esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Preparation of the Influent Water  
The raw water was collected from Jing-Si Lake in Pingtung, Taiwan. The characteristics of the raw water are shown in Table 1.  
With the aim of investigating the feasibility of using a CW for treating water with different initial organic concentrations, synthetic water was prepared by adding HA stock solution (1000 mg/L) with different volumes into raw water to get three initial DOC concentrations, namely 3.0-4.5 mg/L, 4.5-6.0 mg/L and 10-12 mg/L. Analytical-grade HA, purchased from Sigma-Aldrich (Steinheim, Germany), was used as a simulant. This kind of HA contains approximately 3.1 mmol/g carboxylic groups. The stock solution of HA at 1000 mg/L was prepared by mixing 1 g of HA with 62.5 mL of NaOH (2 N) solution, because HA dissolves well in alkaline conditions, and then dissolved to 1 L with deionized water (DI water) in a 1-L volumetric flask [5]. After that, this solution was put in a shaker to agitate substances in a flask and then the stock solution was filtered through a 0.45 \(\upmu\)m membrane filter to remove the residual non-dissolved humic acid powder. Finally, the pH of HA solution was adjusted to around 8.0 and this stock solution was kept in the dark in the fridge at the temperature at 4 \({}^{\circ}\)C in maximum two weeks.  
With the purpose to evaluate the removal efficiency of varying TC concentrations by using CW, the TC stock solution was added into raw water to get three levels of input TC concentrations (1, 5 and 10 mg/L). The stock solution of TC (1000 mg/L) was prepared by dissolving 0.1 g tetracycline hydrochloride (Sigma-Aldrich, China, purity \(>\) 95%, kept in \(-\)20 \({}^{\circ}\)C) in 100 mL DI water [20]. After one hour of agitation, TC stock solution was filtered through a 0.45 \(\upmu\)m membrane filter and then stored in amber bottles at 4 \({}^{\circ}\)C in the fridge.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Unit** & **Raw Water** \\ \hline pH & - & 7.50 \(\pm\) 0.52 \\ Alkalinity & mgCaCO\({}_{3}\)/L & 88.75 \(\pm\) 5.00 \\ UV-254 & cm\({}^{-1}\) & 0.089 \(\pm\) 0.030 \\ DOC & mg/L & 3.19 \(\pm\) 0.64 \\ sCOD & mg/L & 8 \(\pm\) 2 \\ NH\({}_{3}\)-N & mg/L & 0.127 \(\pm\) 0.017 \\ PO\({}_{4}\)-P & mg/L & 0.024 \(\pm\) 0.011 \\ NO\({}_{3}\)-N & mg/L & 0.72 \(\pm\) 0.25 \\ Tetracycline & mg/L & Not detected \\ \hline \end{tabular}
\end{table}
Table 1: Water characteristics of Jing-Si Lake.
## 2 Materials and Methods
### Materials
#### 2.1.2 Chemicals  
Poly(lactide-co-glycolide) (Purasorb(r) PLDG 5002-PLGA Ratio L/G % 50:50; molecular weight 45.000-75.000 Da) was supplied by Purac Biomaterials (Gorinchen, The Netherlands) and oleic acid by FlukaChemika (Buchs, Swiss). Poly-\(\epsilon\)-caprolactone's molecular weight is 14.000 g/mol (PCL), Pluronic(r) F127 (PF127), collagenase (Coll) from _Clostridium histolyticum_ type IA, 2,2-diphenyl-1-picrylhydrazaryl (DPPH), Dulbecco's Modified Eagle's Medium--high glucose (DMEM), ascorbic acid, quercetin, Thiazolyl Blue Tetrazolium Bromide (MTT), (\(\pm\))-6-Hydroxy-2,5,7,8-tetramethyl-chromane-2-carboxylic acid (Trolox), 2,2'-azobis(2-amidinopropane) dihydrochloride (AAPH), fluorescein sodium salt, hydrogen peroxide solution (H\({}_{2}\)O\({}_{2}\)), and Iron(III) chloride (FeCl\({}_{3}\)) were purchased from Sigma Aldrich (Steinheim, Germany). Roswell Park Memorial Institute (RPMI) 1640 medium, Iscove's Modified Dulbecco's Medium (IMDM--GlutaMAX(tm)), and PenStrep were acquired from Gibco (Carlsbad, CA, USA). Fetal bovine serum (FBS) was supplied by Biowest (Riverside, CA, USA). PrestoBlue(r) Cell Viability Reagent was