esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Anti-Wrinkle Performance with Glycosl Formaldehyde-Free Finishing Agents  
Before discussing the molecular structure and properties of glycosyll formaldehyde-free finishing agents, it was necessary to analyze the anti-wrinkle property of cotton fabrics with those finishing agents. The structure of glycosyll polyaldehydes finishing agents (OFr, OSu, OTr, ORa, OSt) and the glycosyll polyuronic acid finishing agents (openSu, openTr, openRa, openSt) were shown in Figure 3. As monosaccharides will only produce one carboxyl group after carboxylation, a single carboxyl group cannot be cross-linked with hydroxyl groups according to the esterification cross-linking mechanism of polycarboxylic acid finishing agents; therefore, monosaccharides did not undergo a two-step oxidation reaction. Of these finished cotton fabrics with these glycosyll finishing agents and non-finishing cotton fabrics, the BTCA, GA, and DMDHEU finished fabrics were set as the control samples, and the wrinkle recovery angle (WRA), whiteness index (WI), and tensile strength (\(TS\)) of the finished fabric were measured, and the results are shown in Figure 4.  
From Figure 4, the anti-wrinkle performance of fabrics finished with glucose and the OFr finishing agent failed to reach the standard range of cotton fabric anti-wrinkle performance (WRA \(\geq\) 250\({}^{\circ}\)) [1; 3]. The anti-wrinkle performance of the fabrics finished with disaccharides, trisaccharides, and tetrasaccharides glycosyll anti-wrinkle finishing agents reached the standard range. After glucose finishing, the WRA of the treated fabric was increased slightly, because glucose was a reducing monosaccharide, and the aldehyde group of glucose was cross-linked with the hydroxyl group of cellulose under the high temperature, which improved the WRA of the finished fabric.  
After finishing with sauger-based polyaldehyde finishing agents OSu, OTr, ORa, and OSt, the WRA of the fabric was significantly improved, and the ORs finished fabric had the highest WRA of 249\({}^{\circ}\). The Ofr-, Osu-, Otr-, Ora-, Ost-finished fabrics had large differences in TS and WI, the WRA of the OFr-finished fabric was the best and the WRA of the OSt-finished fabric was the worst. It may be because OFr has a low aldehyde content, while OSt has a higher aldehyde group content than OFr. The aldehyde group fully reacted with the cellulose hydroxyl group during the high-temperature curing, leading the strength and whiteness of the fabric to be significantly reduced.  
Compared with the fabrics finished with glyco-polyaldehyde finishing agents, the WRAs of the fabrics finished with openSu, openTr, openRa, and openSt were higher; the WRA of the openTr-finished fabric reached 262\({}^{\circ}\). This was because the molecular structure of openTr was relatively symmetrical, and the molecular volume was small. The openSu-, openTr-, openRa-, and openSt-finished fabrics have good TS, which were above 68%, and the WI of the finished fabrics was around 70.
## 4 Discussion  
### Techniques for Measuring the Changes in the Residual Lower Limb  
#### 4.1.1 Water Immersion  
The water immersion measuring technique is one of the most frequently used techniques for determining the residual limb volume. Most publications used this technique as their 'gold standard' in performing validity tests [28; 29; 31; 32]. In this technique, the amount of water displaced from a tank is measured and calculated as the residual limb volume (Archimedes' principle). The measurement is conducted by asking the subject to lower their residual limb into the tank until it reaches a specific marker [21; 26; 28; 29; 30; 31; 32], or water is pumped into the tank to immerse the residual limb [27]. During the measurement sessions, the knee should be in the position of the same degree of flexion/extension (knee flexed about 25\({}^{\circ}\)) throughout all the sessions, and the same marking placement for the residual limb immersion level should take place [21]. The temperature and atmospheric pressure of the water also need to be considered since they also affect the results [30]. This measuring technique is quite sensitive to the subject's movement as the subject needs to keep their residual limb at a constant position during the measurement sessions. Even one movement of the subject will produce inaccurate results [4]. Furthermore, since this is a contact measuring technique, it can distort the shape of the residual limb during the measurement sessions owing to the hydrostatic or buoyancy effects. Later, [PERSON] et al. [37] made some improvements to this method by introducing a hydrostatic weighing technique that uses the apparent weight of water with residual limb immersion (i.e., there is no difference by measuring the amount of displaced water) as the residual limb volume.
