esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Dynamic Initial Weights Assignment in SLS  
In the previous section, we discussed the negative effect of the equal assignment of the clauses' initial weights on SLS solvers' overall performance. Our experiments show that the small size clauses take a long time to build up weights to become attractive for the search to satisfy them, especially if their neighborhood's size is large. Secondly, the clauses' size plays a vital role when calculating the cost function, such that the large clauses are more likely to break more clauses than small clauses if they become unsatisfied. Thus, small clauses remain in their condition (whether satisfied or not satisfied) for longer than necessary time. These factors contribute to prolonging the search period in general. Therefore, we started thinking about developing a mechanism that would fill the gap between weights, which is often considerable, so that the time spent until a small clause build weights is much less. Our newly innovative algorithm is divided into two main parts. The first is to analyze a set of information from the problem structure that relies on collecting data before starting the search. The other part is to dynamically allocate initial weights for the clauses, based on the outcome from part one. Table 2 illustrates the problem-specific structural information, as discussed previously. Our novel mechanism starts with the information-gathering based on observations one and two in Section 3.1. More specifically, we count the total number of clauses (#MinClsS) of the minimum-sized clause (MinCIS) and the total number of clauses with maximum-sized (MaxClsS) of the maximum-sized clause (MaxCIS). Moreover, we calculate the average clauses size (AvgS) and the total number of average-sized clauses (#AvgS). Using these measures, we designed our mechanism to assign the initial weights as in Algorithm 1. The lines from 2 to 16 of Algorithm 1 show the initial weights allocation based on the clause (\(Cl\)) size. If the size of (\(Cl\)) is less than or equal to the arithmetic average of the sizes of the clauses (\(AvgCIS\)) in the problem (which means that the (\(Cl\)) is of small size), if it is, we check whether (\(Cl\)) belongs to a small neighborhood (\(size(Neighbor(Cl)<=AvgN)\), if so, we assign to the clause (\(Cl\)) an initial weight equal to the neighborhood size \(size(Neighbor(Cl))\) of the clause \(Cl\). If the size of the clause's neighborhood is large (\(size(Neighbor(Cl)>AvgN)\), we allocate (\(Cl\)) initial weight with the equivalent of the arithmetic average clauses' sizes (\(AvgCIS\)). For the clauses of large size (\(size(Cl)>AvgCIS\)), we check whether the neighborhood size associated with (\(Cl\)) is small (\(Neighbor(Cl)<=AvgN\)); in this case, we allocate the (\(Cl\)) weight to equal the arithmetic average of the clauses' sizes (\(AvgCIS\)). Lastly, if the clause is large (\(size(Cl)>AvgCIS\)) and located in a large neighborhood (\(Neighbor(Cl)>AvgN\)), we assign the initial weight value of (\(Cl\)) equal to the clause's size (\(cl\)).
## Towards the Realization of Graphene Based Flexible Radio Frequency Receiver  
**[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Microelectronics Research Center, Department of Electrical and Computer Engineering,  
The University of Texas at Austin, Austin, TX 78758, USA;  
E-Mails: [EMAIL_ADDRESS] (M.N.Y.); [PERSON]ADDRESS] (K.N.P.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (S.P.); [EMAIL_ADDRESS] (L.T.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]IL_ADDRESS]; Tel.: +1-512-471-4345.  
Academic Editor: [PERSON]  
_Received: 27 September 2015 / Accepted: 2 November 2015 / Published: 11 November 2015_  
**Abstract:** We report on our progress and development of high speed flexible graphene field effect transistors (GFETs) with high electron and hole mobilities (\(\sim\)3000 cm\({}^{2}\)/V\(\cdot\)s), and intrinsic transit frequency in the microwave GHz regime. We also describe the design and fabrication of flexible graphene based radio frequency system. This RF communication system consists of graphite patch antenna at 2.4 GHz, graphene based frequency translation block (frequency doubler and AM demodulator) and graphene speaker. The communication blocks are utilized to demonstrate graphene based amplitude modulated (AM) radio receiver operating at 2.4 GHz.  
