esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduction of Hexavalent Chromium Using _Sorbaria sorbifolia_ Aqueous Leaf Extract
###### Abstract  
Aqueous plant leaves extract (PLE) of an abundant shrub, _Sorbaria sorbifolia_, was explored for the reduction of hexavalent chromium, Cr(VI), to trivalent chromium, Cr(III). The effect of contact time, pH, PLE quantity, ionic strength, hardness, temperature and effective initial Cr(VI) ion concentration were tested; Cr(VI) reduction followed the pseudo-first order rate kinetics and maximum reduction was observed at pH 2. Significantly, Cr(VI) reduction efficacies varied from 97 to 66% over the pH range of 2 to 10, which bodes well for PLE to be used for the reduction of Cr(VI) also at a higher pH. PLE-mediated Cr(VI) reduction displays considerable efficiency at various ionic strengths; however, hardness strongly affects the reduction ability. Higher temperature significantly enhances the Cr(VI) reduction. This study reveals the potential use of PLE as a green reducing agent in aqueous extract for the efficient reduction of Cr(VI) to Cr(III).  
hexavalent chromium; aqueous stream; reduction; kinetics; _Sorbaria sorbifolia_ extract +
Footnote †: journal: Applied Physics Letters
# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
## 2 Methodology
### The Framework of Self-Supervised Shear Wave Noise Adaptive Subtraction
\[\mathcal{L}_{1}(\Theta_{1})=\|\mathbf{Z}-f(\mathbf{X};\Theta_{1})\|_{2}^{2}. \tag{5}\]  
In this way, the output of the network \(f(\mathbf{X};\Theta_{1})\) is expected to be the predicted noise associated with the X-component after training, which is essentially an estimate of the true noise with respect to the X-component (symbolized by \(\hat{\mathbf{Z}_{SX}}\)) in Z-component data. Subtracting \(\hat{\mathbf{Z}_{SX}}\) from \(\mathbf{Z}\) yields the updated Z-component data \(\mathbf{Z}^{\prime}\) with leaked X-component noise removed (seen in Equation (6)). Figure 1: The workflow of self-supervised shear wave noise adaptive subtraction in OBN data. In the first step, the X-component data are input into the network to predict the noise related to the X-component, then subtract the noise from the noisy Z-component data. In the second step, the Y-component data are input to the network to predict the noise related to the Y-component, and the denoising result is obtained by subtracting the predicted noise. \[\mathbf{Z^{\prime}}=\mathbf{Z}-\mathbf{\hat{Z}_{SX}}=\mathbf{Z}-f(\mathbf{X};\Theta_{ 1}). \tag{6}\]  
Naturally, in the second step, \(\mathbf{Z^{\prime}}\) will be further denoised as the updated noisy data by repeating the above steps using Y-component data (\(\mathbf{Y}\)) as the reference noise model. After feeding \(\mathbf{Y}\) into the network and optimizing the network parameters with Equation (7) as the loss function, the final denoised Z-component (\(\mathbf{Z_{d}}\)) can be reconstructed as follows:  
\[\mathcal{L}_{2}(\Theta_{2})=\|\mathbf{Z^{\prime}}-f(\mathbf{Y};\Theta_{2})\|_ {2}^{2}. \tag{7}\]  
\[\mathbf{Z_{d}}=\mathbf{Z^{\prime}}-\mathbf{\hat{Z}_{SY}}=\mathbf{Z}-\mathbf{ \hat{Z}_{SX}}-\mathbf{\hat{Z}_{SY}}=\mathbf{Z}-f(\mathbf{X};\Theta_{1})-f( \mathbf{Y};\Theta_{2}). \tag{8}\]  
In this case, the minimization of the loss function (Equations (5) and (7)) corresponds to the minimization of the objective function (Equation (4)) in the traditional method, while the network parameters correspond to the leakage coefficients (\(\mathbf{W}\)). Moreover, DNNs can learn nonlinear mapping relationships, in contrast to the linear iterations of traditional adaptive subtraction, to better represent the complex relationships of data.
# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 4 Experiments and Results
### Extractive Summarization

Table 1: ROUGE scores in percentage (%) for proposed extractive summarization methods. The topic-based method achieved a relatively high recall of 42.89%, suggesting that it successfully captured most of the important information from the reference summaries. However, its precision of 1.29% and F-score of 2.47% were comparatively lower, indicating that it may have generated some irrelevant sentences in the summary. When topic and concept approaches were combined in the topic + concept-based method, precision decreased significantly to 0.64%, indicating a decrease in the quality of selected summary sentences. The recall (10.73%) and F-score (1.19%) also dropped, indicating that the combination did not effectively improve the performance compared to topic-based alone. Lastly, the clustering-based method achieved the highest precision (1.69%) and F-score (3.17%) among the evaluated methods. This indicates that it selected summary sentences that closely matched the reference summaries, resulting in higher-quality summaries. However, its recall (38.91%) was relatively low, suggesting it may have missed some relevant information from the source document. It is observed that the overall performance of these models is very low; this is due to the fact that the summaries generated by these models were tested against the ground truth as annotated in [19]. The diagnoses and problems presented in the ground truth do not appear explicitly in the notes because they were manually extracted [19]. For this reason, the ROUGE scores, which test the similarity of the generated summaries and the ground truth by counting the overlapping words and concepts in both sequences, were very low for the extractive summarization models. This highlights the need for the abstractive summarization step. The goal of this step was to shorten the text and keep only the most important sentences.
## 4 Discussion
### Discomfort Rating and Preference  
We conducted a study on comfort and preference by using quantitative (e.g., the CR) and qualitative (e.g., choice question) methods. Some previous studies have yielded consistent results by combining the subjective rating values and survey questions, for example, [PERSON] et al. who investigated muscle activity during hip abduction and abduction movements [34], and [PERSON] et al. who asked the subjects to adjust the seating positions to their personal preference for given activities [35]. However, some studies have reported the opposite results. [PERSON] recruited 42 subjects from different occupations to experience two different office chairs, a standard typist (ST) chair or a newly designed prototype multiposture (PMP) chair [5]. They found that discomfort ratings indicated that the ST chair was better, but the preference survey showed a majority chose the PMP chair. [PERSON] et al. found that most subjects (72%) felt that the continuous passive lumbar motion system (CPLMS) reduced back discomfort based on a preference survey, but failed to show a significant effect of CPLMS on discomfort of the upper back from results of discomfort ratings [36].  
We found that the differences between all speeds were evident in the preference results, whereas they were not significant in the group data on the CR100 scale. The preference survey showed that most subjects preferred the \"fast\" condition because they experienced the external force for shorter durations than the \"slow\" condition. The external force might cause \"discomfort\" associated with the pain, tiredness, soreness, and numbness. A possible reason for the non-significance in discomfort rating could be that the CR100 scale, although precise, was not suitable for all subjects. Subjects without sufficient training might not adapt to the relatively small scales with fine increments (e.g., CR100 scale with one increment) for discriminating relatively small subjective intensities (most of which were less than 40) in this study [37].
## 1 Introduction
Similarly, the temperature fields are simplified as constant or linear fields, which may result in inaccurate analytical results--especially in the case of silencers with complicated temperature and flow velocity distributions. In light of the aforementioned disadvantages, this paper proposes an approach based on CFD results to investigate the acoustic attenuation performance of silencers. This approach comprises the following steps: (1) the flow field inside the silencer is calculated by performing a 3D steady computation using CFD; (2) the CFD results--including temperature and air flow velocity--are transferred to acoustic meshes by mesh mapping, so that the results can be used as a boundary condition of sound field analysis; (3) the _TL_ of the silencer is calculated at a high temperature and air flow. Compared to previous works, the proposed approach avoids the use of simplified temperature and flow velocity distributions. Consequently, the effects of temperature variation and air flow on the acoustic attenuation performance of silencers can be calculated and observed in greater detail. This paper proceeds as follows: Following the introduction, Section 2 describes the proposed computational approach and verifies its accuracy. Section 3 builds an internal fluid model of a perforated tube silencer of a car, and generates CFD meshes and acoustic meshes of the model, respectively. Following this, a 3D steady computation using CFD is performed in order to calculate the pressure loss of the silencer and obtain its temperature and flow velocity distributions. Section 4 employs the proposed approach to investigate the acoustic attenuation performance of the silencer under the influence of different temperatures and air flow. Section 5 concludes the study.
