esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Sorption Abilities of the Studied Materials  
Although the strong effect of phenol and trichlorophenol on the creation of pDVB porosity was observed, their molecular imprint was not reflected by the more selective or significantly better sorption properties. Figure 5 presents the results of the dynamic sorption studies performed using SPE method for phenolic compounds.  
Figure 4: The results of the nitrogen sorption experiments for the studied polymers. (**a**) N\({}_{2}\) adsorption-desorption isotherms; (**b**) Pore size distribution.  
The performed studies revealed the sorption abilities of pDVB, pDVB-T and pDVB-M are comparable, however some differences are observed. In the case of 2,4-dichlorophenol and 2,4,6-trichlorophenol pDVB-T and pDVB-M sorbents had higher sorption capacity than pDVB, pointing out the positive role of 2,4,6-trichlorophenol in the formation of pores suitable for the sorption of these compounds. Maximal recoveries for 2,4-dichlorophenol are in the range of 43 to 49%. The interactions of the other phenols with the polymer surface is not so strong, therefore their recoveries are lower. As our previous studies proved [30], the adsorbents possessing pores with width 3-4 nm were the most effective in the sorption of phenols. The materials presented in this work have also pores in this range, but their contents in the structure is probably insufficient to provide higher sorption capacity. In the case of pDVB-P uptake of phenols is almost twice lower than for pDVB. Both materials have almost identical porous structure parameters. Therefore, it should be assumed that during SPE experiment the flow of the solution through the bed of polymeric sorbent was not the same (channeling effect). On  
Figure 5: Results of solid phase extraction (SPE) experiments obtained for the mixture of phenols.  
the other hand, for pDVB-SO\({}_{3}\)H, drastic decrease in sorption capacity is observed, due to repulsive character of the acidic surface of the polymer, caused by the presence of sulphonic groups.  
The sorption of ibuprofen on the studied materials (Figure 6) is very similar to that of trichlorophenol. Both compounds have slightly acidic chemical character and their solubility in water is poor. The recoveries are in the range of 35-38% excluding pDVB-SO\({}_{3}\)H with twice lower value. In the case of salicylic acid recoveries are in the range of 4.5 to 7.6% for all the materials, even for the sulphonated one. Although uptake was not high, it is obvious that the acidic character of the polymer did not interfere with the process and the main factor influencing it was porosity.
# Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding
## 3 System Development and Verification  
Scatter index \(J=\frac{R_{c}(k,k)}{R(k,k)}\) that estimates the between-class to within-class scatter ratio was used to analyze the correlation between selected features and bonding quality. Between-class scatter \(R_{c}\) and within-class scatter \(R\) are defined as follows [16]. The feature mean for each class \(\overline{Y}_{i}(k)\) was obtained using individual features \(Y_{ij}(k)\):  
\[\overline{Y}_{i}(k)=\frac{1}{M_{i}}\sum\limits_{j=1}^{M_{i}}Y_{ij}(k), \tag{1}\]  
where  
\(i=i^{th}\) class  
\(j=j^{th}\) pattern in a class  
\(k=k^{th}\) feature  
\(M_{i}=\) the number of patterns in class \(C_{i}\).  
The overall system mean \(\overline{Y}\) is determined as follows:  
\[\overline{Y}(k)=\sum\limits_{i=1}^{C}p_{i}\overline{Y}_{i}(k), \tag{2}\]  
where  
\(p_{i}=\) a priori probability of class \(C_{i}\)  
\(C=\) number of classes.  
Within-class scatter is obtained by calculating covariance for each feature as follows:  
\[R_{i}(k)=\frac{1}{M_{i}}\sum\limits_{j=1}^{M_{i}}\left(Y_{ij}(k)-\overline{Y} _{ij}(k)\right)\left(Y_{ij}(k)-\overline{Y}_{ij}(k)\right)^{T} \tag{3}\]  
Moreover, the individual-class scatter is defined as follows:  
\[R_{c}(k)=\sum\limits_{i=1}^{C}p_{i}\left(\overline{Y}_{i}(k)-\overline{Y}(k) \right)\left(\overline{Y}_{i}(k)-\overline{Y}(k)\right)^{T} \tag{4}\]  
From Equations (3) and (4), the feature-selection criterion, a cost function, is defined as follows:  
\[J(k)=\frac{R_{c}(k)}{R(k)}, \tag{5}\]  
where \(R(k)=\sum\limits_{i=1}^{C}R_{i}(k)\).  