obtained from Molecular Probes, Invitrogen (Waltham, MA USA). L-glutamine was supplied by Lonza (Leuven, Belgium). Rutin was acquired from Extrasynthese (Genay, France). Ketamine (Imalgene(r) 1000) was purchased from Merial (Lyon, France), medetomidine (Medetor(r), 1 mg/mL) from CP-Pharma (Handelsges, Germany). Commercial diclofenac sodium gel (23.2 mg/g) was acquired in a local pharmacy. Carbopol 940(r) was purchased from Fagron (Barcelona, Spain). Epigallocatechingallate (EGCG) and \(N\)-[3-furyl-acrylolyl]-Leu-Gly-Pro-Ala (FALGPA) were purchased from Panreac (Barcelona, Spain). Tricine buffer was purchased from VWR (Leuven, Belgium). Folin reagent and uranyl acetate were supplied by Merck (Darmstadt, Germany). High performance liquid chromatography (HPLC) grade acetonitrile and formic acid were obtained from Chem-Lab NV (Zedelgem, Belgium). Milli-Q water (18.2 M\(\Omega\) cm\({}^{-1}\) resistivity) was obtained from a Millipore-Direct Q3 UV system (Millipore(r), Burlington, MA, USA). All other chemicals were of analytical grade.
## 4 Fiber Sensing Technologies  
Fiber optic sensors have proven themselves in harsh environments, presenting high sensitivity, wide bandwidth, high operation temperature, immunity to electromagnetic interference, lightweight, long life, and multipoint as well as remote measurement capabilities. In view of these characteristics, fiber optic sensing technology was selected for the build-up of the coal waste pile monitoring system, which has two components, one relative to the emission of methane (CH\({}_{4}\)), ammonia (NH\({}_{3}\)), and carbon dioxide (CO\({}_{2}\)) in certain points of the waste pile, and the other associated with distributed temperature measurement.
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## Appendix A Machine Learning Models and Hyperparameters Optimization
### Gaussian Process Regression  
The Gaussian process regression (GPR) model is a nonparametric kernel-based probabilistic machine learning method. It is capable of making predictions that take into account prior knowledge (kernels) and offering uncertainty measures for predictions. The selection of the kernel function has a significant impact on how well the Gaussian process performs. There are several standard kernel functions such as the squared exponential kernel, rational quadratic kernel, Matern kernel, and exponential kernel. In the present research, the most optimal kernel function is found to be the Matern kernel function (as shown in Equation (10)) with a \(\
u\) of 5/2 after hyperparameter tuning.  
\[k(x,x^{\prime})=\frac{2^{1-\
u}}{\Gamma(\
u)}\Bigg{(}\frac{\sqrt{2\
u}\|x-x^{ \prime}\|}{l}\Bigg{)}^{\
u}K_{\
u}\Bigg{(}\frac{\sqrt{2\
u}\|x-x^{\prime}\|}{ l}\Bigg{)} \tag{11}\]  
where \(l\) is the length scale parameter, \(\
u\) is the smooth factor, \(\Gamma\) is the Gamma function, and \(\Gamma(\
u)\) is the Bessel function.
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 5 Case Study
### Relationship between Inertia Constant and ESCR  
In this subsection, we discuss the relationship between the inertia constant and AC system robustness based on (22). Additionally, the ESCR is affected by the AC-rated capacity and DC-rated active power. In other words, the total capacity of offshore wind turbines directly affects the rated active power of the MMC-HVDC. As the capacity of each type-4 wind turbine is fixed, the number of generators that penetrate the system determines the transmission power. As shown in Figure 8, we provided an ESCR ranging from weak to strong (e.g., ESCR of 1 to 4), an inertia constant, and the number of wind turbines infiltrating the system.  
