esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Air Pollution Monitoring System Infrastructure
### GUSTO Sensor Unit  
The sensors (including MSN and SSN) we designed within MoDisNet are GUSTO sensor units. GUSTO is an acronym for Generic Ultra violet Sensor Technologies and Observations. It is designed to quantify relative concentrations of a suite of urban air pollutants in real time. The key features of the GUSTO unit are:  
Figure 3: The network architecture of MoDisNet.  
Simultaneous detection of multiple species of pollutants (SO\({}_{2}\), NO\({}_{\text{X}}\), O\({}_{3}\), Benzene and others)  
Real time data collection and transmission (sampling frequency is approximately 1 Hz)  
Relative low unit cost (compared to permanent monitoring sites)  
Robust (self corrects for background changes for each scan)  
Accurate over ambient concentrations (ppb levels)  
GUSTO makes use of the characteristic narrow band absorption of the gas under study (includes SO\({}_{2}\), NO, NO\({}_{2}\), O\({}_{3}\), NH\({}_{3}\), and Benzene) in the UV spectral range 200-300 nm. Retrievals are based on a variation of the well established Beer-Lambert Law, which describes the empirical relationship that relates the absorption of light to the properties of the material through which the light is traveling. Accordingly, the amount of light emerging from a sample is diminished by three physical phenomena:  
1. The amount of absorbing material in its optical path (concentration)  
2. The distance the light must travel through the sample (path length)  
3. The probability that the photon of that particular wavelength will be absorbed by the material (absorptivity or extinction coefficient)  
This relationship can be expressed mathematically and has been covered in several previous works [8].  
A schematic of the GUSTO unit for deployment within the MoDisNet program is presented in Figure 4. When vehicles pass within the vicinity of the sensor, key pollutants (SO\({}_{2}\), NO\({}_{\text{X}}\), O\({}_{3}\), etc) emitted by such vehicles absorb UV light at characteristic frequencies and then detected by the GUSTO sensors as illustrated in Figure 4(a).  
Figure 4: GUSTO sensor unit schematic representation  
Figure 4(b) shows that the sensor unit primarily consists of four main components: (1) the Deuterium Light Source (DLS), (2) UV optics in the form of a Multi-pass White Cell (MWC), (3) a Spectrometer and a Linear CCD unit, and (4) the sensor control unit for data processing and transmission. The sensor is closed path and ambient air is sampled at a frequency of around 1 Hz. The UV light from the DLS passes through the Deuterium and the resulting spectral output is directed along an open optical path via a set of Vertical Transfer Mirror. Then the spectral output imaged onto the surface of the CCD detector. The intensity values are obtained via a 14-bit ADC to produce an atmospheric spectrum of wavelength versus intensity over the GUSTO range. The narrow absorption features are subsequently de-convolved from the atmospheric spectrum and the resulting differentials are used to calculate the concentration of each absorber.
# Reducing Video Coding Complexity Based on CNN-CBAM in HEVC
## 2 The Proposed Methods
### Proposed Algorithm
#### 2.2.3 Proposed CNN-CBAM Algorithm  
As above-mentioned, to increase the link between CUs of various sizes and the convolution structure, the size of the convolution kernel in \(C_{i-1}\)\((i=1,\ 2,\ 3)\) is half of the current CU size. The feature map obtained from the convolution is a global description of the current CU information, which is critical in the CU partition of the output layer. However, when the CNN performs feature mapping, the weights assigned to each channel are the same that result in the poor description of the key information. In light of this, as shown in Figure 5, we introduced CBAM in the CNN structure to better characterize key features of the image by adaptively assigning the weights within the channels according to the content of the image and focusing on important regions of the image content.
## 5 Simulation Results
### Simulations of Distance vs Energy Consumption  
Figures 8 and 9 show the relationship between transmission distance and consumed energy in shallow water and deep sea. Both figures show that as the internode distance increases, the consumed energy increases as well. We also observe from two figures, as the frequency increases, the consumed energy also increases.  
For shallow water, as shown in Figure 8, the depth of water impacts the energy consumption: the deeper the water, the higher the energy consumption. It is also shown that with same transmission distance, the energy consumption in shallow water is larger than in deep water. However, the overall figures are the same, except the quantity. In Sections 5.2. and 5.3., we only simulate energy consumption in shallow water.  
Figure 8: Energy dissipation vs sensor nodes in shallow water.  
Figure 9: Energy dissipation vs sensor nodes in deep sea.
