esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
###### Abstract
[PERSON] and [PERSON] [28] propose a fast forward algorithm for single-well imaging to calculate the borehole displacement excited by reflected waves. The synthesis of the dataset in this study benefits from the methods of the two aforementioned papers. This article aims to utilize MC-ConvTasNet for the clear extraction of reflected waves. By synthesizing a large number of theoretical waveforms through analytical algorithms to train MC-ConvTasNet, it addresses the challenge of insufficient field logging data for forming a training dataset. The automatic separation of reflected waves is achieved, with the entire process requiring no additional manual intervention in the neural network and no prior acquisition of wellbore velocity information. In the field logging data, MC-ConvTasNet demonstrates impressive generalization ability, outperforming parameter estimation, a median filter and an F-K filter in suppressing direct waves. When the acoustic logging tool is continuously lifted, MC-ConvTasNet is capable of extracting reflected waves in real time, even when the interfaces are parallel to the wellbore axis. The rest of the paper is organized as follows: Section 2 introduces the architecture of MC-ConvTasNet, the synthesis of the datasets, the loss function and training details. In Section 3, theoretical waveforms are synthesized for a hard-to-hard single-interface model, a soft-to-hard single-interface model and a double-interface model. The results of MC-ConvTasNet are compared to those of parameter estimation, a median filter and an F-K filter. Next, the wave separation capability of MC-ConvTasNet is verified using synthetic data with noise and field logging data. Finally, we compare the wave separation performance of MC-ConvTasNet and Wave-U-Net. In Section 4, we delve into the requirement for MC-ConvTasNet and its potential for widespread application.
# Auralization of High-Order Directional Sources from First-Order RIR Measurements
## 3 Listening Experiment--Comparative Study
### Results and Discussion

Table 3: Mean direction offset to dy per auralization technique over all tested beam directions \(\varphi_{S}\). Figure 10: Median value and \(95\%\) confidence interval of distance ratings per beam direction \(\varphi_{S}\). Please note the fixed y-axis showing the interval of \([0,10]\)m form the listener. Although there is no clear perceptual winner, it still appears that the \(As\) and \(Sr\) approaches match the \(Dy\) reference best. We assume that using the first-order source (Cubelet) enhances the flexibility as its frequency range for directivity synthesis is larger than the one of the IKO because of its smaller size. Its upscaling permits a modular exchange of the directivity to arbitrary artificial or measured higher-order directivity patterns. On the receiver side, measurements with the dummy head (\(Dy\)) are not modular in terms of exchanging the receiver directivity patterns in terms of other HRIRs, while the measurements with the em32 and ST450 microphone arrays permit HRIR exchange. The em32 reaches a native resolution up to the 4 th order, whereas the 1 st-order ST450 is less demanding in terms of channel count, has no satisfactory native resolution but allows to be upscaled to higher orders. Our impression is that auralization involving a first-order receiver and either the highly directional source or its first-order source as a replacement tend to work most reliably. Essentially, the results allow us to recommend the SRD ARIR (\(Sr\)) model and processing method for its high degree of modularity and reduction of measurement hardware and effort. It reaches perceptual qualities comparable to rendering based on dummy-head measurements (\(Dy\)), while higher-order directional sources can be exchangeably interfaced with the processed and upscaled SRD ARIRs. It is necessary to mention that the particular prototype employed as first-order measurement source (Cubelet) in our study is not necessarily powerful enough for every application, for instance, when the signal-to-noise ratio is low because of background noise. In such cases, stronger alternatives could be considered [55].
