esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Top-Down Fabrication Process for Vertically-Stacked Silicon-Nanowire Array
###### Abstract  
Silicon nanowires are widely used for sensing applications due to their outstanding mechanical, electrical, and optical properties. However, one of the major challenges involves introducing silicon-nanowire arrays to a specific layout location with reproducible and controllable dimensions. Indeed, for integration with microscale structures and circuits, a monolithic wafer-level process based on a top-down silicon-nanowire array fabrication method is essential. For sensors in various electromechanical and photoelectric applications, the need for silicon nanowires (as a functional building block) is increasing, and thus monolithic integration is highly required. In this paper, a novel top-down method for fabricating vertically-stacked silicon-nanowire arrays is presented. This method enables the fabrication of lateral silicon-nanowire arrays in a vertical direction, as well as the fabrication of an increased number of silicon nanowires on a finite dimension. The proposed fabrication method uses a number of processes: photolithography, deep reactive-ion etching, and wet oxidation. In applying the proposed method, a vertically-aligned silicon-nanowire array, in which a single layer consists of three vertical layers with 20 silicon nanowires, is fabricated and analyzed. The diamond-shaped cross-sectional dimension of a single silicon nanowire is approximately 300 nm in width and 20 \(\upmu\)m in length. The developed method is expected to result in highly-sensitive, reproducible, and low-cost silicon-nanowire sensors for various biomedical applications.  
silicon nanowire; top-down fabrication; monolithic process; vertically-stacked array +
Footnote †: journal: Applied Physics Letters  
_Applied Physics Letters_
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 2 Method and Experimental Results  
This section presents the working principle of the measurement technique, along with an explanation of the sensor's structure, a theoretical description of the developed analytical model and the results obtained using a proof of concept laboratory test bench, which are ultimately benchmarked against analytical predictions.
# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 4 Traffic Sign Detection, Tracking and Classification Methods
### Detection Phase
#### 4.1.3 Hybrid Methods  
As previously discussed, both color-based and shape-based methods have some advantages and disadvantages. Therefore, researchers recently have tried to improve the efficiency of the TSDR system using a combination of color- and shape-based features. In the hybrid methods, either color-based approaches take shape into account after having looked at colors, or shape detection is used as the main method but integrate some color aspects as well. In color-based approaches a two-stage strategy is usually employed. First, segmentation is done to narrow the search space. Subsequently, shape detection is implemented and is applied only to the segmented regions [58]. Color and shape features were combined into traffic sign detection algorithms in studies [71; 72; 73; 74; 75; 76]. In these studies, different signs with various colors and shapes were detected using different datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Technique** & **Paper** & **Overall Forces** & **Recognition** & **Advantages** & **Sign Type** & **No. of Test Image** & **Test Image Type** \\ \hline \multirow{6}{*}{**Hough Transformation**} & [77] & \multirow{6}{*}{**Local**} & N/A & Invariant to to give better rotation and & **Clogging, square,** & 45 & Low-ens \\  & [78] & & viewing angle & triangle & & & \\ \cline{2-7}  & [79] & & AddBoost & High accuracy & Avry sign & N/A & Low-ens \\ \cline{2-7}  & [79] & & Rebustness to Illuminate, scale, & & & & \\ \cline{2-7}  & [79] & Each pixel of edge image votes for the object center at object boundary & & & & & \\ \cline{2-7}  & [80] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [81] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [82] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [83] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [84] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [85] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [86] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [87] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [88] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [89] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [87] & & Rebustness to Illuminate, scale, & & & \\ \cline{2-7}  & [88] & & Rebustness to Illuminate, scale, & & & \\ \hline  
**DTM** \\ \hline
**Edge Detection** \\ \hline
**Feature** \\ \hline
**Edge Detection** \\ \hline
**Feature** \\ \hline
**Edge with** \\ \hline
**Features** \\ \hline \end{tabular}
\end{table}
Table 4: Shape-based methods for TSDR system.