## 2 Materials and Methods
### Data and Pre-Processing  
The data information used in this article is shown in Table 1. The LAI time-series data, with a spatial resolution of 500 m and temporal resolution of 8 days, from 2000 to 2018 were accessed from the GLASS product [24] ([[http://glass-product.bnu.edu.cn/](http://glass-product.bnu.edu.cn/)]([http://glass-product.bnu.edu.cn/](http://glass-product.bnu.edu.cn/))) on 20 October 2021, which has been validated using a large number of in-situ observations and has been successfully applied in eco-hydrological research around the world [25]. The 8-day GPP time series from 2000 to 2018 was obtained from the Penman-Monteith-Leuning (PML\(\_\)V2) product, with the same resolution as the LAI data [26] ([[https://data.tpdc.ac.cn](https://data.tpdc.ac.cn)]([https://data.tpdc.ac.cn](https://data.tpdc.ac.cn))). This product has been proven to be noticeably better than most globe GPP products through  
Figure 1: Geographic map of China showing the different regions defined in this study and the flux stations used in this study.  
comparison with field observations from flux stations [27] throughout the world. The land use data for 2000 and 2020 were obtained from the GlobeLand30 maps (30 m) produced by the National Geomatics Centers of China ([[http://www.globallandcover.com](http://www.globallandcover.com)]([http://www.globallandcover.com](http://www.globallandcover.com))), accessed on 8 January 2022. The land cover in 2020 was regarded as that in 2018 to match the period of the LAI and GPP data. While there was a small time difference between 2018 and 2020, the land cover was nearly temporally stable during this short period. The GlobeLand30 consists of forest, shrubland, grassland, cropland, tundra, water bodies, wetlands, artificial surfaces, bare land, and permanent snow and ice. In this study, the tundra category was merged with the grassland category, and we further combined the water body, wetlands, and permanent snow and ice categories into a new category. Then, we obtained the hourly TEMP, PRCP, and SR at a 0.25\({}^{\circ}\) resolution from the ERA5 reanalysis product for 2000-2018 ([[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu))), accessed on 2 May 2022. All of the time-series datasets for the LAI, GPP, and climate variables were temporally aggregated at the annual scale for use in the subsequent analysis.  
We collected carbon flux observation data from eight flux stations of the FLUXNET2015 Observation and Research Network ([[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/))) to evaluate the accuracies of the GPP estimates of the PML_V2 product across China on the 8-day and annual scales. The flux stations used in this study are listed in Table 2.
## 3 Timing Predictability in CPSs
### Predictability in Time-Critical CPSs  
The time-critical embedded systems represent only one component of a time-critical CPS. Therefore, the span of timing predictability in the CPS should extend the boundaries of embedded systems to include the timing predictability impact of sensors and actuators that are deployed in the physical processes. The sensor values in the running example of the autonomous quarry can arrive from the other haulers, RSUs or the remote control centre. In this regard, we identify the following two key parameters [27].  
1. _Ready Time of Inputs:_ The ready time of inputs in the CPS is referred to as the interval of time between the instant when the value of a sensor that is deployed in the physical process changes and the instant when the changed value appears at the input interface of the embedded system. The ready time of all sensor inputs coming from the physical processes should be timing predictable.
2. _Ready Order of Inputs:_ In the case of more than one sensor value arriving at the input interface of an embedded system, the computed output that controls the physical processes depends heavily on the arrival order of the inputs. The desired function of the CPS requires a specific arrival order of the inputs from the sensors that are deployed in the physical processes, which must be timing predictable. This is referred to as the order predictability.  
It is interesting to note that these two parameters are not considered in the definition of timing predictability for embedded systems as they exist outside the interfaces of these systems. A CPS is considered to be timing predictable if it can be shown, proven or demonstrated that all the specified timing requirements are satisfied at the design time. These requirements include the timing requirements on the computations and communications as well as the timing requirements that constrain the ready time and ready order of the inputs. If a CPS offloads computations to the cloud, the definition of timing predictability should additionally consider the timing impact of the offloaded computations [28]. We argue that, even if a time-critical CPS is proven to be timing predictable at the design time, the predictability of the system can be jeopardised at runtime due to security threats to the time-critical data entering the system, e.g., from sensors, networks or other CPSs. Therefore, security of the data is integral to the timing predictability of the system. The security aspects are discussed in the next sections.