# In-Situ Monitoring and Analysis of the Fitting Corrosion of Carbon Steel by Acoustic Emission
## 4 Conclusions  
The pitting corrosion of Q235 carbon steel in NaHCO\({}_{3}\) + NaCl solutions was studied by in-situ monitoring of the AE technique and OCP simultaneously. The concentration of NaCl had a pronounced influence on the OCP evolution. In 500 mg/L NaCl, the OCP varied in a very narrow range, indicating a relatively stable state. However, with the increase of the NaCl concentration, the OCP dropped significantly and stabilized at a rather negative potential.  
The AE monitoring results were in accordance with the results of the OCP monitoring. However, the OCP only presented thermodynamic information of the corrosion in the interface, while the AE sensor detected the breakdown of the passive film and small damage in the occluded pits under the metallic surface, and the relative events.  
The AE signals of pitting corrosion on carbon steel were classified into three types by waveform parameters clustering after AE waveform processing, including pre-treatment, shape preserving interpolation, and denoising. The result indicated that the developed signal processing is a highly efficient method for the classification of AE signals and preparation of the waveform for further data analysis.  
A method based on 2D pattern recognition was established for the identification of different types of corrosion in Matlab. The analysis results showed that the method can be used to distinguish between uniform corrosion and localized corrosion effectively, while it was not very effective for distinguishing different localized corrosion.  
The AE technique could be applied to in-situ monitoring of the corrosion of carbon steel or stainless steel containers in different industries and for the reinforcement of steel bars for construction. However, the main obstacle to the use of AE monitoring is environmental noise, which make data processing difficult.  
Conceptualization, J.T. and H.W.; methodology, J.T. and H.W.; software, J.T. and H.W.; investigation, J.T., H.W. and G.C.; data curation, J.L.; writing--original draft preparation, J.T. and Y.W.; writing--review and editing, J.T. and Y.W.; visualization, J.T. and J.L.; supervision, H.W.  
This research was funded by Applied Basic Research Programs of Science and Technology Department of Sichuan Province, grant number 2017Y0044 and Project Funding to Scientific Research Innovation Team of Universities Affiliated to Sichuan Province, grant number 18 TD0012.  
The authors declare no conflict of interest.
## 4 Registration Algorithms Based on Feature  
The feature-based algorithm only selects a certain portion of the point cloud for registration rather than using all of the points. This approach is robust and does not require the initial pose. Feature extraction and feature matching are common components of feature-based point cloud registration algorithms. Among them, feature extraction decreases the amount of data to be processed. Moreover, it converts difficult-to-compare feature information into easily comparable information, making it easier to match important points; feature matching is used to find the correct correspondence between features of the source and target point cloud to compute the transformation matrix [27]. Figure 8 depicts the flowchart of a typical feature-based point cloud registration method. Milestone methods are listed in Figure 9.
## 2 Materials and Methods
### Ablation  
Ablation studies use regions of noninformation to determine significant data points in an input. In RNNs, zeroing of input can be achieved through forward filling with the average of prior inputs. The rationale for using the average is that an RNN exploits prior information in the time series. Therefore, replacing a single input value with the average of the previous cells removes any information supplied by those cells as seen in Figure 3. A single data point would be ablated and fed into the model, whereby predictions would be made by sliding over the single ablated feature value (Algorithm 1 and Figure 3). For a given multivariate time series \(\mathbf{T}_{l}\) of length \(l\), a region \(\mathbf{A}\) exists, which can be ablated at point \(p\), given that there are \(w-1\) values that precede the value and an additional \(w-1\) values that follow it. It follows the understanding that there must be a sufficient number of entries preceding the ablated value, whilst still allowing the RNN to slide over the time series, generating the errors. Specifically, in this study, using models that take in \(w=92\) values and have a 7-day forecast horizon(\(h=7\)), \(\mathbf{A}\) spans the central 108 values. The method produces \(w\) pairwise errors \(\mathbf{e}\), calculated by taking the absolute value of the differences between the ablated and non-ablated predictions as the RNN slides over the ablated data. The algorithm returns the average percentage pairwise error for all the inputs into the RNN.  