## 5 \(p\)-Value Computation
### Private Table Lookup  
We first assume that each party \(P_{i}\) has a share of the secret index \(k_{i}\) such that the secret index \(k=\sum_{i}^{N}k_{i}\bmod\frac{\mathcal{N}}{2}\) where there are \(N\) parties and \(\mathcal{N}\) is the ring dimension of the MHE scheme. \(P_{0}\) additionally has the lookup table \(L=[\ell_{1},\ell_{2},\ldots,\ell_{\frac{\mathcal{N}}{2}}]\) such that the parties want to compute \(\ell_{k}\) securely.  
Firstly, \(P_{0}\) rotates the table by \(k_{0}\) rotations and encrypts it and sends this ciphertext to Party 1. Each subsequent party \(P_{i+1}\) then rotates the ciphertext received from \(P_{i}\) further by \(k_{i+1}\). The last party, \(P_{N-1}\), sends the resulting ciphertext to \(P_{0}\), which multiplies the ciphertext by the encoding of the vector \([1,0,\ldots,0]\) before all parties collectively decrypt the ciphertext. The result will be the vector \([\ell_{k},0,\ldots,0]\) since rotations in MHE happen modulo \(\frac{\mathcal{N}}{2}\). Furthermore, as all but the resulting lookup value is masked in the final calculation and the index of lookup value is not leaked in any way, we can conclude that this protocol is indeed secure. Crucially, we notice that for lookup tables of size up to \(\frac{\mathcal{N}}{2}\) (which in practice is usually quite large--16,384), the number of rounds of this protocol is constant compared to equivalent SMPC protocols where the number of rounds would depend on the size of the lookup table. Further analysis of the communication efficiency and runtime can be found in Section 6.4.
## 1 Introduction
Figure 1: (**a**) The architecture of the 1-D CNN model; (**b**) the architecture of the GRU model; (**c**) the architecture of the LSTM model; (**d**) the architecture of the RNN model. * The different layers of the proposed model are configured for improved accuracy and effectiveness and are examined by an Adam optimizer. * The efficiency of the proposed model is compared with a conventional system, the LSTM model implemented in our experiment, TCN [41], and (Demod)-CNN-based RIS model [40], with respect to the different SNR values where the proposed model outperforms other methods at different SNRs with the measurement error of BER and SER. The remaining parts of the paper are arranged as follows. The system model for RIS-based communication, which includes the channel model and signal transmission, is described in Section 2. The training data preparation, training, and inference of the proposed deep learning model are covered in Section 2.4. The simulation outcomes and complexity analysis are reported in Section 3, and Section 4 presents the conclusion. _Notations_: A vector and matrix are denoted by the lowercase and uppercase bold letters \(\mathbf{h}\) and \(\mathbf{H}\), respectively. \(\mathbf{H}^{H}\) stands for the conjugate transpose matrix of \(\mathbf{H}\), and \(\text{diag}(\mathbf{x})\) stands for the diagonal matrix with vector \(\mathbf{x}\) on its diagonal.
## 3 Results and Discussion
### Water Absorption Karsten Test  
Figure 3a,b show the evolutions of the amounts of absorbed water during the Karsten tests. Mean values corresponding to the three specimens tested for each concrete type along with the corresponding error bars are shown. As a representative parameter of the behavior of the concretes under study it has been chosen the so-called water absorption coefficient (\(WAC\)) [22], expressed here in the units of \(\left(\frac{\text{kg}}{\text{m}^{2}\cdot\text{min}}\right)\), and calculated as the mean absorption rate during the first 150 min of the test [32]. Table 5 shows the values of this mean absorption rate (\(WAC_{150}\)) for both concretes. It is apparent from Figure 3a that the water absorption behavior of both concretes is practically equal at 14 days after the end of the curing period. On the other hand, at 105 days after the end of the curing period, the concrete C* shows a lower water absorption rate, as compared to C (Figure 3b), which is quantified as a reduction in \(WAC_{150}\) of 23%, see Table 5. These results can be compared to those obtained in previous researches [15], where it was shown that the incorporation of different CA materials to Portland cement concretes lead to a significant reduction in the water sortivity, which can be 50% lower than that of the control mix. The reductions were dependent on the (\(w/c\)) ratio, being higher for a high \(w/c\) value of 0.60 than for low \(w/c\) values of about 0.39 and on the length of the curing period, being higher the longer was the curing [15]. The quantitative discrepancy of results of this work to previous results can be understood by considering the different curing procedures: humid chamber (no permanent contact with liquid water) in this work, and permanent water curing in the previous work [15]. This points again to a possible important effect of the curing and service conditions of concrete on the efficacy of crystalline admixtures in reducing the permeability of concrete, see Section 3.3.  
