esa-sceva/satcom-chunk-collection huggingface dataset:
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 4 Experiments  
### Datasets  
The datasets used in this paper include the GoPro dataset [8], the RealBlur dataset [37], and the RWBI dataset [31].  
The GoPro dataset is commonly used for training and evaluating deep-learning-based deblurring methods, which is produced from clear videos captured at 240 fps (frames per second) using the GoPro Hero4 Black camera, and the blurred images are obtained by averaging sharp videos over time windows of different durations, which correspond to the sharp images in the center of the time window. The GoPro dataset consists of 2103 pairs of blurred and sharp images for training and 1111 pairs of images for testing. In this paper, the GoPro dataset is applied to the training of IDR\({}^{2}\)ENet.  
The RealBlur dataset, produced by [PERSON] et al. [37], contains paired real blurred images and consists of two subsets with the same image content, RealBlur-J and RealBlur-R. RealBlur-R is generated from raw camera images (RAW images) and RealBlur-J is generated from JPEG images processed by the camera ISP. Each subset contains 4738 pairs of blurred and corresponding real sharp images from 232 different low-light static scenes, of which 3758 pairs are used for training and 980 pairs are used for testing. In this paper, the RealBlur dataset is applied to the testing of IDR\({}^{2}\)ENet.  
The RWBI dataset contains 3112 real blurred images from 22 different scenes. These blurred images were obtained from a variety of mobile devices, including Huawei P30 Pro, Samsung S9 Plus, iPhone XS, and GoPro Hero5 Black cameras. However, it is worth mentioning that the RWBI dataset only contains real blurred images without the corresponding sharp images. Therefore, the RWBI dataset is only for the testing of IDR\({}^{2}\)ENet in this paper.
# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
###### Abstract
Furthermore, existing methods mostly utilize skip connections, directly merging the features of the encoder into the decoder. While this approach partially alleviates the issue of information loss, it inevitably introduces noise and redundancy. Given the aforementioned circumstances, this paper introduces a novel Dual Attention Mechanism Weak Edge Target Segmentation Network (WETS-Net) to address these challenges. The network is founded on the U-Net architecture and integrates dual attention mechanisms to, respectively, enhance the network's feature extraction and fusion capabilities. Specifically, an enhanced spatial attention mechanism is introduced in the shallow encoder of the U-Net, optimized in conjunction with edge extraction algorithms like the Laplacian operator. Through the aggregation of cross-scale gradient information, this module achieves selective extraction and enhancement of weak edges and texture features, enabling the network to more sensitively capture subtle variations in weak target regions and providing essential priors for subsequent segmentation tasks. Additionally, to address the underutilization of multi-scale information during feature decoding in U-Net, a channel attention mechanism is introduced at the skip connection points in this study. Through adaptive learning, it significantly evaluates features at different scales from the encoder and adjusts the weights of these features during the decoding process. Unlike directly concatenating features from different scales, the introduction of the channel attention mechanism allows for selective enhancement of features in target regions based on task requirements and properties, suppressing redundant background interference, thereby making the fusion of multi-scale information more efficient and targeted. By combining these two significant innovations, the WETS-Net demonstrates a notable performance enhancement in the realm of weak edge object segmentation, paving the way for novel directions in subsequent research endeavors. The main contributions of this paper are as follows:  
1. Proposed an improved Weak Edge Target Segmentation Network, WETS-Net, which significantly enhances the network's extraction and fusion capabilities for weak target edges and multi-scale features by introducing a dual attention mechanism in the encoder and decoder of U-Net. 2. Drawing inspiration from the Laplacian operator's role in edge detection, a novel Edge Enhancement Convolution (EE-Conv) was designed and applied in constructing the Edge Attention Extraction Module (EAEM). Positioned at the shallow feature output location of the encoder, EAEM effectively extracts and enhances the edge and texture features of weak targets. 3. Introduced a channel attention mechanism at the skip connections of U-Net and devised a Multi-Scale Information Fusion Module (MIFM). MIFM adaptively adjusts the importance of different scale features from the encoder and decoder, facilitating efficient fusion of multi-scale features and further enhancing the segmentation performance of the network.
# A Compact Planar Wi-Fi Antenna with Optimized Radiation Patterns for Small UAV Applications
## 2 Antenna Design and Analysis  
In this study, a compact slot antenna with a coplanar waveguide (CPW) was designed that supports Wi-Fi dual-band communication and has a single-sided planar structure.
