esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 3 Health Monitoring in Corrosion
### Conventional Corrosion Sensors
#### 3.1.5 Magnetic Flux Leakage Method  
This is yet another marvelous method that does not severely damage the system for monitoring; instead, it uses the magnetic properties of the substance. It can detect a change in flux. The flux changes simply because the flux line always exist in ferromagnetic substances. Normally, the undeformed structures will have a simple continuous and will produce magnetic lines in a fixed direction. However, when a defect is encountered, it surely would have caused some misalignment of the magnetic flux lines due to the deformation. This magnetic field change can be used to interpret a variety of information about the damage (Figure 5). The only problem that it faces is the differentiation in location of the damage and the inability of the confirmation of a reason for the damage. In addition, it can only detect damage; it cannot predict it [82, 83].  
Figure 3: Variations of the electrochemical sensors. Reproduced with permission from [77] Copyright 2019 AIP Publishing.
## 3 Results
### The Hand Feel Panel Assessment in a Comparison with Consumer Test  
A different set of knitted fabrics made of different cellulosic fibers in two yarn counts (Nm 50 and 70) was evaluated by the hand feel panel in the same scaling method. Ten fabrics were scaled in two successive sessions, relative to the fixed reference fabrics R1k and R2k.  
Five fabrics were selected to be evaluated at a consumer test. The fabrics were evaluated by 42 participants at a commercial textile fair (Premier Vision, Paris, 2019) at the Lenzing booth. The participant was asked to select the \"softest\" fabric, and if possible, to provide scaling of the fabrics regarding softness. Unlike the hand-feel panel, the participants scaled the fabrics without external reference fabrics. The results are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Fiber Content**} & \multirow{2}{*}{**Yam Nm**} & **Hand Feel Panel** & \multirow{2}{*}{\
\begin{tabular}{c} **Consumer** \\ **Scaling (\(n=42\))** \\ \end{tabular} } & **Hand Feel** & \multirow{2}{*}{
\begin{tabular}{c} **Consumer** \\ **Panel** \\ **Ranking** \\ \end{tabular} } \\ \hline
**A** & 100\% Cotton & 70 & 4.8 & 3.4 & 5 & 5 \\ \hline
**B** & 100\% Modal & 50 & 8,5 & 5.8 & 3 & 4 \\ \hline
**C** & 100\% Modal (micro) & 50 & 10 & 7.7 & 1 & 2 \\ \hline
**D** & 100\% Lyocell & 70 & 7.9 & 6 & 4 & 3 \\ \hline
**E** & 100\% Lyocell (micro) & 70 & 10 & 8,4 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fabric description and evaluation results.  
While the hand feel panel ranked fabrics # E and # C equally, a vast majority chose fabric # E as the \"softest\" fabric, and fabric # C came in second place. Forty-two participants were able to provide a scaled comparison of the fabrics, ranking the fabrics on a given scale between 1 (roughest) and 10 (softest), according to softness preference. In this test, the fabrics were compared with each other without an external reference fabric. A high variation was observed. Comparing the average consumer results with the hand feel panel scale shows acceptable accordance in categorizing the fabrics. The hand feel panel and the consumer ranking are concordant with the softest and least soft fabrics. Some discordance occurs in the two middle-range fabrics, whereas the score difference between the fabrics is low on both sides.  
Although scores on a scale of 1 to 10 (their mean values, see Figure 7) are different between both groups, the standard deviations in all measurements are overlapping, so the data can be considered to be consistent, and the hand feel panel's view is to be taken into account in determining customer preferences.
