal: Applied Physics Letters
## 2 Circuit Implementation
### Common-Mode Charge Pump Stage  
The architecture of the proposed CMCP is illustrated in Figure 4. The CMCP comprises two switches, a CMCP capacitor (C\({}_{\text{CMCP}}\)), buffer, and window comparator. The CMCP control logic creates an upward- or downward-shifting voltage to the common-mode input to maintain an acceptable input common-mode range of the CFIA.  
In this study, the CMCP operation was prevented within the input common-mode range by applying a window comparator, thereby reducing additional power consumption. The comparison voltage V\({}_{\text{COMP\_H}}\) of the window comparator was set to 1.1 V, the maximum operating point voltage of CFIA, and V\({}_{\text{COMP\_L}}\) was set to 0.7, the minimum operating point voltage.  
Figure 5 shows the timing diagram of the CMCP operation. When V\({}_{\text{CMI}}\) increases more than V\({}_{\text{COMP\_H}}\), and if the amplitude of V\({}_{\text{CMI}}\) is higher than that of V\({}_{\text{COMP\_H}}\), the V\({}_{\text{PP}}\) is raised to a high (1.8 V) state. When p2 is high, the current state is maintained. Subsequently, when the p1 phase is converted to a high state, the common-mode voltage is maintained at V\({}_{\text{COMP\_H}}\) by downward-level shifting. When V\({}_{\text{CMI}}\) falls lower than V\({}_{\text{COMP\_L}}\), and if the amplitude of V\({}_{\text{CMI}}\) is lower than that of V\({}_{\text{CMIP\_L}}\), the V\({}_{\text{PP}}\) is reduced to a low (0 V) state. When p2 is in the high state, the current state is maintained. Subsequently, when the p1 phase is converted to a high state, the common-mode voltage is maintained at V\({}_{\text{COMP\_L}}\) by upward-level shifting.  
Figure 4: Top architecture of the proposed CMCP.  
The V\({}_{\rm C}\) is level shift range is expressed as [4]:  
\[\rm V_{C}\approx V_{DD}\frac{2C_{CMCP}}{2C_{CMCP}+C_{C}} \tag{1}\]  
where C\({}_{CMCP}\) is the CMCP feedback capacitor shown in Figure 4, and C\({}_{\rm C}\) is between the V\({}_{\rm CMI}\) and ESD pad. The proposed circuit operates at 1.8 V\({}_{DD}\), and the C\({}_{CMCP}\) value is set to 2 pF. The typical environmental parameter value [23] of C\({}_{\rm C}\) is 220 pF, and the calculated V\({}_{\rm C}\) was set to 32 mV. C\({}_{CMCP}\) was programmable to 2 pF and 10 pF. The C\({}_{CMCP}\) value was determined through a register setting and can be controlled by the SPI.  
Figure 6 shows a schematic of the buffer. The buffer was applied driving the CMI voltage and V\({}_{\rm PP}\) voltage. The buffer structure adopts a PMOS input stage and implements folded-cascode topology. Using folded-cascode topology can achieve a wide output voltage swing range. The total current consumption was 1.029 \(\rm\upmu\)A.  
The comparator structure is shown in Figure 7. The comparator comprised a two-stage preamplifier and latched comparator. The preamplifier implemented PMOS input single-stage cascade amplifier can reduce the kickback noise parameter. The latch implemented a CMOS latch to compare with the V\({}_{\rm CMI}\).  
Figure 5: Timing diagram of the proposed CMCP.  
Figure 6: Schematic of the buffer.
## 2 Theoretical Background
### Optimizing Power Performance  
A piezoelectric harvester receives input in the form of amplitude and frequency from a vibrational source. [PERSON] et al. described a 2-step method used to optimize the power performance of a PVEH plate as follows:  
* Maximizing the extracted electric power for a fixed deflection by matching the resistive load. It is a sub-optimal condition for a matched resistive load, assuming that \(y_{0}\) is not a function of \(R\).
* Maximizing the deflection for the matched resistor as derived in Equation (16).  
At this stage, maximum deflection is achieved under matched natural frequency with excitation frequency as well as considering the optimal resistance. [50]. It is also mentioned that optimizing the electric power assuming a fixed deflection at the resonance frequency of the system does not severely impact the accuracy of the calculation compared with the exact numerical solution of the model.  