**Keywords:** graphene; demodulators; antenna; AM radio receiver; speaker; transistor
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 1 Introduction
### Research Objective and Contribution  
This paper aims to predict mIoU of an image using semantic segmentation maps and design a network to determine whether it belongs to a failure case. In this study, we propose a two-stage network algorithm which evaluates the score to detect the failure case for the image input. First, the encoder network Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation (ESPNet) [31] extracts features for the image and semantic segmentation map. Second, failure cases of the segmentation network are detected by FCL for mIoU prediction of images through the proposed methods. As a result, the proposed method can be used as a basis for a fully autonomous driving system to allow self-diagnosis. Our main contributions can be summarized as follows:  
* We secure safety by proposing a failure detection network for image segmentation.
* Our model simultaneously performs mIoU prediction and failure detection for a single image.
* We propose a modified loss function to solve the data imbalance problem of the generated ground-truth (GT) mIoU.
* The proposed model exhibits good performance not only on the Cityscapes dataset but also on the Hyundai Motor Group (HMG) dataset.  
The remainder of the paper is organized as follows. Section 2 describes the complete process of our mIoUNet method and analyzes the structure of the model. Section 3 presents the experimental results using various network structures and the various input channels.  
Figure 2: An example of a convolutional neural network structure.  
The challenge dataset verifies the robustness of the network using the surround-view monitoring (SVM) camera road image provided by HMG. Finally, the paper ends with the conclusion in Section 4.
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 2 Optical Imaging System Principles for Large-Size Stepped Shaft Diameter Detection
### Symmetrical Double-Telecentric Optical Path for Large-Sized Stepped Shaft Detection  
Given the measurement range of diameters from 600 mm to 800 mm, the projection method is unsuitable for measurement. Therefore, an improved method based on a dual-field-of-view optical path with dual CCDs is proposed [15], as illustrated in Figure 2. The system comprises five main components: illumination, the object under measurement, reception, signal processing, and actuation. The working principle of the system is as follows: Light emitted from the source is transformed into parallel beams through a collimation and beam expansion system. After error compensation by a plane mirror, the light is projected onto the surface of the object under measurement, forming shadows due to the object's obstruction. These shadows are captured by a dual-field-of-view optical imaging system and received by two CCD sensors. Since the size of the object under measurement exceeds the sensing area of the CCD sensors, the two CCDs can only capture the edges of the object. After edge data acquisition, the actual size of the object under measurement is determined through the signal processing module in conjunction with previously measured dimensions of standard parts. For the diameter measurement of large-sized step shafts, a wide parallel light source and a dual-field-of-view optical imaging system are required. Thus, a collimation and beam expansion system is added between the light source and the object under measurement. A plane mirror, added between the collimation and beam expansion system and the object under measurement [16], compensates for angular errors due to installation. The light source utilizes a LASER525 nm-100 nW with a laser wavelength of 525 nm. The CCD employed is a TCD1501D, with a pixel size of 7 um \(\times\) 7 um.
# The Effect of Laser Pulse Widths on Laser--Ag Nanoparticle Interaction: Femto- to Nanosecond Lasers
## 3 Numerical Simulation and Experimental Setup  
### Numerical Analysis  
To numerically analyze the interaction between a nanosecond pulse-width laser beam and a nanoparticle, a simulation was carried out using COMSOL ver. 5.2 software (COMSOL, Inc., Burlington, MA, USA). To validate the simulation, the results were compared with an analytical solution based on Mie theory in our prior research [52].  
