esa-sceva/satcom-chunk-collection huggingface dataset:
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 3 Results and Discussion
### Interference of Organic and Inorganic Matter on PFAS Removal
#### 3.3.3 Interference of Highly Concentrated NOM and Inorganic Matter on PFAS Removal  
The removal efficiency of PFHxS, PFOA, and PFOS was also studied in the presence of extremely high concentrations of both NOM (i.e., 50 ppm tann acid + 50 ppm humic acid) and inorganic matter (i.e., 250 ppm nitrate + 250 ppm sulfate) to check any possible impact on PFAS adsorption for MIEX(r) GOLD resin. Notably, 67.42, 94.35, and 91.00% removal of PFOA, PFOS, and PFHxS were found, respectively, under these conditions with an extreme abundance of competitive pollutants (Figure 5, Table 1). Among these three PFAS compounds, PFOA showed a significant drop in removal efficiency from >99.9% removal to 67.4%. In contrast, both sulfonic acid head-containing PFAS compounds showed >90% removal efficiency. These data indicate that competitive ion influence on PFAS adsorption to resin is more prominent for carboxylic acid head-containing PFASs compared to sulfonic acid head-containing PFASs because sulfonated PFAS has a greater affinity toward the resin. A similar competitive interference of HA to PFOA adsorption on MIEX resin (a product of Orica Watercare) was found by [PERSON] et al. [37]. They studied the removal efficiency of PFOA with increasing concentrations of HA from 0 to 4 ppm and observed a distinct decrease in the removal efficiency of PFOA from 93.9 to 74.0%. Such competitive interference results from the strong \(\pi\)-\(\pi\) interaction between HA and resin. Moreover, high concentrations of salts in solution can reduce the soluble PFAS compounds via the salt-out effect, which promotes the formation of PFAS micelles, eventually leading to the decline in PFAS removal by the resin [32].
## 2 Materials and Methods
### Experimental Details
#### 2.5.2 Data Processing Steps and Analysis
RINEX data collected by the UAV were used during the experiment to investigate the PPP-AR solution's ability to correct the camera position. The PRIDE PPP-AR data processing strategy is summarized in Table 2. Kinematic processing was applied to acquire position information at each epoch. The coordinates obtained with PPP-AR were imported to the images according to the timestamp info. In this study, the spatial accuracy of the solutions produced was determined by analyzing the differences between the measured coordinate and the model coordinates of the 12 CPs. The most common statistical method for determining accuracy is the root mean square error (RMSE) analysis. This method evaluates the difference between ground points with known coordinates (\(\chi_{G}\)) and those obtained from the orthomosaic or 3D model (\(\chi_{M}\)). \[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(\chi_{M_{i}}-\chi_{G_{i}}\right)^{2}}{n-1}} \tag{12}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Strategy** \\ \hline System & GNSS \\ Positioning Mode & Kinematic \\ Epoch Interval & 0.2 s (5 Hz) \\ Cut Off Elevation Degree & 7 \\ Ambiguity Resolution & Fixed \\ Mapping Function & VMF3 \\
2 nd Ionospheric Delay Model & Yes \\ Ambiguity Fixing Method & LAMBDA \\ ZTD Model & STO \\ Receiver Clock Model & White Noise \\ HTG Model & NON \\ Strict Editing & Disable \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PRIDE PPP-AR processing parameters. Figure 8: PALA station (on **left**) and distance (on **right**) from the study area. Evaluations of the horizontal, vertical, and 3-dimensional accuracy of the georeferenced points were conducted for every positioning method and model.