## 3 Results and Discussion
### Microbial Quality  
The initial counts of mesophilic, psychrophilic microorganisms and molds and yeasts on peeled potatoes were 4.36, 4.30 and 4.05 log CFU/g, respectively (Figure 3), which were similar to results of [PERSON] et al. [29], who found that these values were 4.09, 3.83 and 3.00 log CFU/g on fresh-cut mushrooms on day 0. The microbial counts increased rapidly from 0 to 4 days, and then, the growth slowed down during the subsequent storage. [PERSON] et al. [30] also found a similar trend in fresh-cut sweet potatoes. This trend may have been due to the stable phase of microbial growth or the inhibition of some aerobic microbes by vacuum packaging [31]. Besides this, the slowing-down of microbial growth during the late storage period may also be caused by the increasing of organic acid. [32]. On the 8 th day, the count of mesophilic bacteria increased to 7.61 log CFU/g, which is lower than the maximum acceptable contamination value (7.70 log CFU/g) required by the Applied and Environmental Microbiology of France for minimally processed products [33], indicating the potatoes maintained a qualified microbiology load until the 8 th day. Moreover, the count of psychrophilic bacteria reached 7.64 log CFU/g, which is lower than 8 log CFU/g, which was reported to be insufficient to cause visual defects of minimally processed vegetables [34; 35].  
Figure 2: The distribution of bacteria on a genus-level during the storage of vacuum-packaged peeled potatoes.
## 3 Results and Discussions
### Fed-Batch Simultaneous Sacharification and Fermentation with Fr. RH  
RH fractionated with 6 wt.% NaOH at 150 \({}^{\circ}\)C for 45 min in the bench-scaled (30 L) horizontal rolling reactor was subjected to SSF. The initial solid loading in the first stage of SSF was 6% (_w/v_) water-insoluble solid (WIS). As shown in Figure 6a, 24.0 g of Fr. RH, which contained 19.2 g of glucan, was loaded into the YP medium (20 g/L yeast extract, 10 g/L peptone) in citrate buffer solution (pH 4.8) adjusted to a total volume of 400 mL. After the first SSF, when the rate of ethanol production no longer increased, 11.4 g of solids remained unconverted; therefore, 12.6 g of Fr. RH was added at the beginning of the second stage of SSF and adjusted to 6% WIS.  
Figure 6b presents the glucose and ethanol concentration profiles and ethanol yield from Fr. RH during the two-stage fed-batch SSF. When the fed-batch SSF started, the glucose level increased within the first 5 h and then quickly dropped until the second WIS loading. Therefore, it is difficult to achieve high bioethanol production in a batch-type process because it is subjected to glucose-limiting conditions. In the first stage, the ethanol production almost reached a maximum at 28 h, and its yield and concentration were 74.3% and 20.2 g/L, respectively. However, in the second SSF, the ethanol production rate was moderate and was only 8.5 g/L at 32 h. That is, 28.7 g/L of ethanol was obtained with a total of 60 h of SSF. In the second stage of SSF, the ethanol yield was only 40.1%, which was relatively low because the amount of substrate was increased. The decrease in the ethanol yield was primarily attributed to a high solid loading; i.e., in a conventional bioreactor, high viscosity may prevent efficient mixing, and the reaction rate may be affected by a lack of water or uniform mixing [40].  
Figure 5: Ninety-six-hour enzymatic digestibility of alkaline-fractionated RH with cellulase loadings of 15 FPU/g glucan. All data are the means of triplicates.
# Improving Data Communication of Enhanced Loran Systems Using 128-_ary_ Polar Codes
## 2 Materials and Methods  
This section presents the fundamental techniques and system parameters underpinning the eLoran data communication architectures evaluated in this study. We first provide a detailed exposition of the conventional eLoran data communication system, which employs RS coding and 128-_ary_ PPM. Subsequently, we introduce the proposed 128-_ary_ polar coding scheme, specifically designed to enhance the error performance of eLoran data transmission while maintaining full compatibility with the established modulation and positioning framework. Figure 1. offers a comparative overview of both system models: Figure 1a illustrates the traditional eLoran data communication system, while Figure 1b depicts the proposed system incorporating the 128-_ary_ polar code. In both configurations, the original \(K^{\prime}\) message bits are first processed by a Cyclic Redundancy Check (CRC) encoding, yielding \(K^{\prime}\) bits (the original message bits with appended CRC parity). These \(K^{\prime}\) bits are then mapped to \(K\) 128-_ary_ symbols. These symbols are subsequently encoded using the RS code or the 128-_ary_ polar code. Following 128-_ary_ PPM, the modulated pulses are transmitted over an Additive White Gaussian Noise (AWGN) channel. At the receiver, the signals are demodulated, and the resultant sequences are processed by the corresponding RS or 128-_ary_ polar decoder. Then, a final CRC verifies the integrity of the recovered message.  