## 3 Materials and Methods
### Theoretical Study
#### 3.2.4 Expected Number of Clicks  
We have studied the probability of attracting the first click from the user and the number of impressions required to obtain that click. However, as we have mentioned, an advertiser would potentially like to obtain many clicks. Therefore, in this section, we focus on studying the expected number of clicks from each user. As for the previous case, we will consider both probabilities as constant (\(p_{I}=c_{1}\) and \(p_{C}=c_{2}\)).  
This time we refer to \(X\) as the variable that captures the number of clicks a user will realize out of all the received impressions. In the previous section, we have calculated the probability of the first click that is reaching one of the states \(1,01,001,0001,\cdots\) for obtaining two clicks the paths are reaching one of the previous and then adding to the path \(1,01,001,0001,\cdots\). Therefore, we have that \[P(X\geq 1) =\frac{p_{C}}{1-p_{I}(1-p_{C})}\] \[P(X\geq 2) =P(X=1)*P(X=1)\] \[P(X\geq 3) =P(X=1)*P(X=1)*P(X=1)\] \[P(X\geq 4) =P(X=1)*P(X=1)*P(X=1)*P(X=1)\] \[P(X\geq n) =P(X=1)^{n}=\left(\frac{p_{C}}{1-p_{I}(1-p_{C})}\right)^{n}\]  
If we apply the formula of the probability to the expected value, we obtain  
\[E(X) =\sum_{i=1}^{\infty}\biggl{(}\frac{p_{C}}{1-p_{I}(1-p_{C})} \biggr{)}^{i}=\frac{1}{1-\frac{p_{C}}{1-p_{I}(1-p_{C})}}-1\] \[=\frac{1-p_{I}(1-p_{C})}{1-p_{C}-p_{I}(1-p_{C})}-1=\frac{1-p_{I}+p _{I}p_{C}}{1-p_{I}+p_{I}p_{C}-p_{C}}-1\]  
This previous calculation is for the case of not having a \(FC\) in place. In the case we set up \(FC=n\), we obtain  
\[E(X)=\sum_{i=1}^{n}\biggl{(}p_{C}\frac{1-p_{I}^{n-i}(1-p_{C})^{n-i}}{1-p_{I}(1- p_{C})}\biggr{)}^{i}\]  
Let us use some examples to illustrate the obtained result. For instance, if \(p_{I}=0.5\) and \(p_{C}=0.5\), the expected number of clicks is 2 if we do not configure \(FC\). Instead, for \(FC=10\) and \(FC=17\), the expected numbers of clicks are 1.89 and 1.99, respectively. The results suggests that the value of \(FC\) does not have a big impact on the final number of expected clicks from a user. However, if we use another set up with \(p_{I}=0.95\) and \(p_{C}=0.95\), the expected number of clicks for the case when no \(FC\) is established are 380, whereas for \(FC=10\) and \(FC=50\), this number goes down to only 8.51 and 45.02 clicks, respectively. For the latter set up, \(FC\) has an important impact. In summary, \(FC\) is especially relevant when we use higher \(p_{I}\) and \(p_{C}\) probabilities.
## 3 Results
### Analysis for a Variable Weight of the Satellite
Additional results obtained in STK 12.7.1 include a satellite with a mass of 8 kg and a swath width of 15 km, for which the overpass time was approximately 2.15 s (analytically: 2.44 s), and a satellite with a mass of 18 kg and a swath width of 21 km, for which this time was approximately 3.01 s (analytically: 3.42 s). For all analysed configurations, the differences between analytical calculations and simulation results fell within an acceptable error margin of 10-15%. Slight differences between the results may be caused by the advanced modelling in STK 12.7.1, which takes into account additional orbital perturbations, such as the influence of the flattening of the Earth (the J2 model), drag on Low Earth Orbits (LEOs) and the dynamic adjustment of the satellite's trajectory in real time. For the purposes of analytical calculations, a simplified geometry of the orbit and an ideal field of gravity were assumed, which eliminates the effects discussed above. Moreover, the differences may result from the numerical precision of the simulation, temporal resolution of calculations, and potential rounding up of certain input values, such as swath width or orbital velocity. It is also worth noting that the STK 12.7.1 software operates on a dynamic trajectory of the satellite, while analytical calculations treat the time and satellite positions as points. In conclusion, the comparison of results shows that the values obtained in the numerical and analytical analyses are compatible, with slight differences that result from the fact that STK 12.7.1 takes into account more detailed physical parameters. Detailed analyses and the conclusions that result from the calculations performed are presented and discussed in Section 4--Discussion.