# Design of Adaptive Kalman Consensus Filters (a-KCF)
## 2 Problem Statement  
Consider a continuous-time dynamic system that has the following form:  
\[\dot{x}(t)=Ax(t)+Bw(t);\quad x(0)\in\mathcal{N}(x_{0},P_{0}) \tag{1}\]  
with \(m\) states measured by \(n\) distributed agents via sensing devices and local filters. Here, \(x(t)\in\mathbb{R}^{m}\) denotes the states of this dynamic system, \(A^{m\times m}\) represents the dynamical matrix, \(w(t)\) represents the white Gaussian noise of the system, which is distributed by the matrix \(B\) with zero mean and covariance \(Q(t)=E[w(t)w(t)^{T}]\), \(x_{0}\) is the initial guess for the states of the dynamic system with error covariance \(P_{0}\), and the sensing capability of each agent is determined by the following equation:  
\[y_{i}(t)=C_{i}x(t)+v_{i}(t);\quad y_{i}\in\mathbb{R}^{\ell},\ell\leq m \tag{2}\]  
with the measurement matrix \(C_{i}^{\ell\times m}\). The sensing devices are not able to measure all the states of the system; thus, only partial information is available to the local filters for the state estimation. Here, \(v_{i}(t)\) represents the white Gaussian noise of measurement for the \(i\)th agent, with zero mean and covariance matrix \(R_{ij}(t)=E[v_{i}(t)v_{j}(t)^{T}]\). Throughout this paper, we assume that there is no noise coupling effect among the agents; therefore, we can set both \(Q(t)\) and \(R_{ij}(t)\) as diagonal matrices. The _main purpose_ of this paper is to design an adaptive state estimation structure for each agent such that the state estimation of individuals can be exchanged among their immediate neighbors through the communication topology \(\mathcal{G}(\mathcal{V},\mathcal{E},\mathcal{A})\).
## 2 Background Theories
### Performance Metrics  
In this research, the performance metrics are separated into two main parts. The first part evaluates the performance of the financial news sentiment analysis model and the second part evaluates the performance of the stock price prediction model.  
In the first part, confusion metrics are used to validate the financial news sentiment analysis model, in order to compare performance with other models using precision, recall, \(F1\)-score and accuracy from (5)-(8), respectively where \(TP\) denotes true positive, \(TN\) denote true negatives, \(FP\) denotes false positives, \(FN\) denotes false negatives and \(n\) denotes the number of observations.  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\[F1-score=\frac{2*(Precision*Recall)}{(Precision+Recall)} \tag{7}\]  
\[Accuracy=\frac{TP+TN}{n} \tag{8}\]  
In the second part, to evaluate the performance of the stock price prediction model, the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), MeanAbsolute Error (MAE) and coefficient of determination (\(R^{2}\)) statistics are used to compare performance with the other models (9)-(11), respectively, where \(y_{i}\) denotes actual value, \(\hat{y_{i}}\) denotes predicted value and \(\overline{y}_{i}\) denotes the mean of \(y\) value.  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(\hat{y_{i}}-y_{i}\right)^{2}}{n}} \tag{9}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{|y_{i}-\hat{y_{i}}|}{y_{i}}\times 100 \tag{10}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y_{i}}| \tag{11}\]  
\[R^{2}=1-\frac{\sum(\hat{y_{i}}-\overline{y}_{i})}{\sum(y_{i}-\overline{y}_{i})} \tag{12}\]
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 2 Algorithm for the Automatic Extraction of the Reflection Period Based on Image Processing
### Step 1: Wavelet Denoising  
The multi-resolution wavelet analysis decomposes the signal into different scales and splits the mixed signal into sub-signals of different frequency bands. The decomposition and reconstruction of the wavelet transform are used for signal denoising: decomposing the noise-containing signal into different frequency bands at a certain scale, and then the high-frequency band in which the noise is located is eliminated. The low-frequency coefficients are used for signal reconstruction. The wavelet basis selected for wavelet denoising is Daubechies. In order to give consideration to the calculation amount and the denoising effect, the number of decomposition layers was set to 6. This number  
Figure 1: Algorithm illustration.  
was determined by experiments, and the high frequency coefficient of the 6 th layer component was removed during the reconstruction.