The primary distinction between the systems, as illustrated by the comparison in Figure 1a,b, lies in the encoding/decoding strategies, along with a corresponding refinement of the demodulation process for the 128-_ary_ polar code scheme. Importantly, the 128-_ary_ PPM scheme remains unchanged. This consistency ensures that improvements in data encoding do not compromise the established positioning functionality of the eLoran system, which is a key objective of this work.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods  
### Data Sources  
In this paper, we performed the integration of several data sources: TROPOMI satellite measurements, wind data, information about the ships' positions, and information about the properties of analyzed ships. In this section, the reader can find a general overview of the data sources that will be utilized throughout the study. We also provide here the criteria applied for data selection, which set the scope of this study.
## 3 Results and Discussion
### Ablation Study  
In this paper, an attention mechanism and multi-scale feature extraction module are added to improve the recognition effect of the TransUNet model. In the ablation study, the influence of the SE module, the ASPP module and their combination on model performance improvement are verified, respectively, and the results are shown in Table 3. Among them, Number 1 refers to the identification results of the original TransUNet model, Numbers 2&3 refer to the identification results when SE and ASPP modules are used separately, and Number 4 refers to the identification results when two modules are used simultaneously.  
As can be seen from Table 3, the integration of the SE module and the ASPP module has greatly improved the accuracy of the model. When both modules are used simultaneously, the _IoU_ index of the model can reach 94.24% and the _Dice_ index can reach 97.02%, which have an improvement of 1.74% and 0.94%, respectively, compared to the original model. The segmentation metrics improved only marginally when the SE module was used alone, with only a 0.04% and 0.02% improvement in _IoU_ and _Dice_ indices, respectively. The segmentation metrics improved considerably when the ASPP module was used solely, with a 1.56% and 0.84% improvement in _IoU_ and _Dice_, respectively. The reason for this phenomenon is that, for the dataset in this experiment, the channel attention mechanism plays a smaller role because the solidified region image is close to a gray scale image and the pixel values of the initial three channels are similar, which leads to a small difference in the importance of features among different channels. At the same time, the scale of the  
Figure 5: Visualization of comparison results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **SE** & **ASPP** & **_IoU (\%)_ & _Dice (\%)_ & _IoU Improvement (\%)_ & _Dice_ Improvement (\%)_ \\ \hline
1 & - & - & 92.48 & 96.08 & - & - \\
2 & \(\surd\) & - & 92.52 & 96.10 & +0.04 & +0.02 \\
3 & - & \(\surd\) & 94.04 & 96.92 & +1.56 & +0.84 \\
4 & \(\surd\) & \(\surd\) & 94.24 & 97.02 & +1.74 & +0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation study.  
region to be segmented in the forming layer image is small and changing constantly. The ASPP module has a pyramid pooling structure to obtain multi-scale features, which can fully extract feature information from the image. Therefore, it shows excellent segmentation performance on the dataset of the solidified region image. When two modules are used simultaneously, multi-scale feature extraction is further achieved by obtaining different levels of importance for channel features, which results in more efficient acquisition of solidified region image features and improving segmentation accuracy effectively.
## 4 Proposed Framework
### Federated Learning Strategy
#### 4.3.4 Global Aggregation  
The central unit aggregates the model updates from all edge servers to refine the global model. The aggregation step uses the following **Federated Averaging (FedAvg)** algorithm:  
\[w^{t+1}_{0}=\frac{1}{m}\sum_{j=1}^{m}w^{t}_{j} \tag{18}\]  
where  
* \(m\) is the number of edge servers.
* \(w^{t}_{j}\) is the locally trained model from edge server \(j\) at round \(t\).