In Figure 11, it can be observed from the results that the value of the inertia constant depends to a certain extent on the value of the ESCR, which maintains the same DC voltage level. If the number of wind turbines infiltrating the grid is reduced, then the inertia of the system increases. Therefore, as the capacity of a single motor is fixed, the penetration level of the wind turbine will also present great challenges to the stability of the grid.  
Figure 10: ESCR is 3.5 for 20% load decrease: (**a**) DC voltage; (**b**) SG active power; (**c**) SG rotor speed; (**d**) AC system frequency; (**e**) MMC active power.
## 3 Materials and Methods
### Data Organization  
Data initially received from the rescue station was collected through 80 different files in HTML format. Much repetitive and ambiguous information was present in the dataset without any significance for the research. Hence, after an initial data sorting, we removed some attributes like the address of the patients, type of rescue wagon, timestamp information, after-rescue hospital details, or type of treatment given to a patient. A data dictionary software was developed using Python to decipher rescue-related words and record them in an organized way for future reference. The dictionary also explains all the acronyms and data types within the database. We also created a Python-based algorithm to merge all the records provided by the rescue station in a single file, where redundant data columns were removed. We considered the rescue case IDs as master keys and combined unique information of duplicate cases in their respective data cells. The raw database contained a lot of null values and sometimes included information that has no significant importance for the research.
## 1 Introduction  
### Motivation  
The demand for ubiquitous mobile services in an intelligent transportation system (ITS) is ever increasing to improve road safety and transport management [1]. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications have attracted significant attention in vehicular networks [2, 3]. V2V allows vehicle users to communicate directly within a group. This not only decreases the path loss by reducing the distance, but is also appropriate for many vehicular applications, including hazard warnings, path planning, vision sharing, and platooning [4, 5]. V2I enables vehicle users to access roadside units (RSUs) for downloading or uploading data, which improves the stability of communication for vehicular users (VUs) with high mobility. Therefore, the efficiency and reliability of a vehicular network can be improved significantly by the cooperation between V2V and V2I networks [6].  
Interference represents a problem in vehicular networks due to the increase in the density of VUs [7]. Resource allocation is a promising approach for interference management [8]. However, as a result of the rapidly growing demand for services, radio resources are scarce [9]. Thus, not all communication links can be allocated to a resource without suffering interference in dense networks. To guarantee the quality of services, the set of served services at which the scale of demand exceeds the service capability of the network has to be determined. To increase the number of guaranteed services, the resource allocation has to be adjusted to adapt to the dynamic environment of the vehicular network. However, frequent and uncontrolled resource re-allocation may bring about a large number of control signaling exchanges. Our goal in this paper is to devise a low-cost resource re-allocation scheme that reduces the re-allocation rate and increases the number of guaranteed services.
## 2 Overview of Privacy Challenges and Disclosure Risks in Organisational Data Sharing
### Organisational Privacy Disclosure Risks  
In today's data-driven world, where organisations rely on exchanging high quantities of data with each other to gain valuable understandings, there are challenges in sharing that data, such as privacy risks of disclosure. Privacy risk refers to the potential threats posed to personal information, which can eventually disclose the information of individuals. In the context of organisational data sharing with third-party entities, the potential privacy disclosure risks could take two forms, such identity disclosure and attribute disclosure.  
Identity disclosure [27] refers to the risk of re-identification of individual's identities from shared data. The privacy risk arises when personal data are shared with third-party organisations, revealing combinations of different attributes (QID), allowing the adversaries to recognise the data records by profile-mapping the QID together. The attribute disclosure [28] is achieved by exposing specific QIDs of individuals that were supposed to remain confidential. When data are shared, it might contain sensitive attributes such as medical conditions or financial status. These attributes, although may not directly identify the individual's identities, can still lead to privacy breaches if linked to other publicly available information or combined with other attributes to create a detailed profile.  
Two of the main privacy disclosures, identity and attributes, have helped researchers to study many approaches to reduce privacy risks; however, it is important to understand membership disclosure, which could help researchers and regulators to identify privacy risks in organisational data sharing. Membership disclosure [29] occurs when an individual's affiliation to a particular group is revealed without their consent. Usually, this disclosure occurs from data modelling or analysis when an adversary comes to an understanding of an individual's presence in a dataset.