```
input :A time series \(\mathbf{T}_{l,f}\) // length \(l\) and features \(f\) :An RNN model (\(\cdot\)) \(l-(w+h)\) predictions output :An error matrix \(\mathbf{E}_{Avg}\) of size \(w\times f\)
1\(Error_{Avg}=[]\)
2\(\mathbf{\hat{y}}\leftarrow\)model(X)
3 forfeatureinfdo
4\(E_{Feature}=[]\) /* Iterate over region A */
5 foriin1:len(A)do
6\(\mathbf{X}_{Ablated}\leftarrow\)ablate(X,\(i+91\),feature)
7\(\mathbf{\hat{y}}_{Ablated}\leftarrow\)model(X\({}_{Ablated}\))
8\(\mathbf{e}\leftarrow\frac{|\mathbf{\hat{y}}_{Ablated}-\mathbf{\hat{y}}|}{ \mathbf{\hat{y}}}\) /* Concatenate nonzero errors from RNN sliding over ablated value */
9\(\mathbf{E}_{Feature}\leftarrow\)concat(E\({}_{Feature}\), \(\mathbf{e}\)\([i:i+91]\))
10 end for
11\(\mathbf{E}_{Avg}[feature]\leftarrow\frac{1}{\texttt{len}(\mathbf{A})}\sum_{ \texttt{len}(\mathbf{A})}^{j=1}\mathbf{E}_{feature}[j]\)
12 end for
13 returnE\({}_{Avg}\)
```  
**Algorithm 1**Ablation Algorithm.  
\begin{table}
\begin{tabular}{l r r r r r} \hline \hline
**Model** & **Hidden States** & **Layers (\# Cells)** & **Dropout** & **Alpha** & **Test Accuracy (SMAPE)** \\ \hline RNN & 64 & 1 & 0.000 & 0.005 & 1.83 \\ GRU & 128 & 2 & 0.065 & 0.010 & 1.81 \\ LSTM & 128 & 2 & 0.050 & 0.008 & 1.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters and test accuracy for the various RNN architectures.
# Quantifying Emotions in Architectural Environments Using Biometrics
## 1 Introduction
### Biometric Tools  
The experience of positive emotions can be measured using several biometric tools such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopic (fNIRS), electromyography (EMG), electrocardiography (ECG), galvanic skin response (GSR), and eye-tracking [10]. An EEG records the brain's electrical activity using electrodes placed on the scalp. The power spectrum is used as an analysis metric, describes the distribution of power, showing EEG signals in the frequency domain, and represents power distribution in frequency bands. Another analysis metric for EEG is frequency bands; their approximate spectral boundaries are delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-40 Hz), and gamma (40 Hz and above) [5, 11]. Different neural parts are activated in response to different subjective experiences. fMRI measures the flow of blood in the brain, with shaded areas indicating that the corresponding region of the brain is activated. fNIRS measures cortical hemodynamic activity in response to neural activity [10].  