## 3 Materials and Methods
### GC-TOFMS Analysis of Volatile Compounds  
Each wine sample (4.0 \(\pm\) 0.1 g) was placed in a 20 mL vial which was then sealed with a PTFE-lined silicone septum. The extraction of volatile compounds from the wines was achieved through headspace solid-phase microextraction (HS-SPME). Prior to extraction, the samples were heated at 40 \({}^{\circ}\)C for 3 min and agitated at 700 rpm. Before each injection, the fiber was desorbed at 250 \({}^{\circ}\)C for 3 min in a desorption unit. Extraction was performed at 40 \({}^{\circ}\)C for 47 min using a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) SPME fiber of 50/30 \(\mu\)m thickness and 1 cm of length (Merck Co., New York, NY, USA). After extraction, the fiber was withdrawn from the vial and transferred to the injector of a gas chromatograph for thermal desorption of the analytes at 250 \({}^{\circ}\)C for 280 s and then subjected to a second desorption in the unit at 250 \({}^{\circ}\)C for 5 min.  
The GC system comprised an Agilent 6890A gas chromatograph (Agilent Technologies, Palo Alto, CA, USA) coupled with a Pegasus IV time-of-flight mass spectrometer (LECO Corp., St. Joseph, MI, USA). The column set consisted of a 30 m \(\times\) 0.25 mm \(\times\) 1 \(\mu\)m column with an Equity1 stationary phase (Supelco Co., Bellefonte, PA, USA) and a 0.5 m \(\times\) 0.1 mm guard column (Restek, Bellefonte, PA, USA). The separation of sample components was achieved using an optimized temperature program: the initial temperature was set at 28 \({}^{\circ}\)C and maintained for 2 min, then ramped at 4 \({}^{\circ}\)C/min to 190 \({}^{\circ}\)C, at 8 \({}^{\circ}\)C/min to 210 \({}^{\circ}\)C, and at 12 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, where it was held for 2 min. The total analysis time was approximately 47.8 min. The injector was operated at 250 \({}^{\circ}\)C in splitless mode. Helium (N6.0 grade) was utilized as the carrier gas at a constant flow rate of 1.0 mL/min. The transfer line was maintained at 250 \({}^{\circ}\)C. The ion source temperature was 250 \({}^{\circ}\)C, and the detector voltage was set to \(-\)1647 V. Ions in the \(m/z\) 35-450 range were analyzed at a relatively slow data acquisition rate of 10 spectra/s to increase method sensitivity. To ensure consistent sample preparation and dosing conditions, the apparatus was equipped with a multitask autosampler MPS II (Gerstel Co., Mulheim an der Ruhr, Germany).  
Manual data processing was conducted using the peak deconvolution program implemented in the ChromaTOF software (LECO Corp., version 4.51.6.0). Tentative identification of analytes was achieved through an MS library search using the NIST 2011 and Wiley 2010 libraries. A key criterion for peak selection was a comparative parameter that described the similarity of a particular chromatographic peak's mass spectrum with the spectrum from the database. The examples of GC-TOFMS chromatograms of the volatile fraction wine profiles in TIC mode are presented in the Supplementary Material as Figures S1-S3.
# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 4 Experiments
### Experimental Results and Comparison
#### 4.2.2 Results and Analysis  
The comparison was made using the MSE and MAE for forecast horizons set from the first hour to the 84 th hour. The forecast horizon is the length of time into the future for which forecasts are to be prepared. To avoid abundant observations, we hence only report a result at particular time steps, which are 1, 12, 24, 36, 48, 60, 72, and 84 h. In time series forecasting, larger horizons make forecast prediction more challenging. Thus, the experiments offer a detailed analysis of the results in this vast horizon. We compared the results of the proposed method with other algorithms (LSTM, GRU, and TCN) to prove the effectiveness of this approach. The best results (lower values are better) per method are highlighted in red. Table 5 reports the MSE and MAE values of predicted energy consumption on the CNU dataset (dataset 1) over time steps from baselines LSTM, GRN, TCN, and our proposed stride-TCNs. As clearly depicted in Table 5, our stride-TCNs steadily achieve the lowest errors between 60 h and 84 h. Notably, the stride-TCN with two layers model reaches the lowest prediction error of 84 h. Compared to the LSTM, GRU, heuristic-stride-TCN, and TCN baseline, our auto-stride-2 layers are 2.07%, 7.19%, 30.05%, and 32.7% better in terms of average relative errors, respectively. In the case of dataset 2, the TCN baseline has demonstrated its superior