esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection
## 5 Conclusions  
In this paper, we propose an accurate, fast and lightweight face detection method based on a fast residual network. To detect multi-scale faces, we fuse feature maps of multiple convolutional layers within the network to generate multi-scale features for prediction. To further promote the detection accuracy, we add multi-context that includes local and global context to the fused multi-scale feature maps. Both of the multi-scale features and multi-context bring no computational burden to the backbone network. Experimental results show that our method can run at about 110 fps on VGA-resolution images. This speed is evaluated on a high-performance Titan X GPU, but the 110 fps implies that our method will still get a fast speed on a cheap and ordinary GPU. And it achieves high precision comparable to the state-of-the-art counterparts on WIDER FACE and FDDB dataset. In some cases, our lightweight method can even outperform the 'heavy' VGG-16 and ResNet-101 based methods. The lightweight characteristic and high precision make our method practical for deployment in real life applications. In the future, we will try to use some quantization and pruning approaches to compress our network, to further reducing the model size and accelerating the detection speed. But our method still has a gap comparing to best face detection methods in some complicated conditions, and the model size and computation still constrain our method deployed in embedded systems. In the future, we will try to improve the precision by designing a more powerful backbone network. And we will exploit quantization and pruning approaches to compress our network for further reducing model size and computation.  
Conceptualization, L.Z.; Data curation, L.Z.; Funding acquisition, X.Z.; Methodology, L.Z.; Project administration, X.Z.; Writing-original draft, L.Z.; Writing-review & editing, X.Z. This work was supported by Shanghai Science and Technology Commission Research Project (Grant No. 16010500400), the Shanghai Innovation Action Plan Project (Grant No. 16511101200) and the National Key Research and Development Plan of China (Grant No. 2016 YFB1000600 and 2016 YFB1000601). The authors declare no conflict of interest.  
Figure 7: Qualitative results under various challenging conditions, i.e., illumination, pose changes, occlusion, race and etc. Ground truth bounding boxes are in red and predicted bounding boxes are in green. (Zoom in to see better).
## 3 Results
### Difference Due to Assistance of Power-Support Scissors  
Table 2 shows the VAS score of pain and fatigue, and the muscle activity level of each muscle, with and without power support. The ADP showed significantly lower muscle activity with and without power support (\(p\) = 0.004). The ECR did not show a significant difference in muscle activity under each condition. VAS score was found to be significantly lower for fatigue and pain of the thumb and radial sides of the forearm under the assisted power condition.  
An APDF showed significant differences in ADP muscles overall for P0.1, P0.5, and P0.9, indicating that ADP muscle activity was reduced by the support mechanism of the power-assisted scissors (Figure 3A). The ECR without power assistance showed the highest peak load (P0.9) (mean: 39 %MVE) in overall muscle activity (Figure 3B). Power-assisted ADP was found to be 1.9% lower at the P0.1 level, 3.3% lower at the P0.5 level, and 6.5% lower at the P0.9 level. The ECR showed no change at any activity level. The relationship between percentiles with and without assistance was verified by ANOVA. As a result, no interaction effect was observed between the assisted conditions, and the main effect of the percentile was observed (ADP: F\({}_{[1.15,\,40.16]}\) = 104.43, \(p\) < 0.01; ECR: F\({}_{[1.05,\,36.79]}\) = 102.88, \(p\) < 0.01).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measure** & **With Assist (\(n\) = 20)** & **Without Assist (\(n\) = 20)** & **Statistics** \\ \hline
**VAS** & **Mean** & **SD** & **Mean** & **SD** & \\ \hline Thumb Fatigue & 2.25 & 1.29 & 3.50 & 1.82 & \({}^{\text{a}}\) P = 0.008, \({}^{\text{b}}\) r = 0.60 \\ Thumb Pain & 0.80 & 1.06 & 1.45 & 1.54 & \({}^{\text{a}}\) P = 0.031, \({}^{\text{b}}\) r = 0.51 \\ Rad Fatigue & 4.35 & 1.79 & 5.75 & 2.07 & \({}^{\text{a}}\) P = 0.021, \({}^{\text{b}}\) r = 0.58 \\ Rad Pain & 1.95 & 1.79 & 3.20 & 1.96 & \({}^{\text{a}}\) P = 0.021, \({}^{\text{b}}\) r = 0.61 \\ \hline
**Mean \%MVE** & **Mean** & **SD** & **Mean** & **SD** & \\ \hline ADP & 0.14 & 0.05 & 0.15 & 0.09 & \({}^{\text{a}}\) P = 0.021, \({}^{\text{b}}\) r = 0.58 \\ ECR & 0.23 & 0.07 & 0.26 & 0.10 & \({}^{\text{a}}\) P = 0.124, \({}^{\text{b}}\) r = 0.36 \\ \hline \hline \end{tabular} For statistical analysis, the Wilcoxon signed-rank test was used for VAS and %MVE (ADP) after the normality test, and the paired 4-test was used for %MVE (ECR). \({}^{\text{a}}\) Test between two groups due to difference in assistance of power-assist scissors. \({}^{\text{b}}\) Effect size in each test. VAS, visual analog scale; Rad, radial side forearm; MVE, maximal voluntary electrical activity; ADP, adductor polILS muscle; ECR, extensor carpi radialis; SD, standard deviation.  