## 3 Research Methods  
### Continuum Removal  
In the case of similar spectral curves, direct analysis from the original spectral curves is not conducive to extracting spectral features, so it is necessary to conduct further processing of the original spectral curves. Removing the continuum allows for the normalization of different spectral curves to a uniform background, effectively highlighting the absorption, reflection, and scattering features of the spectral curves, which is convenient for the identification and classification of different vegetation [22; 23; 24]. In this study, continuum removal was performed using the ENVI5.3 software, and its calculation formula is as follows:  
\[K=\frac{R_{end}-R_{start}}{\lambda_{end}-\lambda_{start}} \tag{1}\]  
\[R_{Hi}=R_{start}+K\cdot(\lambda_{i}-\lambda_{start}) \tag{2}\]  
\[CR_{i}=\frac{R_{i}}{R_{Hi}} \tag{3}\]  
where \(K\) is the slope between the absorption start and end bands, \(R_{start}\) and \(R_{end}\) represent the reflectance of the original spectra at the absorption start and end points, \(\lambda_{start}\) start and \(\lambda_{end}\) denote the wavelengths of the absorption start and end points, \(R_{Hi}\) corresponds to the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Botanical Name** & **Latin Name** & **Subjects** & **Field Survey Photos** \\ \hline Suaeda glauc & Suaeda glauc(Bunge) Bunge & Chenopodiaceae & \\ Tamarisk & Tamarix chinensis Lour & Tamarixaceae & \\ Spartina alterniflora & Spartina alternifloraLoisel & Poaceae & \\ Typha orientalis & Typha orientalis Presl & Typha orientalis & \\ Phragmites & Phragmites australis (Cav.) & & \\ Trin. ex Steu & Poaceae & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical wetland plants of the Yellow River Delta.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Satellite** & **Treatment Level** & **Imaging Time** & **Number of Bands** & **Spectral Range** & \
\begin{tabular}{c} **Spectral** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} \\ \hline OHS-2C & L1B & 18 September 2020 & 32 & 400–1000 nm & 2.5 nm & 10 m \\ OHS-2C & L1B & 18 September 2020 & 32 & 400–1000 nm & 2.5 nm & 10 m \\ OHS-3B & L1B & 24 July 2022 & 32 & 400–1000 nm & 2.5 nm & 10 m \\ OHS-3B & L1B & 24 July 2022 & 32 & 400–1000 nm & 2.5 nm & 10 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: OHS imaging for research.  
\"Hull\" value for band \(i\), \(CR_{i}\) signifies the value of continuum removal for band \(i\), and \(R_{i}\) is the original spectral reflectance for band \(i\).
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 2 Working Principle of CW Doppler Radar
### Demodulation Techniques
To deal with the phase discontinuity problem, an extended differentiate and cross-multiply (DACM) algorithm is proposed in [68] for automatic phase unwrapping for phase reconstruction without ambiguities. In digital domain, the procedure of differentiation is usually approximately by forward difference:  
Figure 2.0  
where \(\varphi\left[n\right]\), \(I\left[n\right]\), and \(Q\left[n\right]\) are digital representations as in \(\varphi\left(t\right)=arctan\left[Q\left(t\right)/I\left(t\right)\right]\), and \(\Delta t\) is sampling interval. With a further accumulation, the phase information \(\varphi\left[n\right]\) can be recovered as:  
Figure 2.1  
Figure 2.2 \(I/Q\) signals form an arch fitting with the unit circle in \(I/Q\) plane. Instead of bothering with phase unwrapping, the DACM based phase demodulation technique allows the phase information to be directly retrieved from the calibrated \(I/Q\) signals without phase ambiguities. Figure 5b shows the processing of the same 0.2 Hz 16 mm displacement movement with the extended DACM algorithm. It is seen that the phase discontinuity problem is naturally solved. The DACM algorithm results in robust and accurate phase demodulation, regardless of the magnitude of vibration motions. It extends the linearity in nonlinear phase demodulation in Doppler radar sensing.