# Wavelet Time-Frequency Analysis on Bridge Resonance in Train-Track-Bridge Interactive System
## 3 Time-Frequency Interaction Analysis on the Train-Track-Bridge System
### Division of the Band from Resonant to Nonresonant Bridge Condition  
In order to study the preconditions of resonance and accurately describe the time-frequency evolutionary characteristics of resonant, nonresonant and resonantly transitional bridge response, several speed conditions were firstly selected within the range of 90-720 km/h, which helps to determine the mid-span bridge vibratory acceleration signal energy calculated by Equation (5) in the function of train speed shown in Figure 6.  
Secondly, the bridge natural frequency is 6.5 Hz according to Table 2. The theoretical resonant speeds (195, 292.5 and 585 km/h) were determined with the arrow in Figure 6 according to first-order loading frequencies (train speed/vehicle length, namely \(V/lv\); 2.17, 3.25 and 6.5 Hz) which is equal to \(1/n\) (\(n\) is integer) time of the bridge natural vibration frequency. It can be seen in the Figure that the component of 585 km/h dominates the bridge acceleration signal energy, which reflects that the bridge vibrates the most intensely when the first-order loading frequency is identical to the bridge natural frequency.  
Thirdly, the speed ranges in blue are defined as the resonantly transitional band through the gradient of signal energy. Their limits are marked according to the relative percentage to the theoretical resonant speeds. The reason why the resonantly transitional band is not allocated to the theoretical speed of 292.5 km/h is because there is no significant fluctuation in bridge vibratory acceleration signal energy around this speed.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
###### Abstract
Section 2 describes the laser crosslink mission in detail, including the requirements and concept of operations. Section 3 covers the nanosatellite system design specifications for the laser crosslink, as well as the optical link budget analysis for each scenario. Sections 4 and 5 describe the design and analysis of payload and bus systems, respectively. Finally, Section 6 summarizes the study and presents concluding remarks.
# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 4 The Proposed Method
### Deep Representations for the Fingerprints
```
0:\(N\) packet receptions for each of the \(q\) training locations of the \((k+1)\)th set of data;
0:\(q\) groups of fingerprints corresponding to \(q\) locations of the \((k+1)\)th set of data;
1:Initialization:
1:for\(i=1;i<=q\)do
2: //Make the input data \(X^{0}\) as the 0 th hidden layer;
3: Set \(H^{0}=X^{0}\);
4: Randomly initialize \(w,b\), where \(w^{\text{T}}w=1\) and \(\text{b}^{\text{T}}\text{b}=1\);
5: //\(p\) is the number of hidden layers;
6:for\(l=1;l<=p\)do
7:\(H_{0}=g(wH_{0}^{l-1}+b)\);
8:\(P_{0}=\big{(}H_{0}^{l}H_{0}\big{)}^{-1}\);
9:\(\beta_{l}^{0}=P_{0}(H_{0})^{T}(H_{0}^{l-1})\);
10:\(H_{0}^{l}=g\Big{(}\big{(}\beta_{l}^{0}\big{)}^{T}H_{0}^{l-1}\Big{)},l=1,2, \ldots,p\);
11:endfor
12: //Obtain \(Fingerprint_{i}\);
13:\(Fingerprint_{i}\leftarrow[\beta_{1}^{0},\ldots,\beta_{p}^{0}]\);
14:endfor
15: Sequential Learning:
16://\(K\) is the number of data sets;
17:for\(k=0;k<=K\)do
18:for\(i=1;i<=q\)do
19: //Make the input data \(X^{0}\) as the 0 th hidden layer;
20: Set \(H^{k}=X^{k}\);
21: //\(p\) is the number of hidden layers;
22:for\(l=1;l<=p\)do
23:\(H_{k}=g(wH_{k}^{l-1}+b)\);
24:\(P_{k}=\big{(}H_{k}^{T}H_{k}\big{)}^{-1}\);
25:\(\beta_{l}^{k}=P_{k}(H_{k})^{T}(H_{k}^{l-1})\);
26:\(H_{k}^{l}=g\bigg{(}\big{(}\beta_{l}^{k}\big{)}^{T}H_{k}^{l-1}\bigg{)},l=1,2, \ldots,p\);
27:endfor
28: //Obtain \(Fingerprint_{i}\);
29:\(Fingerprint_{i}\leftarrow[\beta_{1}^{k},\ldots,\beta_{p}^{k}]\);
30:endfor
31:endfor
```  
**Algorithm 1** Weight-Based Fingerprint Representations
# Occurrence of Fungi on Duck Egg Shells and Drug Resistance Analysis of _Aspergillus_ spp. Isolates
## 3 Results
### Aspergillus fumigatus Strains Antifungal Resistance Profiles  
The MIC values for _A. fumigatus_ isolates ranged from 0.094-32 \(\upmu\)g/mL for amphotericin B, 0.125-32 \(\upmu\)g/mL for caspfungin, 0.19-32 \(\upmu\)g/mL for itraconazole, 0.047-12 \(\upmu\)g/mL for posaconazole and 0.023-32 \(\upmu\)g/mL for voriconazole (Table 2).  