The HMMs for determining welding quality were developed using collected training sound signals. Once the models were developed, the unknown condition of welding quality was determined using collected sound signals and developed models with selected features. The schematic of determining the unknown quality condition by using the developed model is illustrated in Figure 3. In this method, signals collected from events other than those used for model development served as input data. Moreover, quality state \(S_{n}\) based on selected features was determined by referring to the model with a large probability value based on the Viterbi algorithm. By using a combination of results obtained from various selected features, the final decision pertaining to the quality state was determined by fusing all decisions.
## 3 Methods
### Constructing the Knowledge Graph  
#### 3.1.1 Ontology Construction for the Knowledge Graph  
After obtaining data from various sources, it is necessary to design the ontology concepts for the knowledge graph. A well-designed ontology schema can better describe the relationships between knowledge, reduce data redundancy, and improve efficiency. Ontology construction methods can be divided into two categories. The first is the top-down approach, which starts by defining the data schema and constructs the ontology from the top-level concepts to the lower-level concepts. The second is the bottom-up approach, which is based on the underlying domain data and concepts and gradually abstracts upward to form higher-level concepts.  
Several renowned ontologies in the general domain that can be utilized for constructing knowledge graphs include DBpedia Ontology [42] and WordNet [43]. These ontologies provide a solid foundation and proven frameworks for representing and structuring knowledge in a meaningful and machine-readable way.  
When aiming for high efficiency and accuracy in the spacecraft anomaly detection knowledge graph, the top-down construction method is often employed. However, due to the broad range of data sources and complex data types, a \"combined approach\" is preferred in this case. Here, domain experts design the upper-level concepts of the knowledge graph, while the lower-level concepts are designed based on the data sources and their generalization.  
This combined approach leverages the strengths of both top-down and bottom-up methods, providing a structured framework for organizing knowledge while also being flexible enough to incorporate diverse and complex data sources.
## 3 Methods
### V-RTI: Data Acquisition
#### 3.1.2 Photogrammetric Image Acquisition of Small Test Object  
The model of the test object was obtained by 294 orthogonal photographs and 77 oblique views. The first batch of 294 images was orthogonally captured close to the minimum focus distance (approximately 31 cm) to maximize the Ground Sampling Distance (GSD) (0.0007 cm/px), thereby optimizing the resolution of the Canon EOS 6D DSLR equipped with a Canon 100 mm f/2.8 macro lens (Figure 5a). The second set of 77 images was acquired at a significantly greater distance to ensure proper alignment of the first dataset (Figure 5b).  
In order to collect as much information as possible and to allow for the correct alignment of the images, a 70% overlap was maintained throughout the acquisition. In order to speed up the shooting process, the painting was placed on cardboard, moving it between shots, while the camera was mounted horizontally on a tripod. The illumination provided by two polarized LED panels reduced the influence of surface reflections, while the use of a low sensitivity value (160 ISO) contained digital image noise. By adopting a f/18 aperture, it was possible to minimize the impact of depth of field issues when shooting subjects from such close distances [36].  
\begin{table}
\begin{tabular}{l l} \hline \hline  & \multicolumn{2}{c}{**Photogrammetry Setup Specification**} \\ \hline Camera model & Canon EOS 6D \\ Sensor type & CMOS 20.2 Mpx \\ Sensor size & Full frame (35.8 \(\times\) 23.9 mm) \\ Image size & 5472 \(\times\) 3648 pixels \\ Focal length & Canon EF 50 mm f/1.8 STM \\ Strobe model & Yongnuo YN685 II, guide number 60 \\ Polarizer, on strobe & Linear polarizing filter sheet (380–700 nm) \\ Polarizer, on camera & Hoya HD circular polarizing filter (380–700 nm) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the tools used for the photogrammetric survey.  