# Investigations into Frost Flower Physical Characteristics and the C-Band Scattering Response
## Materials and Methods
### LiDAR Measurements  
A Leica Scnstation C10 Light Detection and Ranging (LiDAR) system provided measurements of the centimeter-scale roughness of the ice surface. The scanner was mounted on a tripod and positioned on a platform directly beneath the scatterometer at a height of 2.1 m above the sea ice surface. Surface roughness data were acquired over a coincident area to radar backscatter measurements. The LiDAR was configured to emit pulses at a frequency of 50 kHz and to record the time-of-flight for each of the pulses reflected by the ice and received by the scanner. The system operates at a wavelength of 532 nm, so laser pulses are consistently reflected by surfaces, such as snow and ice, but are absorbed by water [29].  
The raw scan included outlying points located well above or below the true pool surface, which are caused by laser reflections from precipitation and were removed using algorithms based on the morphological and adaptive triangular irregular network (TIN) filters of References [30; 31], respectively. A series of 0.5 m \(\times\) 0.5 m square sections were extracted from the LiDAR scan along the azimuthal swath of each scatterometer incidence angle from 20\({}^{\circ}\) to 45\({}^{\circ}\). Each section was aligned to the direction of the scatterometer to coincide with the radar footprints as closely as possible. LiDAR points within each subsection were resampled to a regular grid made up of 2 mm \(\times\) 2 mm cells using linear interpolation. Minor large-scale topography at the ice surface (caused by slight spatial variations in sea ice growth rates) was removed through spectral decomposition of the roughness power spectral density (PSD) and filtering out topography with a lateral wavelength \(>\)0.5 m. Two statistical measures of surface roughness: (i) root mean square (RMS) height (h) and (ii) correlation length (L\({}_{c}\)) of deviations from a mean level through the grid were calculated for each subsection based on the algorithms of Reference [32]. The initial results were then calibrated using the functions provided in Reference [33], which accounted for bias introduced by the high inclination scanning angle of the LiDAR sensor to produce precise and accurate surface roughness parameters. Single parameters were estimated for each incidence angle of the scatterometer by averaging all subsections along a swath. Values for the RMS height and correlation length varied by less than 1 mm and 5 mm, respectively, between subsections along each scatterometer swath.
# Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine
## 2 Preliminary and Related Work
### Elms  
The basic ELM can be interpreted as two components, i.e., random hidden mapping and ridge regression classifier. Formally, ELM's hidden layer can be expressed as  
\[\mathbf{Z}=\sigma(\mathbf{X}\boldsymbol{\Theta}+\boldsymbol{b}), \tag{6}\]  
where \(\mathbf{Z}\) is the hidden layer output matrix parameterized by hidden weight matrix \(\boldsymbol{\Theta}\) and bias vector \(\boldsymbol{b}\), \(\sigma\) denotes a nonlinear activation function such as Sigmoid. In the second stage, ELM computes prediction by  
\[\mathbf{Y}=\mathbf{Z}\boldsymbol{\beta}. \tag{7}\]  
Here, \(\boldsymbol{\beta}\) is the output weight matrix. Since \(\mathbf{Z}\) is known to the output layer, Equation (7) essentially is a least-squares optimization problem and can be solved as  
\[\boldsymbol{\beta}=\mathbf{Z}^{\dagger}\mathbf{Y}_{\mathcal{T}}. \tag{8}\]  
ELM avoids iterative parameters tuning, thus significantly faster than gradient descent-based neural networks. SS-ELM is the semi-supervised version of ELM by introducing a graph Laplacian regularization term [21]. Its formulation is given by  
\[\operatorname*{arg\,min}_{\boldsymbol{\beta}}\lVert\mathbf{Z}_{\mathcal{T}} \boldsymbol{\beta}-\mathbf{Y}_{\mathcal{T}}\rVert_{F}^{2}+\lambda \operatorname{tr}\left(\boldsymbol{\beta}^{T}\mathbf{Z}^{T}\mathbf{L} \mathbf{Z}\boldsymbol{\beta}\right). \tag{9}\]  
SS-ELM also has a closed-form solution. It should be noted that the graph structure information is considered in SS-ELM but it essentially works in the regular Euclidean domain.  