## Material and Methods
### Ebolavirus  
GP encoding plasmids pVR1012-GP(Z) (EBOV Mayinga), pVR1012-GP(R) (RESTV Pennsylvania), pVR1012-GP(S) (SUDV Boniface), and pVR1012-GP(IC) (TAFV Cote d'Ivoire) were provided by Drs. [PERSON] and [PERSON] (NHI, Bethesda, USA). pCAGGS-MARY, pCAGGS-BDBV or pCAGGS-LLOW encoding for MARV Musoke BDBV Bundibugyo and _Cuewavirus_ LLOW were a gift from Drs. [PERSON] (MARV and BDBV) (NIH, Hamilton, USA) and [PERSON] (Research Center for Zoonosis Control, Hokkaido, Japan), respectively. EBOV Makona Kissidougou-C15 and mutant variant A82V GP expression plasmids were a donation of [PERSON] (School of Life Science, University, Nottingham, UK). pCAGGS-SARS-S expressing spike protein S of SARS-CoV strain Frankfurt has previously been described [45]. pCMV-LassaGPC (expressing LASV strain AV GP precursor) was provided by Dr. [PERSON] (INSERM U758, Lyon, France). pMD.RVG.CVS24-B2c encoding for the GP of the rabies virus variant B2c (RABV B2c) was a gift from [PERSON] (Addgene plasmid # 19713; [[http://n2t.net/addgene:19713](http://n2t.net/addgene:19713)]([http://n2t.net/addgene:19713](http://n2t.net/addgene:19713)); RRID:Addgene_19713). pIRES-EGFP-CHIKV E3-E1 was a kind gift of Dr. [PERSON] (Department of Virology, Paul-Elrich-Institut, Langen, Germany). EBOV Mayinga GP1-human Fc fusion protein expression plasmid pAB61-ZEBOV-GP-Fc and pAB61 are published [46].  
Overexpressing vectors for tyrosine Axl (pLV[Exp]-Neo-EF1A>hAXL[NM_021913.4]), Mer (pLV[Exp]-Neo-EF1A>hMETK[NM_006343.2]) or NPC1 (pLV[Exp]-Bsd-EF1A>hNPC1[NM_000271.4]*) were commercially purchased (VectorBuilder Inc, Shenandoah, United States). MLV-based DC-SIGN overexpressing plasmid pQCXIP_hDC-SIGN-AU1 has been described [47]. For construction of pWPI-HAVCR1-BSD vector, the coding sequence of human TIM-1 (hTIM-1) was synthesized (Integrated DNA Technologies) and amplified by PCR using the forward primer 5'CCTGCAGGCGCGCGCGCGCGCACC ATGCATCC AAGTGGTCAT CTTAAGCCCTC ATCCACATC TGGCAGATTC TGTAGCTGTTGTGTAAGG TTGGTG3' and the reverse primer 5'GATATCCGGAGCCGCCTCCTCCTCCTCCTGGCAAAAACTATTC3' and cloned into the lentiviral expression vector pWPI-BSD [44] by Gibson assembly. The hTIM-1 insert was confirmed by sequencing.
# Enhancing Reliability Analysis with Multisource Data:
## 1 Introduction
### Research Questions  
Specifically, this study aims to address the following research questions:  
Figure 1: WIM Systems Including (**a**) Weight Sensors Installed on Roadways and (**b**) a Data Acquisition System.  
In recent years, truck traffic has grown significantly on the interstate and major state highways. Is there an effective way to continuously monitor the probabilities of failure or reliability of a large number of bridges using both evolving Big Data (e.g., vehicular weights) measured in WIM systems and operating bridge load ratings available in the NBI database annually?  
It is hypothesized that bridge components' (e.g., deck, superstructure, and substructure) condition ratings have a significant influence on the bridge's load carrying capacity. To test the hypothesis, the following question is asked: Is the operating maximum load carrying capacity a fair indicator of bridge capacities that change over time based on condition ratings? In theory, it should. However, we ask if the past 10-year data support a positive relationship in an inventory. What are the other attributes in the NBI database that affect the operating load ratings?  
As for monitoring bridges, how does one effectively communicate ever-changing reliability, risk, or probabilities of failure as a result of evolving traffic [or vehicle weights] and technologies on highway bridges with decision-makers and stakeholders (e.g., local governments, transportation agencies, and departments of public safety)?  