Figure 3: Evolutions of the mean amounts of absorbed water during the Karsten tests at 14 days (**a**) and at 105 days (**b**) after the end of the curing period.  
Figure 2: Evolution of the bulk electrical resistivity \(\rho\) of saturated concrete specimens. The specimens were kept permanently immersed in tap water after the curing period.
###### Abstract  
A series of calculations has been conducted to study the high-speed interaction of space debris (SD) particles with screens of finite thickness. For the first time, taking into account the fracture effects, a numerical solution has been obtained for the problem of high-velocity interaction between SD particles and a volumetrically reinforced penetrating composite screen. The calculations were performed using the REACTOR 3D software package in a three-dimensional setup. To calibrate the material properties of homogeneous screens made of aluminum alloy A356, stainless steel 316L, and multilayer screens, methodical load calculations were carried out. The properties of materials have been verified based on experimental data through systematic calculations of the load on homogeneous screens made of aluminum alloy A356, stainless steel 316L, and multilayer screens comprising a combination of aluminum and steel plates. Several options for the numerical design of heterogeneous screens based on A356 and 316L were considered, including interpenetrating reinforcement with steel inclusions and a gradient distribution of steel throughout the thickness of an aluminum matrix. The study has revealed that the screens constructed as a two-layer composite of A356/316L, volumetrically reinforced composite screens, and heterogeneous screens with a direct gradient distribution of steel in the aluminum matrix provide protection for devices from both a single SD particle and streams of SD particles moving at speeds of up to 6 km/s. SD particles were modeled as spherical particles with a diameter of 1.9 mm made of the aluminum alloy Al2017-T4 with a mass of 10 mg.  
heterogeneous material; reinforcement; high-speed interaction; barrier of finite thickness; space debris +
Footnote †: journal: Applied Sciences
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
###### Abstract  
Thin-walled tubes are a kind of pressure vessel formed by a stamping and drawing process, which must withstand a great deal of sudden pressure during use. When microcrack defects of a certain depth are present on its inner and outer surfaces, severe safety accidents may occur, such as cracking and crushing. Therefore, it is necessary to carry out nondestructive testing of thin-walled tubes in the production process to eliminate the potential safety hazards. To realize the rapid detection of microcracks in thin-walled tubes, this study could be summarized as follows: (i) Because the diameters of the thin-walled tubes were much larger than their thicknesses, Lamb wave characteristics of plates with equal thicknesses were used to approximate the dispersion characteristics of thin-walled tubes. (ii) To study the dispersion characteristics of Lamb waves in thin plates, the detection method of the \(A_{0}\) mode was determined using the particle displacement-amplitude curve. (iii) Using a multi-channel parallel detection method, rapid detection equipment for Lamb wave microcracks in thin-walled tubes was developed. (iv) The filtering peak values for defect signal detection with different depths showed that the defect detection peak values could reflect the defect depth information. (v) According to the minimum defect standard of a 0.045-mm depth, 100,000 thin-walled tubes were tested. The results showed that the missed detection rate was 0%, the reject rate was 0.3%, and the detection speed was 5.8 s/piece, which fully meets the actual detection requirements of production lines. Therefore, this study not only solved the practical issues for the rapid detection of microcracks in thin-walled tubes but also provided a reference for the application of ultrasonic technology for the detection of other components.  
Lamb wave; microcracks testing; multi-channel detection; signal-to-noise ratio; thin-walled tube +
Footnote †: journal: Applied Physics Letters
# Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks
## 4 Discussion  
Besides the classification accuracies, another important performance parameter is the runtime. Table 3 shows the training time of the proposed method for the three datasets. It can be seen clearly that the training of M-CSVM is fast and needs just few seconds to few minutes in the worse case. In details, dataset 1 presents the highest runtime (76 s) which is due to the high number of filters used for this dataset (512), while training of dataset 3 is much faster (just 8 s) due to the simplicity of the related network (see Table 1).  