To obtain the optimum harvested electric power through largest deflection, it is important to ensure maximum deflection under the matched vibration frequency, \(\omega\), with natural frequency as well as using the optimal resistance, \(R_{opt}=\ 1/C_{p}\omega\)[33]. The optimum resistance value is applied to Equations (12) and (13) to obtain the electrical damping and stiffness, thus yielding the maximum deflection, \(y_{max}\), from Equation (15) as shown below:  
\[y_{max}=\ \frac{P}{\left(K+\frac{\theta^{2}}{2\mathcal{C}_{p}}-\omega^{2}M \right)+j\omega\left(C+\frac{\theta^{2}}{2\mathcal{C}_{p}\omega}\right)} \tag{19}\]  
Applying optimal resistance, \(R_{opt}\) to the average power equation in Equation (16), optimum power, \(P_{opt}\) is generated from the maximum voltage, \(V_{max}\) harvested under the maximum tip deflection, \(y_{max}\). The optimum power, \(P_{opt}\) can be expressed as:  
\[P_{opt}\ =\ \frac{\omega\Theta^{2}y_{max}{}^{2}}{4\mathcal{C}_{p}} \tag{20}\]  
Or:  
\[P_{opt}\ =\ \frac{V_{max}{}^{2}}{R} \tag{21}\]
# Joint Use of Spaceborne Microwave Sensor Data and CYGNSS Data to Observe Tropical Cyclones
## 2 Observation Data
### SMAP Radiometer Data  
The NASA SMAP mission, launched on 29 January 2015, is a combination of passive (radiometer operating at 1.41 GHz) and active (scatterometer operating at 1.26 GHz) L-band sensors designed to provide global soil moisture and freeze/thaw classification. It flies in a polar 8-day repeat orbit at an average altitude of 685 km and maps out a contiguous swath of about 1000-km in width. In addition,  
Figure 1: Wind speeds retrieved from version 2.1 of Cyclone Global Navigation Satellite System (CYGNSS) Level 2 data products on 10 September 2018. Subplots (**a**) and (**b**) were acquired using the fully developed seas (FDS) and young seas with limited fetch (YSLF) geophysical model functions (GMFs), respectively.  
the spatial resolution of the SMAP radiometer is about 40 km. Although designed for hydrological applications over land surfaces, it also has excellent capabilities to measure ocean surface salinity and ocean winds in storms. The SMAP radiometer can measure wind speeds up to 65 m/s without being affected by rain [31]. Furthermore, for wind speeds in the range of 20-40 m/s, the difference in the root-mean-square of the wind speed retrieved from the SMAP radiometer data compared to the stepped frequency microwave radiometer (SFMR) observations is about 4.6 m/s [32]. More details about SMAP and its radiometer data can be found in [31].  
In this study, version 4.3 of SMAP radiometer Level 2 data products (downloaded from [[https://podaac-tools.jpl.nasa.gov/drive/files/allData/smap/L2/JPL/V4.3](https://podaac-tools.jpl.nasa.gov/drive/files/allData/smap/L2/JPL/V4.3)]([https://podaac-tools.jpl.nasa.gov/drive/files/allData/smap/L2/JPL/V4.3](https://podaac-tools.jpl.nasa.gov/drive/files/allData/smap/L2/JPL/V4.3))), which includes the wind speed and direction for high winds, were adopted to observe the TCs of interest. Wind speeds retrieved from version 4.3 of SMAP radiometer Level 2 data products on 12 September 2018 are shown in Figure 2. A hurricane can be observed in the top right portion of this figure.
# A Novel Maneuver-Based Driving Envelope Generation Approach for Driving Safety Assessment
## 3 The Driving Envelope for Candidate Driving Maneuvers
### The Driving Envelope with Road Constraint and Steering Limitation  
Given the initial state and the maximum curvature rate \(k_{rate,max}\) of the vehicle, the reachable-set can be obtained shown as the orange area within the dashed lines in Figure 12. This reachable set considers the initial vehicle states, driver's input, and steering limitation. However, avoiding collision with the lane boundary has not been considered. The evasive motion model can guarantee safe motion avoiding collision with obstacles and lane boundaries. However, the initial steering angle and steering limitation are not considered. We combine the evasive motion model and the boundary of reachable-set to overcome the disadvantages. Considering the road boundary constraint, we set aside a safe distance \(\Delta y\) to the road boundary to get the target lateral position. By utilizing the evasive motion model in Section 2.2, a path arriving at the target lateral position can be obtained. We connect the boundary of the reachable-set (Section 3.1) and the path following the evasive motion model to obtain the boundary paths of driving envelope, denoted as \(\tau_{outside,left}\) and \(\tau_{outside,right}\) in Figure 12. On the connection point, the curvatures of the two paths are equal, so that the continuity of curvature at the connection point could be guaranteed. The \(\tau_{outside,left}\) and \(\tau_{outside,right}\) can be treated as the boundary path of driving envelope considering the constraints of lane boundary and steering limitation. The green area \(R_{s}\) between \(\tau_{outside,left}\) and \(\tau_{outside,right}\) is the driving envelope considering road boundary. No safe trajectory exists when \(\tau_{outside,left}\) and \(\tau_{outside,right}\) partly coincides with each other.