Finally, how can we use Reliability Indices (RI) to monitor bridges and mitigate adverse selection problems for MRR decisions involving a large number of bridges in a state's inventory?
# A Comparative Evaluation of Cement and By-Product Petrit T in Soil Stabilization
## 3 Results and Discussion
### Stress-Strain Behaviors  
For two binder types, Figure 13 shows typical stress-strain curves at different binder content and curing times. Figure 13 shows that the soil without any treatment has a 24% failure strain in addition to the low peak stress of 23 kPa. For the treated soil with cement or Petrit T, the general trends are that the peak strength of treated soil increases with an increase in binder content. Whilst, failure strain, corresponding to the peak stress, decrease as the amount of binder is increased. During the UCS tests, small cracks clearly observed on the surface of the samples when the stress reaches the peak point.  
Figure 12: UCS versus water/plastic limit ratio for all tests.  
For the cement treatment, a significant improvement in the stress-strain behaviors was observed during the first 28 days of curing, with small changes at longer curing times. In contrast, for Petrit T, the most important change occured with the long curing period (90 days) (Figure 13b-d). It can be seen that the stress-strain curve for 4% Petrit T improved with a curing time from 14 days (Figure 13a) until it was almost similar to the stress-strain curves of 2% cement at 90 days curing time (see Figure 13d). A similar observation was valid for the addition of 2% of Petrit T compared to 1% of cement. The soil specimens treated with 7% cement content exhibit a more brittle failure compared to the lower cement content and Petrit T specimens, which showed more ductile behaviors (Figure 13d). Thus the failure mode gradually changed from plastic to brittle failure with an increase in the binder content.  
A similar trend for a change in the behavior of the stress-strain curves has been observed by other researchers for various binder types [14, 15, 31, 39].
## 2 Modeling
### Effect of Different Inlet Pressures  
To investigate the effect of inlet pressure on the rotating blade centrifugal jet characteristics, the inlet pressures of 4, 6, 8, 10, and 12 MPa is assumed. The particle diameter is 0.4 mm, and the average erosion rate and particle velocity with inlet pressure under different blade rotation speeds are shown in Figure 7.  
Figure 6: Velocity vector distribution and volume fraction of the water flows.  
Figure 7(a) shows that the erosion rate increases slowly with the increase in inlet pressure. When blade rotation speed is 1470 rpm, the erosion rate at 12 MPa is approximately 1.24 times that at 4 MPa. Figure 7(b) shows the variation trend of abrasive velocity is the same as that of the erosion rate in Figure 7(a). When blade rotation speed is 1470 rpm, the particle velocity at 12 MPa is approximately 1.28 times that at 4 MPa. However, the increase in system pressure causes a sharp increase in the operating cost of the slurry blast device and may reduce its service life and reliability. Therefore, selecting a reasonable pressure is helpful to improving the rust removal of the slurry blast device economically and efficiently.
# Rectangular Microstrip Array Feed Antenna for C-Band Satellite Communications: Preliminary Results
## 4 Conclusions and Future Work  
The fabricated 2 \(\times\) 2 microstrip array antenna has a gain of 12.1 dBi at a frequency of 4.148 GHz. The measured 10-dB bandwidth is 3.8-4.4 GHz. When the 2 \(\times\) 2 microstrip array antenna combines with a 2.4-m parabolic reflector, a gain of 33.1 dBi is achieved, meeting the required antenna specifications for receiving C-band satellite data transmission. Measuring the antenna with a reflector is a challenging task due to the complexity of the antenna.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Substrate** & **Dimension (mm)** & **Frequency** & **Gain** \\ \hline
[29] & Rogers RT 5880 & 17.79 \(\times\) 21.96 \(\times\) 1.6 & 8.864 GHz & 5.09 dB \\ \hline
[30] & RT/Duroid 6002 & 74 \(\times\) 74 \(\times\) 1.524 & 5.86 GHz & 13.34 dB \\ \hline
[31] & FR4 & 80 \(\times\) 80 \(\times\) 1.6 & 5.7 GHz & 6.16 dBi \\  & & & 7.6 GHz & 2.5 dBi \\ \hline Proposed & RT/Duroid 5880 & 120 \(\times\) 85 \(\times\) 3.255 & 4.148 GHz & 12.1 dBi (w/o refl.) \\  & & & 33.1 dBi (w/ refl.) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of microstrip antennas in C-band.  