# Present and Future Marine Microwave Satellite Missions in China
## 3 Progress History of China's Marine Microwave Satellite Remote Sensing Technology  
With the successful application of the SZ-4 [36] multi-mode microwave remote sensor, China's marine microwave (dynamic environments) remote sensing has been gradually developed. However, most of the past development has focused on the introduction and learning of the existing global algorithm models and independent innovation has been lacking. Since China launched the HY-2A marine dynamic environment satellite on 16 August 2011, marine microwave remote sensing technology has been rapidly developing, and many innovative results have been achieved in the development and applications of algorithms. The successful launch of the HY-2B satellite on 25 October 2018 marked the transition of China's marine dynamic environment satellites from research to operations. The marine dynamic environmental elements now mainly include sea surface wind fields, waves, oceanic currents, temperatures, salinity, and others. At the present time, data regarding all the aforementioned elements can be obtained through remote sensing, and the accuracy has been gradually improving.
## 3 Methodology
### Model Interpretation  
The significance of model interpretation is to improve the transparency and credibility of deep learning models, facilitate understanding of the model's decision-making process and logic, detect whether the model is influenced by biases in the training data, and judge the reasonableness of the results.  
In this study, SHAP is used to explain the Inception model. The SHAP method aggregates local explanations into global explanations, which can better explain the contributions of factors to landslide susceptibility [38]. SHAP draws on the concept of Shapley values from economics, which quantifies the marginal contributions of features to model  
\begin{table}
\begin{tabular}{c c c} \hline
**Evaluation Metric** & **Variable Meaning** & **Formula Number** \\ \hline \(OA=\frac{TP+TN}{TP+TP+TN+FN}\) & TP (True Positive) represents the number of landslide samples & (a) \\  & correctly classified; FP (False Positive) represents the number of & \\ \(TPR=\frac{TP}{TP+FN}\) & landslide samples incorrectly classified; TN (True Negative) & (b) \\  & represents the number of non-landslide samples correctly classified; & \\ \(FPR=\frac{FP}{FP+TN}\) & FN (False Negative) represents the number of non-landslide samples & (c) \\  & incorrectly classified. & \\ \(MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\left(T_{i}-P_{i}\right)\) & & (d) \\  & \(T_{i}\) represents the true value of the \(i\)-th sample, and \(P_{i}\) represents the & \\ \(RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(T_{i}-P_{i}\right)^{2}}\) & predicted value of the \(i\)-th sample. & (e) \\ \hline \end{tabular}
\end{table}
Table 4: Descriptions of accuracy evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model Name** & **BPNN** & **Inception** & **ResNet** & **CNN** & **VGG-16** \\ \hline Input Shape & & & (47,47,15) & \\ Hidden Layer Structure & None & Convolutional Layer + Pooling Layer & \\ Convolution Kernel Size & None & & \(5\times 5\) & \(3\times 3\) \\ Pooling Kernel Size & None & & \(3\times 3\) & \(2\times 2\) \\ Activation Function & & ReLu & \\ Learning Rate & & 0.001 & \\ Epoch & & 25 & \\ Optimizer & & Adam & \\ Loss Function & & Categorical Crossentropy & \\ Classification Function & & Softmax & \\ \hline \end{tabular}
\end{table}
Table 3: Model parameters.  
predictions. The SHAP library in Python provides rich visualization functions, such as \"summary_plot\" and \"force_plot\", which intuitively display the contributions of different landslide factors to the model output.
# ndInIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks
## 3 ICN Architecture for IoT Environments
### IoT Characteristics  
A typical IoT scenario for Smart Cities is shown in Figure 2. Sensor information collection units, also denoted as Data Collecting Units (DCUs), widely disseminated in the City, continuously collecting the environmental information to be transmitted to the Cloud. Each DCU is equipped with a large environmental monitoring sensor set that aims to, for example, collect relevant information about the environment condition in dense urban scenarios. The collected information are exposed through services and consumed by services installed in the Cloud [24], responsible for the organization of the information and making them available for user applications, such as dashboards.  
Spread across the city, DCUs generate a brutal amount of information. Theoretically, within the ICN concept, each reading of each sensor should receive an Interest from each interested consumer, resulting in a flood of Interest packets that would inevitably cause the network overload. However, some features of the IoT environment can be useful when overcoming this challenge. A host can manage multiple DCUs, which in turn can contain multiple services that handle the various sets of sensors. In this way, it is possible to treat this whole set as a collection point and thus have a unique identification for the point (prefix name).  