# Experimental Evaluation of a MIMO Radar Performance for ADAS Application
## 4 Discussion  
The main idea of the work was to evaluate the characteristics of the TI AWR1843 radar in terms of the error committed in distance, the number of false echoes, and the number of valid echoes per unit of time. Experiments were performed with pedestrians and motorcycles, with the idea that they are the targets that could eventually go unnoticed by the radar.  
Figure 12: Signal time evolution and parameters in the radar configuration.  
In the analysis, a double adaptive threshold has been used, one on the speed axis and another on the distance axis, for each speed selected by the first, and both with the CA-CFAR method. The results show that it is a good strategy and that it provides a level of intuitive control over the number of false echoes.  
Initially, the experiments were carried out by setting the PFA for determining the threshold at speeds with a loose value of 0.2. It was later found that this value actually eliminates too many potentially valid echoes, and a higher value, \(\mathit{PFA}_{\mathit{vel}}=0.3\), has been found to be more suitable for obtaining a higher echo rate. On the other hand, in our experiments, there is no appreciable difference by using \(Ncf=256\) or 128, so \(Ncf=128\) is a more economic value.  
The maximum error of the radar in estimating the position of a target turns out to be a difficult parameter to quantify since it depends critically on what criterion is used to assign an echo to a specific target.  
The average error has turned out to be very acceptable, taking into account that interpolation techniques can still be included and, in this work, with the exception of zero-padding in the angle-FFT, have not been used.  
However, it must be said that making meaningful statistics from realistic experiments with FMCW MIMO radar seems like a not possible task, at least at this initial level. There are two factors that make this task very difficult, or simply impossible. The first one is, as it was pointed out, that there can be an important number of false or poorly positioned echoes, and the final statistic will depend strongly on the criterion adopted in the selection of valid echoes and, also, on the PFA values selected in the CFAR thresholds. The second reason is that in large targets, the echoes can come from different parts of the targets, making it necessary to have another sensor, e.g., a camera, to inform the size of the vehicle and validate the accuracy of the selected echoes.  
Finally, we did not set out to estimate the maximum distance at which our radar can capture targets, although, in other measurements not presented in this work, pedestrians have been detected at about 30-35 m and, eventually, some cars were perfectly detected at 80 m.
## 2 Related Works
### VANET Testbeds  
There have been practical implementations of VANET testbeds. For example, a testbed was implemented to extract H.263 video streams through an ADSL (Asymmetric Digital Subscriber Line) gateway to an SQL server in the Internet [18]. A prototype testbed [19] using a handheld device with an Android operating system was presented for data-intensive application. While these approaches are effective, their hardware implementations are expensive and do not allow quick and easy changes to adapt to various industry scenarios and implementation of special safety applications. Video cameras make up a key component in VANET due to their ability to aid navigation safety and give road users and relevant authorities a precise and clear image of the traffic conditions when necessary. Intel Corporation has decided to lead the development of a video camera system on board of a car [20]. Others include the CogniVue system [21]. Since a special high-definition camera can pump into the network to degrade network performance and video quality, this has been a key concern in VANET design. Solving this issue will unlock the power of driving camera video applications.  
In light of the outlined efforts, one of the major focuses of VANET research is the creation of an effective simulation platform that can integrate a network simulator with a realistic vehicular traffic simulation model. Unlike other network environments, VANET mobility has a peculiar and unique nature due to the randomness of human behavior. One classification groups vehicular traffic under four models [22]: Submicroscopic, Microscopic, Mesoscopic and Macroscopic. It is important to choose a realistic mobility model to correspond to each one. An open-loop integration approach and a closed-loop integration approach have been discussed for the integration of the network model with a VANET mobility model. Although there has been other work on creating a similar platform (e.g., [12; 13; 14]), to the best of our knowledge, there is hardly any work on the characterization (e.g., the packet size distribution) of video data streaming from the vehicle camera and the behavior under different data traffic conditions. The closest work is on UAV video [23], but this was done from an image process perspective. A broad view of the state-of-the-art mobility models adapted for VANETs was discussed [24], which also provided a detailed survey and comparison of mobility models.  
However, it only provides a model for data transmission and not for video transmission. Such a model needs to consider other factors (like the physical layer communication mechanism and control algorithms in the application layer for real-time video streaming) for effective VANET simulation. Note that a model or a tool may be good for one aspect/feature, but their integration is necessary to model an effective safety solution.