Figure 10: H-plane radiation pattern of array antenna combined with a 2.4-m parabolic reflector.  
Figure 9: E-plane radiation pattern of array antenna combined with a 2.4-m parabolic reflector.  
combining two antennas and the measurement setup that should comply with the far-field requirement of at least 160 m. This will be conducted as part of our future work.  
Conceptualization, C.A. and B.P.A.M.; methodology, C.A.; software, B.P.A.M.; validation, C.A. and F.H.J.; formal analysis, B.P.A.M. and F.H.J.; writing--original draft preparation, C.A. and B.P.A.M.; writing--review and editing, F.H.J.; visualization, B.P.A.M.; supervision, C.A. and F.H.J.; project administration, C.A.; funding acquisition, C.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Education, Culture, Research, and Technology, the Republic of Indonesia through Penelitian Dasar Unggulan Perguruan Tinggi (PDUPT) Grant, contract number: NKB-847/UN2.RST/HKP:05.00/2022, the year 2022.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 2 System Model and Problem Formulation
### System Model
Using this information, TTG \(k\) can calculate the distance to satellite \(m\), expressed as  
\[d_{k,m}^{t}=\sqrt{(x_{m}^{t}-\xi_{k})^{2}+(y_{m}^{t}-\xi_{k})^{2}+h_{s}^{2}}. \tag{3}\]  
With this distance, the elevation angle \(\theta_{k,m}^{t}\), defined as the angle between the horizontal plane and the line connecting satellite \(m\) and TTG \(k\), can be calculated using  
\[\theta_{k,m}^{t}=\arcsin\left(\frac{h_{s}}{d_{k,m}^{t}}\right). \tag{4}\]  
Subsequently, satellite \(m\)'s remaining cover time for TTG \(k\) can be derived from a geometric relationship denoted as  
\[T_{k,m}^{t}=\frac{R_{E}+h_{s}}{v_{s}}\bigg{(}\arcsin\bigg{(}\frac{R_{E}}{R_{E} +h_{s}}\cos\theta_{\min}\bigg{)}-\arcsin\bigg{(}\frac{R_{E}}{R_{E}+h_{s}}\cos \theta_{k,m}^{t}\bigg{)}\bigg{)}, \tag{5}\]  
where \(R_{E}\) denotes the radius of the Earth, and \(\theta_{min}\) represents the minimum visible elevation angle.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 1 Introduction
### State of the Art
#### 1.1.2 Anomaly Detection Models  
The state of the art provides various anomaly detection models for point, collective and contextual anomalies of uni- and multivariate time series and spatial data. One possibility for clustering such models is presented in [18]. Here, anomaly or novelty detection methods are structured in probabilistic, distance-based, reconstruction-based, domain-based and information theoretic approaches. For a detailed review of anomaly detection methods, refer to [18] or more recently to [19]. Below, only those approaches that are considered in the method evaluation of our publication are presented. Different approaches from the above mentioned classification scheme are compared. From the field of probabilistic models, a kernel density estimator (KDE) based on the values of the time series [20] is used. This model fits a non-parametric probability density function on the data. By calculating the probability that a sample (one step of a time series) belongs to this density and by comparing this value with a threshold, anomalies can be determined. Furthermore, a gaussian process (GP) for one-class classification is used, which works based on a similar principle [21]. From the field of distance based approaches, the local outlier factor (LOF) [22], the isolation forest (IF) [23] and the DBSCAN algorithm [24] are used. LOF is based on determining the density of data points and detects anomalies as data points with few close neighbors. IF is based on multiple tree classifiers for one-class classifcation. DBSCAN is a clustering algorithm that determines anomalies based on their distance to reachable points from cluster core points. Multiple representatives from the reconstruction-based model class are used. An autoregressive (AR) [25] and autoregressive moving average model (ARMA) [26] are applied and compared with a convolutional and a long short-term neural network [27; 28]. All four models are used as regression models between the past time steps of the signals and a time step of the signal in the future. The deviations between these predictions and the actual progress of the signal are then compared with a threshold. If the deviation exceeds the threshold, an anomaly can be assumed. Furthermore, the one class support vector machines (OCSVM) [29] as a domain-based model is included for the comparison. This model builds a domain of inliers based on support vectors and the border data points of this domain. Data points outside this border line are classified as anomalies. As a simplistic baseline model, an approach is considered where a data point is compared to a multiple of the standard deviation of the reference data (abbreviated STD). If this distance exceeds a defined threshold, an anomaly is assumed.