ECG records the electrical signals of the heart. It measures the heart rate (HR) and heart rate variability (HRV) to discern states of relaxation or anxiety. EMG records the electrical activity of skeletal muscle using thin needle electrodes inserted into the muscle through the skin. GSR (SCR) measures fast-varying skin conductance caused by external stimulations, and PPG (HRV) measures variation in the time interval between the subject's heartbeats. The analysis metrics for GSR include SCR peaks, where the SCR signal jumps higher than the defined threshold, and the peak amplitude, which is the amplitude at the peak minus the defined threshold [5]. For PPG (HRV), the inter-beat interval (IBI), which is the time between consecutive heartbeats measured in milliseconds, and the AVNN score, the average of the normal sinus rhythm (i.e., heart rate of 60-100 beats per minute), are used as the analysis metrics [5]. Lastly, eye-tracking devices can measure both the \"point of interest\" (POI) and the duration of attention by tracking the eye movements and the pupil size as related to emotion [12]. It can also be used to identify, quantify, and visualize the patterns shown by specific groups of people, combining factors associated with viewing selected architectural scenes.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{4}{c}{**Standard**} \\ \cline{2-5}  & **ZigBee** & **WirelessHART** & **ISA100.11a** & **WIA-PA** \\ \hline Data Security & High & Very High & Very High & Very High \\ \hline Scalability & Medium & High & High & High \\ \hline Power Usage & Low & Low & Low & Low \\ \hline Data Transfer & & Low (20–250 kbps) & & \\ Rate & & & & \\ \hline Network & \multirow{2}{*}{Star/Mesh/Tree} & \multirow{2}{*}{Star/Mesh} & \multirow{2}{*}{Star/Mesh} & \multirow{2}{*}{Hybrid} \\ Topology & & & & \\ \hline Data & \multirow{2}{*}{Low} & \multirow{2}{*}{Very High} & \multirow{2}{*}{Very High} & \multirow{2}{*}{Very High} \\ Reliability & & & & \\ \hline Routing & \multirow{2}{*}{Limited} & \multirow{2}{*}{Full} & \multirow{2}{*}{Full/Limited} & \multirow{2}{*}{Limited} \\ Capability & & & & \\ \hline Channel & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} \\ Hopping & & & & \\ \hline Frequency & \multirow{2}{*}{27 (All Bands)} & \multirow{2}{*}{15 (2.4 GHz)} & \multirow{2}{*}{16 (2.4 GHz)} & \multirow{2}{*}{16 (2.4 GHz)} \\ channels & & & & \\ \hline Manager & \multirow{2}{*}{Centralized/} & \multirow{2}{*}{Centralized} & \multirow{2}{*}{Centralized} & \multirow{2}{*}{Centralized/} \\ Architecture & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features comparison between the different industrial wireless sensor networks [14]. Figure 4: Content organization of the paper. ments. Comprehensive studies of the existing IWSN communication protocol's network architecture, design, and standardization can be found in [3; 6; 15; 16; 17]. This paper's remaining sections are organized as follows: Section 2 provides an introduction relating the importance of wireless networks in process control automation with the classification of different industrial processes with their functions. Section 3 gives a detailed analysis of WirelessHART in process monitoring and control applications in simulations and real-time environments, along with its industrial network architecture. Section 4 describes the challenges and design requirements for WirelessHART with various limitations and its possible research solutions, followed by a summary and conclusion in Section 5.
## 6 Simulation Results and Discussions
### Multi-Dimensional RWTA for Different Kinds of Channels  
As described above, there are three different kinds of channels, i.e., TDCh, QKCh and MBCh in QKD optical networks. Setting up QKCh and MBCh for key configuration will enhance the security, but it also brings wavelength-timeslots consumption. How to balance the wavelength-timeslot resources that are allocated to those three channels is a very important topic. In this part, while establishing three channels, we apply the proposed multi-dimensional RWTA under three different cases. Three different cases include that the wavelengths allocated to TDCh, QKCh, and MBCh are 32:2:2, 28:4:4, and 24:6:6 respectively. Blocking probabilities of TDCh and QKCh in the network under three cases are shown in Figure 8a,b. It can be seen from the figures that the more wavelengths assigned to QKCh and MBCh, the higher blocking probability TDCh achieved, but lower blocking probability can be obtained by QKCh; and vice versa. One thing we need to notice is that by increasing 2 wavelengths (i.e., 5%) allocated to QKCh, the blocking probability of TDCh increased by 27.5% (under 240 Erlang traffic load), but the blocking probability of QKCh reduced up to 82.1%. This means that we could greatly improve the security level of requests at a relatively low cost of network provisioning capacity.  