According to EUCAST guidelines, an _A. fumigatus_ isolate is considered resistant when the MIC exceeds 1 \(\upmu\)g/mL for AMB, ITC and VOR or 0.25 \(\upmu\)g/mL for POS and \(\geq\)0.5 \(\upmu\)g/mL for CAS [15; 16].  
In accordance with the EUCAST guidelines on fungal drug resistance, high in vitro resistance to ITC (68.4%) and POS (73.7%) was observed. Among azoles, the lowest resistance to VOR (31.6%) was noted. Nearly half of the strains (47.4%) showed resistance to AMB. Thirteen isolates were resistant to more than one antifungal drug, and only two isolates were susceptible to all examined antifungal agents [Table 3].  
\begin{table}
\begin{tabular}{c c} \hline
**Fungus Species** & **Homologous Sequences** \\ \hline _Aspergillus fumigatus_ & KY522968.1; MN58803; MN178807 \\ \hline _Alternaria alternata_ & MN894079.1; KY099683.1 \\ \hline _Alternaria tenuissima_ & MT078698.1; MW720805.1 \\ \hline _Lichttheinia ramosa_ & FJ719386.1; MF919355.1 \\ \hline _Penicillium chrysogenum_ & PP952058; MF363161.1 \\ \hline _Penicillium grisefolvulum_ & KJ467353.1; KU561903.1 \\ \hline \end{tabular}
\end{table}
Table 2: The fungal species confirmed using PCR with homologous sequences from GenBank.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Isolate Number** & **AMB** & **CAS** & **ITC** & **POS** & **VOR** \\ \hline C1 & **3.0** & 0.25 & **3.0** & **0.38** & 0.19 \\ C2 & 0.5 & 0.032 & **3.0** & **0.5** & 0.125 \\ C3 & **4.0** & 0.38 & **3.0** & **1.5** & **>32** \\ C4 & **4.0** & 0.38 & **1.5** & **0.75** & 0.094 \\ C5 & **2.0** & 0.19 & **>32** & **8.0** & **>32** \\ C6 & 0.094 & 0.125 & 0.19 & 0.047 & 0.064 \\ C7 & **2.0** & 0.38 & **3.0** & **0.5** & 0.125 \\ C8 & **2.0** & 0.25 & **12** & **8.0** & **>32** \\ C9 & 0.75 & 0.38 & **2.0** & **0.38** & 0.19 \\ \hline \end{tabular}
\end{table}
Table 3: Resistance of _Aspergillus fumigatus_ isolates from eggshells to individual antifungals (MIC).  
The MIC 50 and MIC 90 values of AMB were 1 \(\mathrm{\mu g}\)/mL and 32 \(\mathrm{\mu g}\)/mL, respectively. The MIC 50 value for ITC was 1.5 \(\mathrm{\mu g}\)/mL, and the MIC 90 value was 32 \(\mathrm{\mu g}\)/mL. POS had MIC 50 and MIC 90 values of 0.5 \(\mathrm{\mu g}\)/mL and 8 \(\mathrm{\mu g}\)/mL, and VOR 0.19 \(\mathrm{\mu g}\)/mL and 32 \(\mathrm{\mu g}\)/mL, respectively [Table 4].  