Regarding runtime at the prediction phase, which includes the feature extraction and classification, the M-CSVM method presents different runtime for the three datasets depending on the complexity of the adopted architecture. For instance, as we can see in Table 4, the highest runtime is with the first dataset 1 with 0.200 s per image, which is due to the high number of filters adopted for it (512). In contrast, the third dataset requires only 0.002 s to extract features and estimate the classes for each image. This short time is due to the small number of SVMs used in this network for this dataset. It is also important to mention that the runtime provided by our method outperforms the three pre-trained CNNs on two datasets (datasets 2 and 3), especially for the dataset 3 where the difference is significant. Except for dataset 1, where GoogLeNet is slightly faster due to complexity of the related network.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{(a)} \\ \hline
**Method** & **Runtime (s)** \\ \hline ResNet & 0.207 \\ GoogLeNet & 0.141 \\ VGG16 & 0.291 \\ Ours & 0.206 \\ \hline \multicolumn{2}{c}{(b)} \\ \hline
**Method** & **Runtime (s)** \\ \hline ResNet & 0.208 \\ GoogLeNet & 0.144 \\ VGG16 & 0.291 \\ Ours & 0.115 \\ \hline \multicolumn{2}{c}{(c)} \\ \hline
**Method** & **Runtime (s)** \\ \hline ResNet & 0.207 \\ GoogLeNet & 0.145 \\ VGG16 & 0.300 \\ Ours & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of average runtime per image for: (a) dataset 1, (b) dataset 2, and (c) dataset 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Runtime (s)** \\ \hline Dataset 1 & 76 \\ Dataset 2 & 42 \\ Dataset 3 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training time of the proposed M-CSVM.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 3 Matrix Applications in Signal Estimation Theory
### Matrix Inverse Lemma for Derivation of the Recursive Least-Squares Filter  
In many signal processing applications, the data is generated by sampling a continuous-time signal. In this way, more data becomes available as the time progresses. One can either wait for all the available data or process the data iteratively in time. The latter approach requires a recursive (online) approach to update the filter taps. Next, the key role played by the matrix inverse lemma (Woodbury matrix identity) in deriving the least-squares (RLS) filter will be presented. The matrix inverse lemma states that [12]:  
\[(\mathbf{A}+\mathbf{x}\mathbf{y}^{H})^{-1}=\mathbf{A}^{-1}-\frac{\mathbf{A}^ {-1}\mathbf{x}\mathbf{y}^{H}\mathbf{A}^{-1}}{1+\mathbf{y}^{H}\mathbf{A}^{-1} \mathbf{x}}, \tag{19}\]  
where matrix \(\mathbf{A}\in\mathbb{R}^{N\times N}\) is assumed invertible, and \(\mathbf{x},\mathbf{y}\in\mathbb{R}^{N}\). Consider now the linear time-invariant (LTI) system shown in Figure 4. The problem is to adapt the \(m\)-tap FIR filter coefficients \(\mathbf{h}\) so that its output \(y(n)\) follows the reference signal \(x(n)\). The received signal \(y(n)\) is expressed as:  
\[y(n)=\sum_{i=0}^{m-1}h_{n}(i)a(n-i)=\mathbf{h}_{n}^{H}\mathbf{a}_{n}, \tag{20}\]  
where \(\mathbf{a}_{n}=[a(n),a(n-1),\cdots,a(n-m+1)]^{H}\) is the vector containing the \(m\) most recent samples of input signal \(a(n)\). Our goal is to estimate the taps of filter \(\mathbf{h}\) at each time slot \(n\) using a least-squares estimation approach. Specifically, the problem resumes to:  
\[\hat{\mathbf{h}}_{n}=\arg\min_{\mathbf{h}_{n}}||\mathbf{A}_{n}\mathbf{h}_{n} -\mathbf{x}_{n}||_{2}^{2},\ n=1,2,\cdots,N, \tag{21}\]  
where  
\[\mathbf{A}_{n}=\left[ \begin{array}{cccc}a(1)&a(0)&\cdots&a(-m+2)\\ a(2)&a(1)&\cdots&a(-m+1)\\ \vdots&\vdots&\ddots&\vdots\\ a(n-1)&a(n-2)&\cdots&a(n-m)\\ a(n)&a(n-1)&\cdots&a(n-m+1)\end{array} \right]=\left[ \begin{array}{c}\mathbf{a}_ {n}^{H}\\ \mathbf{a}_{2}^{H}\\ \vdots\\ \mathbf{a}_{n-1}^{H}\\ \mathbf{a}_{n}^{H}\end{array} \right]=\left[ \begin{array}{c}\mathbf{A}_{n-1 }\\ \mathbf{a}_{n}^{H}\\ \mathbf{a}_{n}^{H}\end{array} \right],\]  
and\[\mathbf{h}_{n}=\left[ \begin{array}{c}h_{n}(0)\\ h_{n}(1)\\ \vdots\\ h_{n}(m-2)\\ h_{n}(m-1)\end{array} \right]\text{and}\ \mathbf{x}_{n}=\left[ \begin{array}{c}x(1)\\ x(2)\\ \vdots\\ x(n-1)\\ x(n)\end{array} \right]=\left[ \begin{array}{c}\mathbf{x}_{n-1}\\ x(n)\end{array} \right].\]  
As time evolves the number of rows of matrix \(\mathbf{A}_{n}\) keeps increasing, and to avoid repeatedly calculating from scratch the least-squares estimate of \(\hat{\mathbf{h}}_{n}\) for each instant of time \(n\), expressing \(\hat{\mathbf{h}}_{n}\) in terms of \(\hat{\mathbf{h}}_{n-1}\) helps to reduce the computational burden. Indeed, such an approach exhibits the huge benefit of reducing the computational complexity incurred by evaluating the taps of the filter at each time instant.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 3 Materials and Methods
### Network Architecture  
Figure 3 shows the network architecture in this paper. Inspired by U-Net [39] and Feature Pyramid [40], we designed a multi-input network based on an encoder-decoder structure. It takes multi-spectral remote sensing images of two resolutions as the input and outputs the corresponding haze-free image.
# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 4 Experiments and Analysis
### Ablation Study  
It is crucial to discuss the influence of each neural network block on the dehazing quality; therefore, we trained six different models to observe the performance of these blocks. Table 4 lists the details of the six models.  
Table 5 shows the dehazing quality metrics of the six models and their corresponding numbers of trainable parameters. From model 2 and model 3, the inception-like block and the pyramid pooling improve the dehazing quality. Model 4 shows that an attention gate can increase _SSIM_ and _PSNR_ without adding too many trainable parameters, and model 5 uses the image preprocessing block without adding trainable parameters. Model  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Blocks** & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 5** & **Model 6** \\  & & & & & **(Proposed)** \\ \hline Image pre-processing block & - & - & - & O & O \\ Inception-like block & - & O & O & O & O \\ Pyramid pooling block & - & - & O & O & O \\ Original attention gate & - & - & - & O & - \\ Proposed attention gate & - & - & - & - & O \\ \hline \hline \end{tabular}
\end{table}
Table 4: Description of ablation analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Trainable Parameters** & **Model Size** \\ \hline DehazeNet [9] & 8.305 K & 0.035 MB \\ AOD-Net [10] & 1.76 K & 0.008 MB \\ ReViewNet [28] & 399.7 K & 5 MB \\ FFA-Net [12] & 455.9 K & 21.3 MB \\ ThehaNet [20] & 157.9 K & 1.98 MB \\ Proposed Method & 207.3 K & 0.86 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of trainable parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Training Dataset** & **Testing Dataset** \\ \hline
**Categories** & **Indoor** & **Outdoor** & **Indoor** & **Outdoor** \\ \hline RESIDE ITS & 13,990 & - & - & - \\ RESIDE OTS & - & 72,135 & - & - \\ RESIDE SOTS & - & - & 500 & 500 \\ RESIDE HSTS & - & - & - & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of the dataset.  
6 uses the proposed attention gate instead of the original attention gate, resulting the best dehazing quality among these approaches.
## 3 Hybrid Protection Mechanism for SFC
### Traffic Detouring in the Case of Failure
SFF4 flow entry to match the destination IP of the packet and forward the packet to SW4, as there is no remaining label in the stack. The following are the changes in the flow table configurations that are required after the failure to detour the traffic to the backup path:  
1. The action field of the SFF1 flow entry that matches label \(l_{12}\) is updated to forward the packets to SW2;
2. The SW1 flow entry that adds the MPLS header is updated with the new label stack (\(L_{1}^{\prime}\)) where \(l_{12}\) is replaced by \(l_{12}^{\prime}\). The backup path configuration and traffic detouring mechanisms of the proposed HP-SFC are limited to single-level failures. This means that HP-SFC can recover network traffic from single or multiple failures in the primary SFC path, but is unable to handle second- or third-level failures. Failures in the backup path or in the backup path of the backup path are defined as second- or third-level failures, respectively [5]. This implies that HP-SFC operates under the assumption that configured backup paths and backup SFs are always available, and their failure impedes HP-SFC operation and disrupts SFC. Making HP-SFC robust against second- and third-level failures is a separate study that requires multi-level SFC segmentation and labeling methods. Hence, the performance evaluation of HP-SFC in the subsequent section was performed for single-level failures.