# Hybrid Deep Reinforcement Learning for Pairs Trading
## 4 Hybrid Deep Reinforcement Learning for Pairs Trading
### State Representation Learning  
State representation is crucial for reinforcement learning. To observe historical spread movements, our SRL model takes a sliding window of preprocessed data, as shown in Figure 3. To adaptively select features, we assign a weight to each feature using the gate structure [34]. The gate \(g\) shown in Figure 5a is defined as in Equation (20) where \(f\) is the input feature vector, \(W\) and \(b\) are parameters learned by end-to-end training, and \(\sigma\) is a sigmoid activation function. Using gate \(g\), we generate the weighted feature vector \(f^{\prime}\) from the element-wise multiplication (\(\odot\)) of vectors \(g\) and \(f\) in Equation (21).  
\[g=\sigma(W\!\cdot\!f+b) \tag{20}\]  
\[f^{\prime}=g\odot f \tag{21}\]  
The LSTM network layer shown in Figure 5b takes a sliding window of weighted feature vectors as the input and outputs the hidden state of the last time step. We employ the LSTM network because it can effectively capture the long-term dependency in the time series and has shown superior performance in stock price prediction over other types of deep neural networks and traditional machine learning algorithms [49; 50; 51]. We refer to the network up to the LSTM layer as the SRL network. The output of the LSTM layer is used in reinforcement learning, as well as in a regression network that predicts the next closing spread to provide accurate state information. The predicted spread does not participate in reinforcement learning, but the underlying SRL network does because it is shared with the actor network, as explained in the next section.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Group** & **Features** \\ \hline Candlestick components [33] & Lengths of the upper shadow line, lower shadow line, and body; body color \\ \hline \multirow{3}{*}{Overlap studies [48]} & BBANDS, DEMA, EMA, HT-TRENDLINE, KAMA, MA, \\  & MAMA, MIDPOINT, MIDPRICE, SAR, SAREXT, SMA, T3, \\  & TEMA, TRIMA, WMA \\ \hline \multirow{3}{*}{Momentum indicators [48]} & ADX, ADXR, APO, AROONOSC, BOP, CCI, CMO, \\  & DX, MACD, MACD, MACDET, MACDFIX, MFI, MINUS\_DI, \\ \cline{1-1}  & MINUS\_DM, MOM, PLUS\_DI, PLUS\_DM, PPO, ROC, \\ \cline{1-1}  & ROCP, ROCR, ISI, STOCH, STOCHF, STOCHRSI, TRIX, \\ \cline{1-1}  & ULTOSC, WILL \\ \hline \multicolumn{2}{c}{Volume indicators [48]} & AD, ADOSC, OBV \\ \hline \multicolumn{2}{c}{Volatility indicators [48]} & ATR, NATR, TRANGE \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input features.  
Figure 4: Data preprocessing.
## 2 Methodology: PartsNet
### General Data Dimensionality Reduction Network (GDR-Net)
The first term on the right-hand side corresponds to the reconstruction term, which maximizes the log-likelihood function \(\log p_{\theta}(x|z)\) to measure the similarity between the reconstructed data and the original input. The second term is the Kullback-Leibler (KL) divergence term, which minimizes the KL divergence between distributions \(q_{\varphi}(z|x)\) and \(p(z)\) to regularize the distribution of latent variables, ensuring that the learned distribution becomes closer to the prior. During the training process, a multilayer perceptron structure is employed. Binary Cross-Entropy Loss (BCELoss) is utilized to measure the discrepancy between the predicted data and the original data in terms of labels. \[\mathcal{L}_{2}=-(y_{label}\cdot\log(p)+(1-y_{label})\cdot\log(1-p)) \tag{5}\]Here, \(y_{label}\) represents the actual quality of the sequence data, which is typically the output of a simulation process, and \(\log(\cdot)\) represents the probability predicted by the model that the data are a positive sample. GDR-Net achieves the learning process by minimizing the negative ELBO and BCELoss. \[\theta^{*},\varphi^{*}=\text{argmin}_{\theta,\varphi}-(\mathcal{L}_{1}(\theta, \varphi;\ x)+\mathcal{L}_{2}) \tag{6}\]  
While a VAE can achieve data dimensionality reduction, it cannot explicitly express the weights of different dimensional features on the results. In this study, a principal component ordering model-based PCA is utilized to handle the features learned in the latent space. This combined approach ensures that GDR-Net can capture crucial features from high-dimensional data and further determine their weights.
# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 2 Related Works
### Cross-View Deep Learning Approaches  
Cross-view-based approaches estimate the latent relationship between UAS-collected imagery and the reference orthophoto available from, for instance, Google Earth. In Ref. [7], [PERSON] et al. proposed CVM-Net for the cross-view-based ground-to-aerial geopositioning task; while this approach was not proposed for UAS, one can apply the method as is to UAS positioning. CVM-Net uses the Siamese architecture to extract feature maps from acquired and reference imagery to perform a query-to-target search strategy. [PERSON] et al. [14] proposed a cross-view geopositioning method including a Scene Localization Network and a Camera Localization Network achieving UAS pose estimation from acquired UAS images and satellite images. [PERSON] et al. [8] introduced the MSBA network architecture to extract landmarks from different views using a multi-branch structure. The network uses a single UAS image and satellite image gallery as input and estimates the UAS geoposition.  
The cross-view approaches use attention mechanisms [15] within transformer architecture [16] to generate feature maps for the image; they significantly increase the computational cost for real-time applications running on the edge. The learned networks for the images learn the latent view geometric relations; hence, a slight UAS displacement may result in different feature maps and, consequently, mismatching of cross-view images. In addition, underlying neural networks trained for one site do not transfer to new areas and require retraining. The proposed approach in our paper does not require retraining and can adapt to new flight plans in unseen environments by replacing the look-up database of ground landmarks. The addition of the attitude control module allows UAS to undergo rotation and altitude fluctuations.
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 5 Experiments and Discussion
### Experiments
#### 5.4.3 Test on LOCATA Dataset  
We also tested the performance of methods on the real-world dataset LOCATA.  
Given the priority of accuracy, the complex-valued neural network methods with better performance were included in the experiment: CV-CNN, CVREAN, and MF-AMnet. Table 3 shows the results of Task 1 and the results of Task 2 are shown in Table 4.  
Compared to simulated data, real-world data have more interference from other factors, such as noise from the microphone, vibration of sound sources, etc., so the demands on the algorithm are higher. Mean deviation and standard deviation are indicators of the stability of the algorithm. All the methods had low metrics, indicating that the CVNN has pretty good stability. Among them, our method achieved a score of less than \(3^{\circ}\) for both MAE and RMSE, showing that the MF-AMnet maintains this consistent performance even with real data. For the case of multiple sound sources, we also ran simulations and tests, and the results are shown in Table 4. It is well-known that neural network models are data-driven. The slightly worse test results for real data compared to simulated data may be due to the fact that the models used for testing were trained with simulated data and then fine-tuned with a small amount of real data. The degradation may be due to the fact that there is a certain amount of mutual interference between the two sound sources, especially in close proximity. But overall, MF-AMnet kept its leading performance in experiments, indicating that our algorithm has practical value and significance, even in the case of multiple sound sources. We randomly selected two of the recordings in Task 2, and Figure 5 shows the ground truth and estimates of the methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Acc5\({}^{\circ}\) (\%)** & **MAE (\({}^{\circ}\))** & **RMSE (\({}^{\circ}\))** \\ \hline CV-CNN & 85.64 & 3.039 & 3.685 \\ CVREAN & 88.78 & 2.779 & 3.693 \\ MF-AMnet & 92.31 & 2.216 & 3.209 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of methods on Task 2.  
Figure 5: The results of MF-AMnet and ground truth for recording 1 and recording 2 from LOCATA Task 2: (**a**) recording 1; (**b**) recording 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean Deviation (\({}^{\circ}\))** & **Standard Deviation (\({}^{\circ}\))** \\ \hline CV-CNN & 2.82 & 3.14 \\ CVREAN & 2.97 & 3.01 \\ MF-AMnet & 2.02 & 2.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of methods on Task 1.  
As shown in Figure 5, the estimations are very close to the ground truth. Even when the sound sources are relatively close to each other, our method distinguished them accurately and produced the most precise estimation. The results of Task 2 prove once again that MF-AMnet has a good practical value.