Figure 7: (**a**) Blocking probability vs. traffic load when the requirements of timeslots are diverse; (**b**) Blocking probability vs. traffic load when the requirements of timeslots are constant.
## 5 Conclusions  
By using MIDAS/GTS software and the nonlinear dynamic time history calculation method, the seismic response characteristics of large diameter vertical double-layer pipe jacking in soil-rock combination sites have been numerically analyzed and studied. The main conclusions are as follows:  
1. With the differential seismic motion input, the acceleration response of the upper tube is 1.1-1.3 times for the lower tube and the horizontal displacement value is more than 2 times. Nevertheless, the relative horizontal displacement of the lower tube changed sharply in the soil-rock interface, which is more likely to cause serious damage to the pipe in the soil-rock composite stratum.
2. The superior frequency range of the Fourier spectrum at the soil-rock composite stratum is wider than that of all homogeneous soil layers and easily causes vibration damage to pipe jacking structures with different natural frequencies. Moreover, the stress increase of the arch waist part in the lower soil-rock combination stratum is 104% compared with the homogeneous stratum, and the failure of the pipe jacking structure will further aggravate if the soil rock interface fluctuates longitudinally.
3. The smaller the thickness of the interlayer soil layer between the upper and lower pipes, the larger connected as a whole of the plastic zone and the more likely damage to the overall stability of pipe jacking in soil-rock combination stratum is. When the small spacing earthquake failure effect cannot be neglected, appropriate measures should be taken to strengthen the interlayer soil strength to resist earthquakes.  
Conceptualization, G.S. and J.H.; methodology, L.D.; software, L.D.; validation, G.S., J.S. and J.H.; formal analysis, G.S. and L.D.; investigation, G.S.; resources, J.S.; data curation, L.D.; writing--original draft preparation, L.D.; writing--review and editing, G.S. and J.H.; visualization, G.S.; supervision, G.S.; project administration, J.H.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key Program of National Natural Science Foundation of China, grant number 52038006 and Innovation Team Development Program of the Ministry of Education, grant number IRT_17R69.  
Not applicable.  
The data are contained within the article.  
We are grateful to the Jinan Rail Transit Group Co., Ltd. for providing us with the experimental project.  
The authors declare no conflict of interest.
# Data-Aided Frequency Offset Estimation for CE-OFDM Broadband Satellite Systems
## 7 Conclusions  
In this paper, we introduced the network architecture and the propagation environment of satellite broadband systems and analyzed the impact of CFO. We proposed a method for estimating and compensating the CFO based on the frequency domain pilot symbols in a flat fading channel. By analyzing the frequency domain signal, we obtained that the frequency domain pilot symbols have optimal estimation performance when they are conjugated symmetrically on both sides of the DC subcarrier. We have given the estimation process of the frequency offset, the specific expression of the estimation result, and the corresponding CRB expression. To demonstrate the performance of the scheme, we simulated the bit error rate performance and MSE performance at different frequency offsets. The simulation results show that the proposed scheme can effectively estimate the frequency offset and achieve good BER performance. The CFO estimation strategy under the frequency-selective fading channel and the phase noise estimation strategy will be studied in further research.  
[PERSON] was in charge of modeling the estimation problem and researching the optimization pilot structure. [PERSON] did the numerical simulations and wrote the paper. [PERSON] gave some suggestions on the mathematical model and formula derivation.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61801033 and the Fundamental Research Funds for the Central Universities (2019 RC05).  
The authors declare no conflict of interest.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 3 Model of an ASK-Type AQC System
### Description of Received Quantum State  
In this section, we describe the received quantum state corresponding to each entangled state. However, based on consideration of the prospect of the discussion, we analyze the quasi-Bell state using a Stinespring representation, and analyze the TSVS using a Kraus representation. Note that the light that corresponds to mode A is assumed to pass through an ideal channel because it is propagating inside Bob.