when substantially better than other models for short-time prediction, as in Table 6 However, the limitations become clear when experimenting on dataset 1, when our proposed architecture could not overcome baseline models, as shown in Table 7. This indicates the lack of complexity of the model to capture information well in a large dataset, as well known as the underfitting phenomenon. Overall, our proposed architecture achieved on-par results with other baseline models on time series forecasting, considering the error is slightly higher or even lower than the baselines' error in some specific settings. We obtain a significant reduction in model complexity with stride-TCNs, as seen in Table 4. Remarkably, our heuristic-stride-TCN for long-time forecasting (84 h) has approximately 6K parameters, which is only 1.6%, 5%, and 1% compared to the number of parameters from the baseline LSTM, GRU, and TCN, respectively. We also note that the model's complexity depends on the length of both history and forecast horizontal, so each model's complexity on different datasets is variant. Details are given in Table 8 (dataset 2), Table 9 (dataset 3) and Table 10 (dataset 1). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Method**}} & \multicolumn{1}{c}{\multirow{2}{*}{**1 h**}} & \multicolumn{1}{c}{\multirow{2}{*}{**12 h**}} & \multicolumn{2}{c}{**24 h**} & \multicolumn{2}{c}{**36 h**} & \multicolumn{2}{c}{**48 h**} & \multicolumn{2}{c}{**60 h**} & \multicolumn{2}{c}{**72 h**} & \multicolumn{2
# Using Autoregressive with Exogenous Input Models to Study Pulsatile Flows
## 1 Introduction
However, despite their differences, these two approaches have been widely used recently for simulating mitral or aortic valve issues by using 3D modeling configurations [29; 30; 31; 32; 33; 34; 35]. Furthermore, lumped models are another alternative to represent the dynamic behavior of a leaflet structure and its relation with the input flow. One of these models is the Windkessel Model (WM), which is an electric analogy that uses elements such as resistors, capacitors, inductors, and current sources to replicate parameters such as peripheral resistance, arterial compliance, the inertia of blood flow, and the input signal of the cardiac cycle [36; 37; 38; 39; 40; 41]. Given previous considerations, the underlying aim of this paper is to introduce the first attempt to apply an SI method--usually employed for signal analysis--to simulate the opening area and pressure difference outputs from an FSI simulation of two rectangular leaflets moving as a consequence of a pulsatile flow. First, the model has to be trained with existing data, and then, when the coefficients for the model are obtained, we use bilinear interpolation to create new coefficients for specific conditions and then simulate a new output. The main purpose is to help researchers still obtain an accurate trend of the phenomena studied, and it is not intended to substitute the current FSI simulation method, but to complement it when time is critical and only a few parameters change between a set of situations, such as the time, the frequency of the fluid, the elastic modulus, and so on. The dataset to train the model is retrieved from an FSI parametric study (which consists of variations in the mechanical properties of both the fluid and solid domain of the leaflet structure). It is essential to point out that this work does not represent an exact mimicking of the flow moving across a native heart valve, nor does it produce or use real clinical data. Besides, we are considering the simplest representation of a valve model to reveal the basics involved in the opening and closing stages of the leaflets. In this manner, the data we are using from the FSI simulations are the result of our simple leaflet structure and are only intended to be used as a reference point for our ARX model characterization and to guide future research. The content of this paper is as follows: In Section 2, the basic parameters to configure the FSI simulations and their respective results are given. Section 3 shows the SI method that uses the input and output signals of the FSI simulation to train the models that estimate each output of the system. Next, Section 4 presents the results of our model in comparison with the FSI approach. Then, with the parameters retrieved from the trained model, we use a new validation dataset to estimate both outputs and then compare them with its respective FSI simulations. Finally, Sections 5 and 6 present the discussion and conclusions of the presented methodology.