As described in the previous chapter, in the nanosecond region, the difference in the photothermal effect according to the pulse width can be numerically confirmed by the dispersion of the wave and its absorption by the particle. For the simulation, pulse widths of 4, 32, 64, and 100 ns were used. The simulation was performed using a water-encapsulated virtual Ag nanoparticle with a diameter of 100 nm. Regarding the optical properties, the complex refractive index according to wavelength was evaluated using the optical property database in COMSOL 5.3. The complex refractive index of a particle can be used to confirm the behavior of a wave in that particle, and is related to energy absorption.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## Appendix A Expressions for Direct and Reflected Waves
\] (A6)  
Here, \(u_{\text{SH}}\), \(u_{\text{SV}}\), \(u_{\text{P}}\), \(u_{\text{SP}}\) and \(u_{\text{PS}}\) are the borehole displacement stimulated by SH-SH, SV-SV, P-P, P-SV and SV-P waves, respectively; \(R=\sqrt{r^{2}+z^{2}}\), \(k_{P}=\omega/\alpha\cos\theta\), \(k_{S}=\omega/\beta\cos\theta\), \(\cos\theta=z/R\); \(\rho_{f}\), \(\rho\), \(\alpha\) and \(\beta\) are the fluid density in the borehole, the formation density outside the borehole and the P-wave and S-wave velocities of the formation, respectively; \(G_{SH}\), \(G_{SV}\), \(G_{P}\), \(G_{PS}\) and \(G_{SP}\) are the plane reflection coefficients of SH-SH, SV-SV, P-P, P-SV and SV-P waves, respectively [31]; \(L\) is the total propagation distance of the reflected wave; \(L_{P}\) and \(L_{S}\) are the distances between the receiver in the borehole and the virtual source of the SV-P and P-SV waves; \(L_{P}\) and \(L_{S}\) are distances associated with the geometric spreading of SV-P and P-SV waves; expressions for \(L\), \(L_{P}\), \(L_{S}\), \(L_{P}\) and \(L_{S}\) can be found in the study of [PERSON] and [PERSON] [28]; \(\theta_{1}\) is the radiation take-off angle of SH-SH, SV-SV and P-P waves from the borehole of the reflected wave; \(\theta_{2}\) is the reception angle of SH-SH, SV-SV and P-P waves upon the borehole of the reflected wave; \(\theta_{1}^{\text{P}}\) and \(\theta_{1}^{\text{S}}\) represent the incident angle and reflection angle of the P-SV wave relative to the geologic interface, respectively; \(\theta_{1}^{\text{S}}\) and \(\theta_{2}^{\text{P}}\) represent the incident angle and reflection angle of the SV-P wave relative to the geologic interface, respectively; and the amplitude coefficients \(C\), \(D\) and \(E\) are obtained by the borehole boundary conditions.
# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### Target Recovery  
It is clearly known that a re-detection module is essential for realizing a robust visual tracking. To better cope with tracking failures and continue tracking in subsequent frames, we employ the EdgeBox [15] as our detector and use this method to generate candidate regions: candidate regions detection \(C_{d}\) is across the entire image and has large step size. In addition, we need to calculate the confidence value of candidate \(c\) and use a traditional learning rate to learn spatial-temporal context correlation filters in order to maintain a long-term memory of the target appearance. Give a candidate \(c\), we represent the maximum response value of correlation filter by \(g(c)\).  
We consider the response map peak as a dynamic threshold with comparison to corresponding \(PSR\) score (Section 3.3.1). Only when the \(PSR\) score is lower than its corresponding response map peak score \(R_{max}\) and the tracking failure occurs at this time, will it proceed with target re-detection. We produce a set of candidate regions across the whole frame for recovering target objects. We choose the desired candidate as our re-detection result by minimizing the following issue:  
\[\text{argmin}_{\text{i}}\ g(c_{t}^{i})+\alpha D(c_{t}^{i},c_{t-1}) \tag{11}\]  
In Equation (11), the purpose of the weight factor \(\alpha\) is to get a balance between candidate regions value and motion smoothing. \(D\) denotes the center location distance between each candidate \(c_{t}^{i}\) and the bounding box \(c_{t-1}\).