\end{table}
Table 2: Difference in variables due to the assistance of power-support scissors.
# Neuro-Inspired Computing with Spin-VCSELs
## 2 The Theoretical Model
### The RC Setup
The virtual nodes \(V_{i}\), shown in Figure 1, can either be formed by sampling the output power \(P=\left|E\right|^{2}\) in a single mode (\(E^{+}\) or \(E^{-}\)) or by concatenating the virtual nodes from both modes \(\left(P^{+}=\left|E^{+}\right|^{2}\) and \(P^{-}=\left|E^{-}\right|^{2}\right)\), resulting in \(2N\) nodes in contrast with the \(N\) nodes introduced by the mask. One could instead also construct an output layer consisting of the output polarization \(\left(POL_{out}=\frac{p^{+}-P^{-}}{p^{+}+P^{-}}\right)\) as virtual node values, but we found that the performance was negatively affected by this added hyperbolic nonlinearity. We briefly discuss this in our results in Section 3.1. The training phase consists of feeding the setup with \(m\) masked datapoints and sampling the virtual nodes, so that we obtain the state matrix \(Q\)\((m\times 2N)\). We already know the expected output \(y_{expected}\) for the \(m\) datapoints and hence, the \(2N\) weights of the output layer can be calculated with the Moore-Penrose inverse \(Q^{+}\), such that:  
\[w=Q^{+}y_{expected}. \tag{5}\]  
The obtained weights are then kept constant, such that unseen data can be fed to the system in order to test the performance. In this paper, the performance was benchmarked by the Santa Fe timeseries prediction task [29]. The data were from the Santa Fe timeseries competition [29] and consist of a univariate chaotic timeseries obtained from a \(NH_{3}\) laser. The goal of the task was to predict the chaotic timeseries one step ahead. This task is frequently used to benchmark RC setups [17; 18; 22; 27]. The first 3000 datapoints were used for the training and 1000 for testing. The performance was measured and indicated by the normalized mean square error \((NMSE)\):  
\[NMSE(y,y_{expected})=\frac{\left\langle||y(n)-y_{expected}(n)||^{2}\right\rangle }{\left\langle||y_{expected}(n)-\left\langle y_{expected}(n)\right\rangle||^{2} \right\rangle}, \tag{6}\]  
where \(y(n)\) is the value predicted by the RC, \(y_{expected}\) is the expected value for the given input and the symbols \(||\dots||\) and \(\langle\dots\rangle\) stand for the norm and time average, respectively. The lower the \(NMSE\) is, the better the system performs. For the Santa Fe timeseries predic  
Figure 1: (**left**) Schematic overview of our setup, consisting of a spin-VCSEL connected to a feedback line with delay \(\tau_{D}\). The spin-up and spin-down populations are pumped separately by \(J^{+}\) and \(J^{-}\), respectively. (**right**) A simplified plot showing the input data \(1(t)\) in blue, the masked input data \(A(t)\) in red and the timescales of \(\tau_{M}\) and \(\theta\). tion task, the state-of-the-art performances for numeric RC simulations ranges between 0.01 and 0.1 [4; 19; 21; 22].