The collection of information from the sensors can be performed on demand for specific cases, but in general, it is done continuously. A subscription Interest system allows minimization of the amount of Interest packets on the network. However, such subscription system does not solve the caching flood caused by huge amount of Data packets. In general, the data from sensors are intended to Cloud-based applications, and for that reason they are not useful for others ICN nodes. Thus, the caching system must have a special role to treat data from IoT sensors.  
Figure 1: Packet forwarding in NDN.  
It is expected that IoT applications can make optimizations, such as aggregating the collected data and transmitting them in a single data block using the network infrastructure more efficiently. However, to do so, they require a special service from the network. For IoT environments, it is important that the network provides a service for which applications can operate in publisher-subscriber (pub-sub) mode. A pub-sub service with simple interfaces allows not only standardization but also facilitates the interoperability between applications. In addition, optimizations at the network level improves the scalability of the entire infrastructure. The ndnIoT-FC architecture here proposed, presented in the next section, was designed to address these challenges.
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Timepoint** & **Crestal Bone Level** & \begin{tabular}{c} **Mean \(\pm\) SD** \\ **Value in mm** \\ \end{tabular} & \(p\)**-Value 1** \\ \hline \begin{tabular}{c} Surgery/Provisional \\ (N = 34) \\ \end{tabular} & Mean Bone Level & 0.51 \(\pm\) 0.49 & \\ \hline \begin{tabular}{c} 6 Months \\ (N = 31) \\ \end{tabular} & Mean Bone Level Change from & \(-\)0.40 \(\pm\) 0.48 & \(p\) = 0.00 \\ \hline \begin{tabular}{c} 1 Year \\ (N = 25) \\ \end{tabular} & Mean Bone Level Change from & \(-\)0.05 \(\pm\) 0.23 & \(p\) = 0.28 \\ \hline \begin{tabular}{c} 2 Year \\ (N = 25) \\ \end{tabular} & Mean Bone Level Change from & \(-\)0.01 \(\pm\) 0.34 & \(p\) = 0.89 \\ \hline \begin{tabular}{c} 3 Year \\ (N = 25) \\ \end{tabular} & Mean Bone Level Change from & \(-\)0.03 \(\pm\) 0.33 & \(p\) = 0.69 \\ \hline \
\begin{tabular}{c} 4 Year \\ (N = 22) \\ \end{tabular} & Mean Bone Level Change from & \(-\)0.05 \(\pm\) 0.36 & \(p\) = 0.56 \\ \hline
\begin{tabular}{c} 5 Year \\ (N = 22) \\ \end{tabular} & Mean Bone Level Change from & \(-\)0.01 \(\pm\) 0.36 & \(p\) = 0.85 \\ \hline \hline \end{tabular}  
* Analyses were conducted using a paired \(t\)-test. The study population for the bone loss value dataset had several missing values from follow-up visits, which is why there is a slight inconsistency in sample size between the crestal bone level dataset and the survival analysis dataset.  
\end{table} 
Table 2: Changes in Crestal Bone Levels Over the 5-Year Follow-Up Period. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Month of** & **No. of** & **No. of** & **Survival** & **95\% CI** \\
**Follow-Up** & **Implants at Risk** & **Failed Implants** & **Estimate** & **Survival Estimate 1** \\ \hline
1 Month & 37 & 0 & 1.000 & (1.0000, 1.0000) \\
2 Month & 36 & 1 & 0.9722 & (0.8187, 0.9960) \\
3 Month & 35 & 0 & 0.9722 & (0.8187, 0.9960) \\
6 Month & 35 & 0 & 0.9722 & (0.8187, 0.9960) \\
12 Month & 31 & 0 & 0.9722 & (0.8187, 0.9960) \\
18 Month & 30 & 0 & 0.9722 & (0.8187, 0.9960) \\
24 Month & 30 & 0 & 0.9722 & (0.8187, 0.9960) \\
36 Month & 28 & 0 & 0.9722 & (0.8187, 0.9960) \\
48 Month & 26 & 0 & 0.9722 & (0.8187, 0.9960) \\
60 Month & 25 & 0 & 0.9722 & (0.8187, 0.9960) \\ \hline \hline \end{tabular}  
* The Kaplan-Meier method was used to calculate cumulative implant survival. One implant failed within 2 months after loading.  
\end{table} 
Table 3: Kaplan-Meier Survival Analysis: 5-Years of Follow-Up. There were no other failures. All 25 implants that completed 5-years of follow-up were surviving and clinically stable, with no indicators of pain, mobility, radiolucency, or clinically meaningful crestal bone loss.