Figure 1: Visual comparison between a simple graph and a hypergraph. (**a**) An example of the simple graph (left) and its corresponding adjacent matrix \(\mathbf{A}\) (right). (**b**) An example of hypergraph (left) and its corresponding incidence matrix \(\mathbf{H}\) (right), where edges with the same color denote a hyperedge. Best view in color.
# Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification
## 3 Experiment Setup and Material
### Particle Suspension Preparation and Cell Culture  
In the current work, polystyrene sphere (PS) particles and two types of spheroid living cells were employed to investigate our system performance. Polystyrene sphere suspensions of 2 \(\upmu\)m and 5 \(\upmu\)m diameter (Zhongkeleiming, Beijing, China) at different concentrations were prepared by dilution and ultrasonication of the original PS aqueous solution with ultrapure water. The PS particles have good dimension uniformity (diameter coefficient of variation values was better than 3%). Two types of human living cells were also cultured and prepared elaborately for living cell characterizations. One type is the human breast cancer cell line (MCF7) of 15 \(\upmu\)m average diameter and the other is the human embryonic kidney (293) of 11 \(\upmu\)m average diameter, all the average dimension values are estimated from the Harvard BioNumbers database [37]. Both cells were obtained from the American Type Culture Collection (ATCC) and cultured in Dulbecco's modified eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% penicillin / streptomycin, 1% glutamine, 10 ug/mL insulin and 1 mM sodium pyruvate. The Cells were maintained at 37 \({}^{\circ}\)C temperature and 5% humidified CO\({}_{2}\) atmosphere.
## 3 Results and Discussion
### Leaking Test  
The ability of EG to retain the different PCMs is investigated considering the leaking test curves shown in Figure 3, in which the PCM loss of the pressed disks is reported as a function of time.  
The fatty acid mixture, MPA, interacts much better with EG with respect to the two commercial paraffins, deducible by the much less pronounced leakage after every mass measurement, experiencing only 0.55 wt.% of mass loss after three hours, around 10 times lower than those of RT35 (\(-\)5.36 wt.%) and RT44 (\(-\)5.11 wt.%). The slightly lower leakage of RT44 in comparison with RT35 can be probably attributed to the higher melting point of the first one, hence the higher viscosity at the test temperature of 60 \({}^{\circ}\)C, considering the same aliphatic nature of the oligomers. Similarly, the slightly higher melting point of MPA (see the following) could influence the leakage behavior. However, for the test, a constant temperature was preferred to a fixed range above the PCM melting point to better simulate the real conditions in which when all the PCM is molten, the system temperature tends to stabilize with that of the PV panel, proportionally to the striking irradiance.  
The most pronounced leakage is evidenced for RT35/EG14 and RT44/EG14 during the first hour, a behavior already encountered in previous works by the authors [35; 39]. The pictures of the specimens after one hour of testing perfectly match the trend of the plot, showing a particularly contained leaked MPA halo.  
For these reasons, the fatty acid mixture can be considered the best candidate to be shape-stabilized in EG, probably due to the higher chain polarity and higher capillary forces conferred by the carboxylic groups [35]. Another explanation could be the planar shape of the fatty acid flakes, which makes them suitable to be intercalated among the already distanced graphitic lamellae [39; 41].
# Temporal-Guided Label Assignment for Video Object Detection
## 3 Methodology  
### Overview  
The architecture of our method of temporal guided Label assignment (TGLA) is illustrated in Figure 2. Given a video, while processing a frame it is noted as the target frame \(I_{t}\). The adjacent frames of the target frame are specified as support frames \(\{I_{t^{\prime}}\}\). First, we proposed a temporal-guided label assignment framework for the learning task of the RPN. Labels produced by label assignment are enhanced. The blue box above is the forward propagation process of the two-stage object detection model. The orange box below is the training process. To establish the labels' relation model, we then introduce a feature instructing module (FIM). All our proposed modules ultimately act on the loss function. Therefore, our method can improve model accuracy without increasing inference costs.