## 3 RLS Design
### Network Model and Assumptions  
To accomplish a localization task in underwater wireless sensor networks, we consider three dimensional UWSNs where sensor nodes are randomly distributed in different depth of water. A possible deployment of 3-D underwater for RLS design is shown in Figure 2. Underwater nodes are assumed to be equipped with pressure sensors by which they learn their depth information. The network architecture consists of two different types of nodes and also a sink station to establish the centralized localization.  
* _Ordinary sensor nodes._ Sensor nodes float and mobile with water currents at different depth in order to observe or detect a phenomenon. These are low-complexity sensor nodes which is equipped with pressure sensor to calculate depth information, so z-coordination is available in underwater environments and 3-D localization problem is transferred to 2-D localization. Underwater sensor nodes have limited power and should preserve their energy.  
* _Surface anchor nodes_. Anchor or beacon nodes are location-aware to help ordinary sensor nodes to estimate their location. Since it may not be practical (either infeasible or cost-prohibitive) to place anchors on the sea floor in 3-D UWSNs [15], they are usually deployed at the surface like buoys and are equipped with GPS, RF and acoustic transceivers.
* _Sink or base station_. The base station is the data gatherer, which is usually a common computer or embedded system with higher process capacity [20]. The localization task is occurred in sink, as soon as it receives sensed data.
# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Weaknesses and Anti-Forensic Use Cases
#### 4.1.5 Weaknesses and Anti-Forensic Use Cases in Flash Drives  
In contrast to the previous section, this one is very general, as it gathers studies applied to Flash memories, which include USB flash drives, SSD hard disks, MMC and SD cards, etc.  
Authors such as [PERSON] et al. [161] deal with anti-forensic techniques applied to invalid spaces of NAND flash memories; others, such as [PERSON] et al. [162], expose anti-forensic wiping techniques for flash drives.  
[PERSON] et al. [163] focus on anti-forensic techniques, specifically those that use RAM to hinder digital forensics using Live CD's and the variety of software packages that can be used for anti-forensic purposes.  
[PERSON] et al. [164] evaluate the information that can identify the use of a USB flash drive on a Windows XP computer and seek to understand how this information could be used by a computer forensic investigator. An anti-forensic tool was created to perform a function to modify the information related to the use of USB flash drives.  
Another article from [PERSON] et al. [165] focuses on the challenge of forensic data recovery on NAND flash memory due to background management operations that make it difficult to completely remove information. They propose a secure reverse copying technique that minimizes the possibility of exposing residual data and altering adjacent cells to avoid reliability issues.  
[PERSON] et al. [166] develop a methodology for investigators and forensic analysts in making informed decisions about the recovery of digital evidence on SSDs, taking into account hardware optimizations and the possible anti-forensic techniques applied.  
[PERSON] et al. [167] examine how the anti-forensic tool \"Fbinst\" interferes with the digital forensics investigation of USB media and proposes methods by which to counteract its effects.  
Further papers explore the vulnerabilities of flash drives, especially in recovering deleted data due to the nature of NAND memory [161; 162; 165; 166]. Methods for secure data erasure on these drives are proposed [161; 165], as well as schemes by which to minimize data erasure delay [162]. On the other hand, general anti-forensic techniques are described [163], including the use of live CDs for malicious purposes, and how to modify the Windows registry to hide the use of USB sticks is discussed [164]. A specific tool (Fbinst) used to create hidden partitions on flash drives and hinder digital forensics is also discussed [167].  
As novel contributions, article [166] proposes a methodology by which to evaluate the \"forensic friendliness\" of an SSD disk, and article [167] discusses a specific anti-forensic tool (Fbinst) used to create hidden partitions on flash drives.