# Performance Evaluation of an Imaging Radiation Portal Monitor System
## 2 Materials and Methods
### Nget Method  
While the main focus of previous studies of systems for SNM detection has been on neutrons, which are a characteristic signature of such materials, there are several advantages connected with the additional detection of gamma rays associated with spontaneous or induced fission. Special and beneficial features of prompt fission gamma rays are their higher multiplicity and short flight time from source to detector. In the fission process, most of the gamma rays are emitted in prompt cascades depopulating short-lived (typically picoseconds-nanoseconds) excited states in the fission products. Therefore, gamma-fast neutron correlations deserve further investigation as an additional SNM detection method, complementing the standard single-gamma and neutron counting, with a potential to significantly reduce RPM false alarm rates (FAR) [11].  
Gamma-fast neutron coincidences provide not only an additional, more selective, detection mode but also excellent 3D localization capabilities for SNM using the novel neutron-gamma emission tomography (NGET) [2] technique. Location of SNM using NGET can be obtained by analyzing fast neutron-gamma coincidences event-by-event or cumulative distributions of, primarily, particle arrival time differences using machine learning or statistical methods. Here, results using a Bayesian method applied event-by-event are presented. For each event, the algorithm calculates the probability distribution for the possible points of origin in space from the measured energy deposited by the neutron and the gamma-neutron time difference. Differently from neutron scatter cameras, which can also provide images of SNM, only the detection of one neutron interaction is required while the probability for detection of coincident gamma rays is enhanced due to the much higher prompt fission gamma-ray multiplicity [12]. Since fast neutrons mainly interact via elastic scattering on protons in the organic scintillator, the measured recoil energy samples the incoming neutron kinetic energy and also sets a lower limit of the kinetic energy carried by the incident neutron. Since neutrons emitted from spontaneous fission of SNM have well known energy distributions, often approximated by a Watt spectrum [13], the measured energy deposited by a neutron can be transformed into a probability distribution for the kinetic energy of the incident neutron. Together with the measured time-of-flight (TOF) between the photon and the neutron from the same fission event, the derived neutron energy probability distribution can be transformed into a probability density function for the location of the emission point. For the image reconstruction, a deconvolution algorithm based on [PERSON]'s theorem [14] was applied to the event-by-event data. The performance of the NGET method applied to the KTH Phase 1 RPM prototype was tested for a \({}^{252}\)Cf source in different shielding conditions.
## 2 Related Work
Both works do not detail how the routes are delivered to the vehicles, assuming that there is a centralized system, and assuming a complete driver's adherence to proposed routes, effectively using the recommended paths. To analyze the performance of a heuristic traffic assignment solution, it is necessary to compare its results with the upper/lower bound and theoretical optimal solutions. Figure 2 illustrates different methods to calculate mean user travel time (MTTS). It is lower-bounded by the ideal free-flow travel time (FF) where any vehicle uses the network in empty conditions. On the contrary, it is upper bounded by the ideal all-or-nothing (AON) situation where any user makes their driving decisions ignoring traffic status. Equilibrium and optimal assignments will be somewhere between them. Routing unfairness situations appear when the assignment objective is focused on the system optimum, and some vehicles suffer penalties in their routes [63]. The efficiency provided by a static traffic assignment shall be referenced to these limits [FF,AON]. It should also be compared to the UE and SO situations. The closer the traffic assignment is to the Free-Flow lower limit, the better. The following methods are used to compare the quality of the results offered by the TWM routing strategy:  
* Free-flow: vehicles use one optimal route and are calculated with basic link costs (1). Though an unreal scenario, it provides a lower bound for routing travel times. * All-or-nothing: vehicles just use one route and consider link occupancy-costs (3). It is also an unreal scenario, but it delivers a reasonable higher bound in the case of optimal routing. * System Optimum estimation strategies such as CAM, MSA or LP. Our work relates to these previous studies in the flow-oriented and GA approaches used to solve the optimal process, though it has significant differences: (a) instead of distributing KSP routes, it uses multi-maps TWM that allows a feasible implementation for traffic routing having the benefits described in [8]; (b) path flows are defined as an abstraction of the O/D+KSP-based routing; (c) path flows determine a bonus-malus strategy encouraging the use of specific routes; (d) individual self-routing is possible using the TWM received without a central routing server; (e) the proposed GA algorithms are more straightforward as they do not use a per-vehicle optimization but a per-path flow basis: only traffic demand matrices are required instead of knowing individual trips in advance.