## 6 Security Solutions
### Communication Security
#### 6.2.2 End-to-End Network Security
In the HIP-PSK scheme, each IoT device needs to share a secret key with the Domain Manager. With a network of a large number of IoT devices, the distribution of the secret keys, thus, becomes a challenging task. Similarly, in Slimfit, it cannot be ensured that all the devices of an IoT network have the Slimfit layer. Therefore, the interoperability between devices with and without the Slimfit layer cannot be guaranteed. Furthermore, it is a complicated task to embed the Slimfit layer with a large number of IoT nodes. Finally, although the LHIP [126] protocol is found resource efficient, this scheme does not incorporate some of the essential security requirements, such as authentication and confidentiality.
## 3 Element Design  
### Antenna Structure  
In Section 2, an array was designed to obtain a radiation pattern featuring a pencil beam with an HPBW of about \(3^{\circ}\) and an SLL lower than \(-30\) dB to be scanned over an angular range of \(60^{\circ}\) on the elevation plane and \(360^{\circ}\) on the azimuthal plane. At this stage, it is necessary to design the antenna element to meet the requirements of low cross-polarization and isolation over the \(f_{\rm min}\)-\(f_{\rm max}\) band, and ensure polarization reconfigurability. In this regard, the low cross-polarization requirement calls for elements that are symmetric along the principal planes, such as square patch antennas [18]. The latter, however, rarely maintain such performance over fractional bandwidths as large as \(10\%\), unless stacked configurations (typically with the top substrate thicker than the bottom one) are considered [20; 21]. As per the polarization reconfigurability, two ports are considered and fed with aperture-coupled techniques to ensures the required isolation [10; 22], achievable even with electromagnetic band-gap (EBG) structures [23]. These aspects led us to consider the antenna architecture proposed in [10] as a reference structure to be suitably modified and optimized to meet our requirements in the \(f_{\rm min}\)-\(f_{\rm max}\) band.
# Structural Health Monitoring of a Road Tunnel Intersecting a Large and Active Landslide
## 1 Introduction  
Structural Health Monitoring (SHM) focuses on damage identification, detection and quantification in engineering structures [1]. For large constructions such as bridges, dams, skyscrapers and tunnels, SHM has become more widespread [2; 3; 4], and is currently implemented starting with the early design process. However, for medium-size constructions, proper SHM is rarely conducted, even though progressive damage is present. The definition of the degree of safety and reliability over time of such structures still relies more on expert judgement of inspectors rather than actual monitoring data. Nevertheless, Damage Prognosis (DP), which is the assessment of the remaining useful life of an engineered system, supported by SHM [5], should be pondered when the risk associated with failure is elevated and when measures to mitigate the progression of damage may be implemented. The advantages of SHM should also be considered with respect to the costs associated with the maintenance, traffic disruption and eventual replacement [6].  
SHM of tunnels is particularly important since these structures are not easily replaceable [7]; it has been applied at various sites, especially for large projects in urban areas [8] or complex geotechnical conditions [9]. However, SHM has rarely been used to study the effect of the interaction between a slow-moving landslide and a tunnel, which may induce the accumulation of stresses and displacements, developing cracks and then collapsing under the unremitting movements of the slope instability [10]. In this situation, the monitoring effort most often focuses on the landslide since monitoring networks following the evolution of natural hazards are widespread [11; 12]. However, the dynamic of the interaction between the structure and the slope instability phenomenon is far from linear and finding a system that would measure the actual damage within a tunnel is crucial for DP and for providing data useful for the design of effective mitigation works.  
This paper describes a real-time monitoring network deployed in 2014 in a road tunnel in Italy where cracks are developing at its intersection with the slip surface of a large landslide.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 3 Proposed Scheme
### Proposed PMIPv6 Protocol Extension
In the case of existing RSS-based extensions, RSS errors had a major effect on performance efficiency. Additional signaling and unnecessary information communication during RSS errors are avoided in the proposed solution. Furthermore, information communication regarding target MAG-ID provides an accurate measure for LMA to know in advance the MN's expected MAG. In addition, such information also helps LMA to effectively manage the load on entities. However, such a problem is not in the scope of this paper. Target MAG-ID avoids the multi-casting of IHR messages to the surrounding MAGs of LMA. The overall working flow of messages and entities involved in the proposed protocol extension is shown in Figure 1.