## 4 Effect of Damping Control Parameters on Wave Energy Attenuation
### The Effect of the Thickness of a Specific Single Absorbing Layer \(\Delta l\)  
Regarding the effect of \(\Delta l\) on energy absorption, the wavelength occupies ten grids (Equation (12)), and \(\Delta l\) is taken to be one and two times the grid size (20 and 40 cm); with \(k\) = 1.08, \(L\) is taken to be 2\(\lambda_{min}\) and \(n\) = \(L/\Delta l\). In this paper, the number of absorbing layers, as well as the damping growth curves, were compiled for individual absorbing layer thicknesses of \(\Delta l\) and 2\(\Delta l\), respectively, as shown in Figure 14a. It can be seen that the number of absorbing layers \(n\) is ten when the absorbing layer thickness is taken to be  
Figure 11: Numerical model of the obliquely incident wave field.  
Figure 12: Snapshots of the wave fields for an obliquely incident uniform soil.  
two times the grid size. By contrast, the quantity of absorbing layers doubles the original one when the thickness equals the grid size. With more absorbing layers, the increasing curve of \(\alpha\) becomes smoother, and the damping of the absorbing layers increases in an orderly manner. In this case, the differences in impedance and wave velocity between the absorbing layers have little variation, and reflection of the pulsed wave between absorbing layers is less likely to occur. In addition, the instantaneous energy peaks of 31 sensors were extracted, and it can be seen that the attenuation curve of the outgoing wave becomes smoother when the thickness of a single absorbing layer is taken as \(\Delta l\), and the attenuation ability for pulse waves is enhanced by about 1.4-1.8 times that of a two-fold mesh size, as shown in Figure 14b.
## 4 Analysis, Results and Discussion
### Noise Levels in the Forecasting  
Having concluded that there are some strong correlations between the noise levels and the number of mobile phones in a nightlife area, the possibility arose that the noise levels in the LSTM models could increase the accuracy of their forecasts. Therefore, the areas of the city with noise sensors were identified, and their neural networks were fitted again. The results based on the test set can be seen in Table 4, where they are compared with the results obtained without the inclusion of the noise levels.  
The only sensor that significantly increased the accuracy of the model was RULAEQ0005, with the RMSE (root mean square error) on the test set decreasing by five units in that area (there was also a decrease in the RMSE on the train and validation set). It can also be noted that the MAPE increased by 0.033 percentage points, from 11.395 to 11.428. As these two errors contradict each other, the choice was made to rely on the findings of the RMSE, as that is the error the model was trained on, and the dataset is the same for both models. The choice to rely on the RMSE is the reason that we concluded that sensor RULAEQ0076 did not improve the accuracy of the model, despite the fact that the MAPE decreased by 0.120 percentage points. Comparing these results with the Pearson's correlation coefficients in Table 3, it can be observed that high correlations to noise levels do not necessarily increase the accuracy of the forecasts (in the context of these LSTM networks). In fact, the only model that had an increase in its accuracy corresponds to a noise level sensor with one of the lowest means in correlation degree. The fact that there was an increase in the accuracy of one LSTM network shows that relocating the noise sensors to more appropriate locations should lead to big increases in the accuracy of the neural networks in general.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & **Grid** & **RMSE (Noise)** & **RMSE (Before)** & **MAPE (Noise)** & **MAPE (Before)** \\ \hline
3 & 304 & 37.709 & 35.982 & 34.297 & 33.425 \\
5 & 344 & 110.499 & 115.830 & 11.428 & 11.395 \\
12 & 678 & 87.696 & 86.607 & 11.185 & 11.022 \\
14 & 746 & 92.086 & 90.762 & 7.776 & 7.536 \\
17 & 796 & 60.614 & 58.445 & 10.283 & 9.989 \\
76 & 742 & 80.196 & 78.757 & 10.713 & 10.833 \\
77 & 624 & 41.835 & 41.853 & 17.254 & 17.359 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the original models compared with those obtained with noise levels.  
Figure 15: Weak correlation between the noise sensor and the number of mobile phones.  
When comparing the night establishments identified by Lisbon municipality with the clusters identified by the algorithm, it is noticeable that there is an overlap between the location of such establishments and the clusters, as depicted in Figure 16.