## 2 Materials and Methods
### Retinal Vessel Segmentation  
To get the blood vasculature, the OD and fovea (or some other background structures) should be removed. The difference between the vessel-removed image \(I_{vr}\) (Figure 3a) and the illumination-corrected image \(I^{\prime}\) gets the crude vessel structure image \(I_{cv}\). In the image \(I_{cv}\), background and OD are no longer obvious. See Figure 3b.  
Based on the fact that the vessels are line-like and smooth, a line detector [16,18] is applied to the image \(I_{cv}\) for further vessel enhancement and detection. The line detector consists of 12 oriented line segments passing through the center pixel \(p(x\),\(y)\) of a square window. Each line is defined as the following formula,  
\[\big{(}i-x\big{)}\sin\theta-\big{(}j-y\big{)}\cos\theta=0 \tag{2}\]  
where \((i\),\(j)\) denotes a neighbor of \(p\), and \(\theta=0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\),..., \(165^{\circ}\), is the slope angle of each line.  
Figure 3: Images in retinal vessel segmentation: (**a**) the vessel-removed image \(I_{vr}\); (**b**) the crude vessel image \(I_{cv}\); (**c**) the image after line detector processing; (**d**) the Otsu binary image \(I_{o}\) of (**d**).  
The average gray level is computed along each line in a fixed length \(l\). The line strength of the pixel _p_(_x_,_y_), denoted as _S_(_x_,_y_), is given by the difference between _L_(_x_,_y_) and _N_(_x_,_y_). Here, _L_(_x_,_y_) is the largest average gray level among those of 12 lines, and _N_(_x_,_y_) is the mean intensity of the _l_\(\times\)_l_ square neighborhood. The best performance would be with \(l\) =15 pixels [16]. Since vessels are darker than the background, the line strength can be use to identify whether a pixel is a vessel pixel or not. After the line detector, the Otsu algorithm [19] is used to get the binary vasculature image Io. Note that the vessel structures in images **(b)**-**(d)** of Figure 3 are highlighted and given to illuminate the effect of each step of vessel segmentation.
# Sample-Based Gradient Edge and Angular Prediction for VVC Lossless Intra-Coding
## 3 Proposed Method
### Sample-Based Intra-Gradient Edge Detection and Angular Prediction
#### 3.1.1 Edge Regions: Gradient Edge Detection  
Edges are the intersection of different image attributes. The value differences between image samples located at both ends of edges are significant. Since images generally contain a large number of different attributes, the occurrence frequency of edges is very high. In conventional intra-prediction, all samples in a prediction block share the same predictor. However, in a prediction block, samples located at edges in different directions are suitable for different predictors. To improve this situation, a gradient edge detection (GED) algorithm is proposed. GED detects edges near the current sample by analyzing gradient information between adjacent reference samples. Then, GED selects the optimal predictor for the current sample based on the result of edge detection. The prediction  
is used to deal with smooth regions. The process of average prediction and the positional relationship between the reference samples and the current sample are shown in Figure 4.  
The adjacent samples \(a\) and \(c\) are employed as reference samples for prediction. The prediction sample is obtained as an average value of two reference samples:  
\[P_{(x,y)}=(a+c+1)>>1 \tag{2}\]  
where \(a\) is the left reference sample of the current sample, \(c\) is the top reference sample of the current sample. Since the regions to be handled are smooth, the values between adjacent samples are expected to be relatively close. In the sample-based prediction, the samples at positions \(a\) and \(c\) of the current sample are the closest reference samples that can be obtained. Therefore, the average value of the reference samples \(a\) and \(c\) is expected to be the optimal value for the prediction sample \(P_{(x,y)}\).
## 3 Results and Analysis
### Transient-State Results  
The results were obtained according to the quantization effects, defined as: \(W_{m}=28\) bits, ADC 12 bits (\(\dot{u}_{x}\), \(v_{c}\), \(\dot{u}_{L}\)), and \(d\) 10 bits, with \(K_{S1}=2\), \(K_{S2}=2\), \(K_{S3}=30\), and \(\text{N}=1\), leading to a delay of one period.  