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Clutter Map CFAR Detector
#### 2.1.4 Analysis of the CMAP-CFAR  
In this section, we analyze the performance of the CMAP-CFAR detector for a single pulse. We will plot the detection probability performance curves as a function of the SNR for different values of Pfa and \(\alpha\). We assume that the target is the Swerling I type, embedded in Weibull clutter. A Monte Carlo simulation is performed for different values of Pfa and \(\alpha\). The threshold Th is calculated from Equation (7). Given the weighting coefficient \(\alpha\), we vary the probability of a false alarm. Table 1 below shows the values of Th for different values of Pfa and \(\alpha\).  
\begin{table}
\begin{tabular}{c c c} \hline \(\alpha\) & **Pfa** & **Th** \\ \hline  & \(10^{-2}\) & 4.6 \\ \cline{2-3}
0.01 & \(10^{-4}\) & 9.4 \\ \cline{2-3}  & \(10^{-6}\) & 143 \\ \hline  & \(10^{-2}\) & 9 \\ \cline{2-3}
0.5 & \(10^{-4}\) & 31.4 \\ \cline{2-3}  & \(10^{-6}\) & 76.6 \\ \hline  & \(10^{-2}\) & 25.6 \\ \cline{2-3}
0.9 & \(10^{-4}\) & 190 \\ \cline{2-3}  & \(10^{-6}\) & 854 \\ \hline \end{tabular}
\end{table}
Table 1: Threshold values Th as a function of Pfa for different \(\alpha\) values.  
the system tends to have a fixed threshold. On the other hand, when \(\alpha\) is large (\(\alpha\) tends toward 1), the previous samples are omitted to arrive at an estimate of the threshold, hence decreasing the probability of detection. The CMAP-CFAR detector exploits the local intrinsic homogeneity of the radar environment, in which the radar space is divided into clutter map cells (see Figure 1) [18]. It is best used for small radar-equivalent areas or targets flying tangentially to the circle formed by the target-radar core [19].
## 2 Materials and Methods
### CNN Design  
The thrombus detection was based on two observations: (1) the asymmetry between the hemispheres and (2) the HAS of the thrombus on NCCT images. Therefore, we developed two CNNs: an asymmetry detection network and a HAS detection network. Both networks were patch-based. The assymetry detection network consisted of two parrallel convolutional pathways, one for each hemisphere. The output of both pathways was combined and fed to the fully connected layers. The HAS network consisted of a single convolutional pathway. Patch size was \(24\times 24\times 24\) voxels, corresponding to \(12\times 12\times 12\) mm.  
Both networks had a design similar to AlexNet (2018) and consisted of five 3D convolutional layers with a \(5\times 5\times 5\) dimension followed by a Rectified Linear Unit (ReLU) layer (see Figure 1). Within the first three layers, we decreased the dimension of the feature maps and increased the number of feature maps by a factor two for each layer. Subsequently, we decreased the number of feature maps by half within each subsequent layer. To reduce the chance for overfitting, we added an additional dropout after the first fully connected layer. We used the default settings of the pytorch library, which is 0.5 for fully connected layers.  
The fully connected part of both networks consisted of two fully connected layers followed by ReLU. The input of the fully connected part of the HAS detection network consisted of a single output from the convolutional layers. For the assymetry detection network, we concatenated the output from the two parallel convolutional pathways before feeding it to the fully connected layers. We added an additional dropout after the first fully connected layer. The CNNs were trained and validated seperately. During training, we alternated thrombus patches with non-thrombus patches for input so that the number of thrombus and non-thrombus was balanced. Because the thrombus is very small, we only required a small number of non-thrombus patches to balance the data. As it is possible to extract an abundant number of non-thrombus patches from a single scan, we did not need additional scans to extract negative patches. During the training process, the hyperparameters of the CNN were optimized for correctly discriminating thrombus patches from non-thrombus patches.