The _A. fumigatus_ isolates showed a specific resistance profile to CAS. The MIC 50 and MIC 90 values of CAS were 0.38 \(\mathrm{\mu g}\)/mL and 32 \(\mathrm{\mu g}\)/mL, and those values group the strains into two: susceptible strains and highly resistant ones (without any ellipse around the MIC test strip).
## 3 Materials and Methods
### Field Trial
The data of each pair are published on the same \"content instance\" of the TIM ICON platform. To explain this system, let us refer, as an example, to Figure 19, which shows the container with the pair of CTDs \"1 Sc\" and \"1 Dx\", and the ICON endpoint for both is the same (i.e., /onem2m/sensor_sea/393315537896/inbox):  
Figure 10:0 Location of containers with CTDs (in the red box) as seen from the bridge deck. Figure 10:1 Diagram showing the placement of CTDs on the ship. The containers at the top (fifth pull) are without CTD because they will have to be unloaded before the final destination of the trip (Mombasa). CTDs are placed on the lower containers at the numbered boxes in the matrix drawn on the photograph. * In the Terrestrial and Near-Sea scenario, the CTD \"1 Sx\" will send data via a commercial TIM NB-IoT network to/onem2m/sensor_sea/393315537896/inbox, while no NB-IoT data arrive from the CTD \"1 Dx\" because the Azcom cell on the ship is turned off. * In the Open Sea scenario, the CTD \"1 Dx\" will send data via the onboard NB-IoT network to/onem2m/sensor_sea/393315537896/inbox, while no data arrive from the CTD \"1 Sx\" because the ship's location is not covered by land-based TIM cells. It must be emphasized that, since the 5G band for NB-IoT B28 (700 MHz) is not yet authorized for mobile communication services onboard vessels (MCV services) [36], to perform the field trial, a specific temporary authorization was requested and granted by the Ministry of Enterprises and made in Italy by the Italian Government. According to this, the field trial was authorized to use the B28 in national waters and open sea (beyond 41 miles from the coast).
# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
## 4 Discussion  
### Dataset and Experimental Environment  
The helmet-wearing detection network proposed in this paper combines SafetyHelmet Wearing-Dataset (SHWD) [29] and Safety Helmet detection with Extended Labels 5K images (SHEL5K) [30] into a new dataset for evaluation. The combined dataset contains 12,582 annotated images, with the training and validation sets randomly divided into 11,324 and 1258 in a ratio of 9:1. For our experiments, the detected objects were divided into two types, \"Helmet\" for objects wearing helmets and \"Person\" for objects without helmets. Figure 9 shows the percentage of ground truth for each object in the dataset.  
Figure 8: The overall structure of LRCA-Netv2 contains the original channel attention mechanism \(A_{\mathrm{c}}\) and the modified channel attention mechanism \(A_{\mathrm{s}}\).  