Figure 2(a) shows the speed of the motor for the simulation test (black), the speed of the motor for the experimental test (blue), and the reference speed (\(W_{mref}\)). Initially, it starts from a reference speed (\(W_{mref}=100\) rad/s). Then, at \(t=1\) s the reference signal changes (\(W_{mref}=400\) rad/s) and Figure 2(c) shows the numerical error (black) and the experimental error (blue) of the DC motor speed signals (\(W_{m}\)). Considering the overshoot (OS), the settling time (ST), and the steady-state error (SSError) as performance indices of the control technique, Figure 2(a) shows the OS, with a value of 0.57% for the simulation test and a 0% for the experimental test, while the SSEerror are -0.1645% and 0.4245% for the simulation and experimental results, respectively. Additionally, the experimental results in Figure 2(a) confirm the predictions obtained by the numerical simulations. Considering the settling time for the controlled variable \(W_{m}\), the numerical simulation produces a value of ST = 0.1473 s, while the experimental value is of ST = 0.1859 s, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Simulation Test** & **Experimental Test** \\ \hline OS (\%) & 0.57 & 0 \\ ST (seg) & 0.1473 & 0.1859 \\ SSEror (\%) & \(-\)0.1645 & 0.4245 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the closed loop system: overshoot (OS), settling time (ST), and steady-state error (SSError).  
Figure 3b shows the motor supply voltage \((V_{a}=v_{c})\) for the simulation (black) and experimental (blue) tests, with very similar behavior shown for both the steady- and transient-state stability, although with a slight difference in time delay for the transient state. It is important to note that the supply voltage of the DC motor does not exceed 36 volts and that this has a low ripple during the steady state, which is optimal for the proper functioning of the DC motor and for maintaining the speed \((W_{m})\) with low steady-state error. Figure 3d confirms that the duty cycle (\(d\)) is not saturated during the steady state, which would generate a CPWM signal with fixed switching frequency.  
In Figure 3, there are some small differences between the results obtained with the simulation and the experiment, especially in the transient state. The experiment shows a longer setup time and less overshoot, because in the simulation test the parasitic resistor of the capacitor in the buck converter was not considered. Additionally, in the steady state there are also small differences between the simulation and experimental results, because the resistance used to measure the current in the inductor \((r_{Med})\) increases when the temperature of the shunt resistor \(r_{Med}\) increases.
# Planar Microwave Resonant Sensors: A Review and Recent Developments
## 2 Classification of Planar Microwave Resonant Sensors
Huge sensitivities and resolutions, useful to detect, e.g., small volume fractions or electrolyte concentrations in liquid solutions, have been achieved by means of these sensors. Although, typically, differential-mode sensors involve four-port measurements (these sensors are four-port devices), it has been demonstrated that by adding extra (microwave) circuitry, differential sensing by means of two-port devices (and measurements) can be achieved [68; 69]. In [69], the differential sensor works in reflection (other reflective-mode sensors have been reported [70], also including single-ended sensors [71]). The sensor reported in [72] is also remarkable, where symmetry truncation in a pair of slotted resonators generates quasi-microstrip to slot-mode conversion, useful for sensing. Although examples of the different sensor types highlighted in this section will be provided, the main emphasis in this paper is on differential-mode sensors. Nevertheless, before reviewing the different implementations, let us first present the potential candidates for resonant sensing elements, and discuss their advantages and disadvantages.
# The Biocompatibility of Wireless Power Charging System on Human Neural Cells
## 3 Results  
Based on the simulation analyses, we adjusted operational parameters (Input/Output) of the physical sample according to previously received results. The coupling coils system were placed within a thermo incubator and the biological samples (as was mentioned in Figures 6 and 7) were placed between the coils, as well as above the receiving coil (Figure 9). The operational frequency of WPT system as well as the frequency of EMFwas equal to 87 kHz, transmitted power was 1.5 kW and the value of magnetic induction within individual cells varied from 0.3 to 1.2 mT based on the location. Compared to the standards issued by ICNIRP 2010 (the limit is 27 \(\upmu\)T), the experimental settings exceed the limit in range 11-44 times. These increased values of induction were set to test more extreme conditions, compared to the limit ones. We compared the effect of EMF exposure on normal (astrocytes, HA) as well as on cancer (neural SH-SY5Y and glial T98G) cells.