# Information Extraction of Cybersecurity Concepts: An LSTM Approach
## 1 Introduction
In practice, the Long Short-Term Memory (LSTM) neural networks became the preferred alternative for text processing using deep learning methods. LSTMs are a type of RNN and address the long-term dependency learning of general RNNs. The objective of this paper is to evaluate the LSTM-based neural network model in performing information extraction tasks for the domain of cybersecurity. To evaluate the performance of LSTMs for the NER tasks, we compare the performance of an LSTM-based model and a CRF-based model that are trained on the same corpus of vulnerability descriptions collected from various online sources. For RE, we compare three recent LSTM architectures in terms of their performance in information extraction. These architectures are \"LSTM along shortest dependency paths\", \"LSTM on least common ancestor sub tree\", and \"LSTM on sequences and tree structures\". To train the models, we auto generate an annotated training corpus using a bootstrapping algorithm [10] and generate a word embeddings model from the NVD. The models are compared in terms of training/testing accuracy, precision, recall, and F1 scores. The paper is organized as follows: Section 2 reviews the related work in the field. Section 3 provides an overview of the evaluated LSTM models. Section 4 describes the data used for training. The next section explains the data preprocessing needed for training. Section 6 covers the training methods for the models. The outcomes of the training and the discussion of the results are covered in Sections 7 and 8. Finally, Section 9 concludes the paper. Figure 1: Vulnerability description from the national vulnerability database (NVD).
# A Quantitative Detection Method for Surface Cracks on Slab Track Based on Infrared Thermography
## 2 Infrared-Thermogram Processing  
### Acquisition of the Infrared Thermography  
In this research, the Fluke IR camera (type: TIX620) was used to record the thermal image of slab surface. The key parameters of the camera are listed in Table 1. The experimental setup was presented in our previous research [5], as shown in Figure 2. The IR camera with a standard lens of 30 mm is installed on a metal bracket and the distance between the camera and slab surface is 0.75 m. The emissivity of the slab surface is 0.92.
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 3 Problem Definition  
### System Description  
We consider an SDDC architecture that is composed of SDN and SDC. Figure 1 shows an example of the SDDC, which consists of OpenFlow-enabled switches [41] and PMs. The network topology is based on a leaf-spine architecture, which is broadly used for layer 2+ switching in recent data centers [42]. All networking-related functionalities are monitored by an SDN controller, and all computing-related functionalities are monitored by an SDC controller. Since all of the data center resources are controlled by software-defined technologies, the infrastructure can be dynamically configured, and the necessary data for the proposed strategy can be easily obtained. Moreover, SDC enables VMs to be moved between different PMs with the support of a VM migration technique [43].  
The VMs on each PM are used for building different functionalities of cloud-based services such as processing business logic, data management, and user interface. Currently, most of the services are based on a microservice architecture, in which the different functionalities of a service are executed separately [44]. With this microservice architecture, a single software application/functionality is implemented as a combination of multiple microservices and their interactions. Hence, the communications between the functionalities and their coordination are vital for a successful realization of service deployment [45].  
Generally, a service in a data center is hosted on multiple VMs, the VMs have communication dependencies on each other. In Figure 1, there is one service running on PM\({}_{1}\) with nine VMs. Note that PM\({}_{1}\) hosts nine VMs, and the other PMs do not host any VMs. VMs of the same color are highly dependent on each other. This implies that the data traffic between those VMs is significantly higher than that between different VMs of different colors.
## 1 Introduction  
Quantum technologies are advancing very rapidly and have the potential to innovate communication and computing far beyond current possibilities. Among the possible platforms suitable to run quantum technology protocols, in the last decades quantum optics has received a lot of attention for the handiness and versatility of optical systems. In addition to studying the fundamentals of quantum mechanics, quantum optical states have been exploited for several applications, such as quantum-state engineering, quantum communication and quantum cryptography protocols, enhanced metrology and sensing, quantum optical integrated circuits, quantum imaging, and quantum biological effects. In this Special Issue, we collect some papers and also a review on some recent research activities that show the potential of quantum optics for the advancement of quantum technologies.