The experimental setup of this paper is shown in Table 1. The TensorFlow2 framework was used to build the experimental model for training, validation, and testing, and CUDA kernels were used to compute the results. The hardware mainly consisted of a high-performance workstation host. The workstation had an Intel(R) Core (TM) i5-11400F processor and an RTX 3050 graphics card. It is worth noting that such a mid- to high-end training environment was chosen only for fast convergence of the training model, thus avoiding the overfitting and gradient explosion caused by a lengthy training process with too small a batch size. In addition, the number of parameters mainly determines the lightweight class of the proposed network, so the lightweight class architecture discussed in the ablation experiments only compares the models' size. To be fair, all models were experimented on in the same configuration environment.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 2 System Model
### Co-Band PRS Receive Signal Model  
In wireless communication systems, ensuring the synchronization of received signals requires considering the effects of STO, CFO, and sampling clock offset. Sample clock offset include phase offset and frequency offset. Sample clock phase offset is absorbed by the STO, while sample clock frequency offset has a minimal effect and can usually be neglected. The focus of this paper is not on the CFO; common carrier frequency synchronization algorithms can be found in [23; 24; 25] which are compatible with the algorithms discussed in this paper. Therefore, in an indoor multipath fading channel, we model the arrival time of the multipath time delay component as a Poisson process and establish the channel impulse response as  
\[h(t)=\sum_{p=0}^{\infty}\sum_{r=0}^{\infty}\beta_{r,p}e^{i\theta_{r,p}}\delta (t-\tau_{p}-\tau_{r,p}) \tag{7}\]where \(\tau_{r,p}\) denotes the arrival time of the r-th ray in the p-th cluster, and the arrival time of the first ray of the p-th cluster is defined as \(\tau_{p}\). For ranging, our goal is to identify the first arrival path of the first cluster. The channel frequency response can be described as  
\[H_{ijk}[k]=\sum_{p=0}^{P-1}\sum_{r=0}^{\infty}\beta_{r,p}e^{-j\theta_{r,p}}e^{2 \pi\left(\tau_{p}+\tau_{r,p}\right)\left(k/T_{sub}\right)}. \tag{8}\]  
At the receiver side, assuming that it has been downconverted back to baseband, the baseband signal is sampled at \(T_{S}\) intervals to obtain the discrete baseband received signal as  
\[y[n]=\frac{1}{N}\sum_{i=0}^{\infty}\sum_{k=0}^{N-1}H_{i,k}[k]X_{i,k}[k]e^{2 \pi\frac{(k+\varepsilon)\left(n+\varepsilon\right)\left(n+\delta-N\tau p-N \right)}{N}} \tag{9}\]  
where \(\varepsilon\) and \(\delta\) denote the normalized CFO and STO, respectively.
## 2 Establishment of the Multi-Time-Delay Control System
### Impact Analysis of Multi-Time Delays
The decrease of control effect of System2 is larger than that of System1 with the same time delays, that is, the time-delay robustness of System2 is less than that of System1. When the unequal time delays are considered, the acceleration control effect to the 8 th floor of System2 decrease by 53.37%. Compared with the case of the equal time delays, the control effects of System2 are slightly decreased. As shown in Figures 3 and 4, the maximum strokes of System2 are close to those of System1 when without time-delay effect. However, compared with the whole forces of System1, the sum of the forces of the sub-controllers is larger, which means relatively speaking, the output of System1 is lower than that of System2. In System2, the differences of the control parameters are relatively close to zero, indicating that the multi-controllers operate synchronously. As shown Figures 3-6, equal time-delays reduce the acceleration control effects of System1 and System2. Comparing Figures 3-5, the control forces of System1 and System2 significantly increase, but their strokes are basically unchanged. This is caused by the less impact of time-delays on the displacement feedback gain of the auxiliary mass. Comparing Figure 4 with Figure 6, the differences of the control parameters of the multi-controllers with equal time delays are nearly equivalent to those of the systems without time delays, which means that equal time-delays have little influence on the synchronization of System2. Figure 8: The deviations to the control parameters of System2 with unequal time delays, (**a**) the forces and (**b**) the strokes. Figure 7: The AMD1 parameters of System2 with unequal time delays, (**a**) the forces and (**b**) the strokes. As shown in Figures 7 and 8, the differences of the control forces are obvious when there are unequal time delays in System2, which means that unequal time-delays have an impact on the synchronization of System2, where System2 consists of two relatively independent subsystems. However, compared to the differences of the control forces, the differences of the strokes are relatively smaller. This is due to the fact that time-delays have less influence on the feedback gains to the displacements of the auxiliary mass. To analyze the impact of the time-delay differences on different systems, the time delay difference is increased from 0 to 0.02 s, and the corresponding maximum strokes and maximum control forces of System2 are plotted in Figure 9. As the time-delay differences increase,