## Abbreviations  
* AAI Authentication and Authorisation Infrastructure
* ABE Attribute-based Encryption
* AES Advanced Encryption Standard
* B2B Business-to-Business
* DOB Date Of Birth
* GDPR General Data Protection Regulation
* IDaaS Identity-as-a-Service
* IDM Identity Management
* IdP Identity Provider
* LSSS Linear Secret Sharing Scheme
* P3P Platform for Privacy Preferences Project
* PAT Purpose Access Token
* PBE Purpose-based Encryption
* Policy Decision Point
* Policy Enforcement Point
* Personal Identifiable Information
* Software-as-a-Service
* Service Provider
* Time Access Token
* Topology and Orchestration Specification for Cloud Applications
## 4 Experimental Results and Analysis
### The Settings of Clustering Center and Privacy Budget Parameters
#### 4.2.1 Synthetic Datasets  
In terms of synthetic datasets, the clustering effect of different combinations of \(b_{1}\) and \(b_{2}\) of the DP-CDPC algorithm varies with the privacy budget, as shown in Figure 2, where the numbers in the algorithm label, respectively, represent the values of parameters \(b_{1}\) and \(b_{2}\).  
From Figure 2, several characteristics of the DP-CDPC algorithm can be observed. Overall, as the privacy budget \(\varepsilon\) increases gradually, the clustering evaluation index of the algorithm initially improves and then stabilizes. It can be seen that, within a certain range, increasing the privacy budget leads to better clustering effectiveness.  
For the flame dataset, when parameter \(b_{2}\) is set as 1 and \(b_{1}\) is set as 3, the algorithm achieves the best performance after reaching the steady state, and the critical value \(\varepsilon\) for reaching the stationary state is 15.  
For the spiral dataset, when parameter \(b_{2}\) is set as 1 and \(b_{1}\) is set as 1, 2, or 3, respectively, the algorithms show a consistent trend of change when different amounts of noise are added. When the same amount of noise is added, the clustering effect does not differ significantly with varying parameter values. For the compound and aggregation datasets, when parameter \(b_{1}\) and \(b_{2}\) are set as 3 and 0.5, respectively, the algorithm has the best effect after reaching the stationary state, and the critical value \(\varepsilon\) for reaching the stationary state is 1.5 and 2, respectively. For dataset R15, when parameters \(b_{1}\) and \(b_{2}\) are set as 1 and 0.5, respectively, the algorithm achieves the best effect after reaching the stationary state, and the critical value \(\varepsilon\) for reaching the stationary state is 0.5.
# Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential
## 3 Acceptable Clearance for Lane Changing
### Vehicle Interaction Potential Lane Change Model Considering Acceptable Lane Changing Clearance
When the workshop distance is greater than the acceptable gap, the result is gravitational force. However, combined with the actual driving of the vehicle, even if the longitudinal gravity of the rear vehicle \(TF\) on the lane changing vehicle \(M\) is generated, it cannot be characterized as attracting the rear acceleration of the vehicle \(M\). Therefore, on the basis of the coordinate system established in Figure 9, the vehicle interaction potential combined with the actual analysis of lane changing can be obtained:  
1. [label=(0)]
2. When \(F_{y}^{M-L}\geq 0\), vehicle \(M\) continues to follow. 3. When \(F_{y}^{M-L}<0\), the vehicle is subjected to repulsive force resulting in lane changing intention. When \(F_{y}^{M-TL}<0\) or \(F_{y}^{M-TF}>0\) indicates that vehicle \(M\) is being repulsed longitudinally by vehicle \(TL\) or vehicle \(TF\), lane changing is not recommended. When \(F_{y}^{M-TL}\geq 0\) and \(F_{y}^{M-TF}\leq 0\), it means that vehicle \(TL\) and vehicle \(TF\) exert gravity on vehicle \(M\) and can change lanes. At this time, vehicle \(M\) is subjected to lateral gravity and is \(\left|F_{x}^{M-TF}\right|+\left|F_{x}^{M-TL}\right|\), which causes vehicle \(M\) to accelerate crosswise and change lanes. As vehicle \(M\) turns to drive, vehicle \(L\) exerts a lateral repulsive force on vehicle \(M\), forcing vehicle \(M\) to accelerate laterally away from the original lane, so the sum of lateral forces received by the vehicle is \(\left|F_{x}\right|=\left|F_{x}^{M-L}\right|+\left|F_{x}^{M-TF}\right|+\left|F _{x}^{M-TL}\right|\). According to the relationship between force and acceleration, the following can be obtained: \(\left|a_{x}\right|=\frac{\left|F_{x}\right|}{m}\), that is, vehicle \(M\) will accelerate to the target lane with an acceleration of \(\left|a_{x}\right|\).