# ALPINE: Active Link Prediction Using Network Embedding
## 5 Experiments and Discussion  
To evaluate our work, we first studied empirically how partial network embedding with the modified CNE benefited the link prediction task. Then, we investigated the performance of ALPINE with the different query strategies qualitatively and quantitatively. Specifically, we focused on the following research questions in this section:  
* What is the impact of distinguishing an \"observed unlinked\" from an \"unobserved\" status of a node pair for partial network embedding?
* Do the proposed active learning query strategies for ALPINE make sense qualitatively?
* How do the different active learning query strategies for ALPINE perform quantitatively?
* How can the query strategies be applied best according to the results?  
**Data:** We used eight real-world networks of varying sizes in the experiments:  
1. The **Harry Potter** network (used also in Section 1) is from the corresponding novel. We used only the ally relationships as edges and its largest connected component, which yielded a network with 59 nodes for the most important characters and 218 ally links among them [11];2. **Polbooks** is a network of 105 books about U.S. politics among which 441 connections indicate the co-purchasing relations [40];
3. **C. elegans** is a neural network of C. elegans with 297 neurons and 2148 synapses as the links [41];
4. **USAir** is a network of 332 airports connected through 2126 airlines [42];
5. **MP_cc** is a Twitter network we gathered in April 2019 for the Members of Parliament (MP) in the U.K., which originally contained 650 nodes. We only used its largest connected component of 567 nodes and 49,631 friendship (i.e., mutual follow) connections;
6. **Polblogs_cc** is the largest connected component of the U.S. Political Blogs Network [40], containing 1222 nodes and 16,714 undirected hyperlink edges;
7. PPI is a protein-protein interaction network with 3890 proteins and 76,584 interactions [43], and we used its largest connected component **PPI_cc** of 3852 nodes and 37,841 edges after deleting the self-loops;
8. **Blog** is a friendship network of 10,312 bloggers from BlogCatalog, containing 333,983 links [44].
## 5 Discussion  
The quantitative simulation results are presented in Table 2. The brightness temperature of the ocean scene in X polarization and Y polarization is much higher than in T3 and T4 polarization. When the antenna pattern error is considered, the brightness temperature error of the reconstructed brightness temperature images of X polarization and Y polarization is larger than that of T3 and T4. Before the correction, the BT errors of \(T^{XX}\) and \(T^{YY}\) both exceeded 12 K, and the BT errors of T3 and T4 both exceeded 4 K. After correction by the two-step correction method, the BT errors of the four polarizations are all reduced. Especially, the correction effects of \(T^{XX}\) and \(T^{YY}\) are significant, with the error reduced to within 3 K. It must be noted that the second step of the two-step correction method is only used for X polarization and Y polarization. The results of T3 and T4 have only undergone the first step of correction. That is, T3 and T4 only corrected the antenna pattern additive errors, but did not correct the antenna pattern multiplicative errors. Therefore, the results in Figure 6a,b show a better correction effect.  
The FTT method focuses on correcting the antenna pattern multiplicative errors of the X polarization and Y polarization, without correcting T3 and T4. Consequently, the errors of T3 and T4 remain unaltered after the application of the FTT method. The results in Table 2 show that the BT error of the two-step correction method is smaller than that of the FTT method. The main differences between the two correction methods are as follows: (1) Compared with the FTT method, the proposed two-step correction method corrects not only the antenna pattern multiplicative errors but also the antenna pattern additive errors. (2) The FTT method requires a \"flat\" target for observation, and the flatness of the target will affect the correction effect. The FTT method is applicable to in-orbit aperture synthesis radiometers, which correct antenna pattern errors by observing the cold sky. The two-step correction method does not require observation of a specific target, and it is suitable for both in-orbit FPASR and ground-based FPASR.  
The two-step correction method proposed in this paper mainly focuses on correcting the brightness temperature in X-polarization and Y-polarization components, especially the second step of the correction method. For the third and fourth Stokes parameters, this method corrects only the antenna pattern additive error. The correction of the antenna pattern multiplicative errors for the third and fourth Stokes parameters requires further investigation.