Figure 4: (**a**) Interior view of the chapel of Ellesiya; (**b**) camera positions in Metashape.
## 4 Metal-Oxide Nanoparticles (MONPs) in Water Treatment
### Zinc Oxide Nanoparticles  
ZnO NPs are suitable candidates for wastewater treatment because of their unique stability and ability to generate reactive oxygen bonds, which accelerate the degradation of pollutants [125]. ZnO NPs stand out as superior photocatalysts due to their wide bandgap for efficient absorption of light and generation of electron-hole pairs [126].  
In addition to these photocatalytic characteristics, ZnO NPs have a large surface area for better adsorption of different pollutants [127]. This dual functionality enhances their effectiveness in water treatment processes, as they can efficiently capture and remove pollutants through adsorption in addition to photocatalytic degradation [128]. Recent studies by [PERSON] et al. [119], [PERSON] et al. [120], and [PERSON] et al. [121] proved the effectiveness of ZnO NPs in the textile industry for dye removal from wastewater, as summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Nanomaterial** & **Modification** & **Synthesis Method** & **Pollutant Type** & **Mechanism of Action** & **Removal Time** & **Removal Rate (\%)** & **Ref.** \\ \hline \multirow{2}{*}{ZnO NPs} & \multirow{2}{*}{Spherical NPs with no modifications} & \multirow{2}{*}{Co-precipitation} & Methyl orange (MO) and & \multirow{2}{*}{Adsorption} & \multirow{2}{*}{120 min} & MO: 60 & \\  & & & amaranth (AM) & & & AM: 78 & \\ \hline \multirow{2}{*}{ZnO nanorods} & Fabrication on zinc and titanium foil & Electrochemical anodization & Congo red and & Adsorption and & & & \\  & & & methyl orange & & photocatalytic degradation & & \\ \hline \multirow{2}{*}{ZnO nanorods} & \multirow{2}{*}{ZnO thin film} & \multirow{2}{*}{Co-precipitation} & Methylene blue & Photocatalytic & \multirow{2}{*}{120 min} & \multirow{2}{*}{69 to 73} & [131] \\  & & & dye (MB) & degradation & & & \\ \hline \multirow{2}{*}{ZnO NPs} & \multirow{2}{*}{Ni tubular thin films} & Pulse electro & \multirow{2}{*}{Textile dyes} & Photocatalytic & \multirow{2}{*}{60 min} & \multirow{2}{*}{91.21} & [132] \\  & & deposition & & degradation & & & \\ \hline \multirow{2}{*}{ZnO NPs} & \multirow{2}{*}{Hydrogel composite} & Bio-reduction & _Escherichia coli_ & Adsorption & 35 min & 70 & [133] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recent studies on the application of ZnO NPs in water treatment.  
Figure 7: Schematic of the adsorption mechanism using magnetic nanosheets. Source: [121].
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 2 Theory and Method
### Elman Neural Network  
The Elman neural network is a neural network with a local recursive function [22]. Compared with traditional neural networks, the Elman neural network introduces a fixed feedback link, which can monitor data changes in real time and enhance the network's ability to dynamically process information. The basic premise is to minimize the mean square error between the actual output and the expected output by using the least square method and the gradient search technique. Elman's main structure comprises two parts: the feedforward connection and the feedback connection. The feedforward connection includes the input layer, the hidden layer, and the output layer. The input layer unit plays the role of the signal transmission, the output layer unit plays a linear weighting role, and the transfer function of the hidden layer unit can adopt a tansig nonlinear function. The feedback connection is composed of a group of \"connection\" units. Its function in the network is to remember the output value of the hidden layer unit at the previous instance, and return it to the input of the network. It can be regarded as a one-step delay operator. Therefore, the biggest advantage of the Elman neural network is its memory ability, which can better reflect the nonlinear and dynamic characteristics of the model.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
## 1 Introduction
### Motivations  
Although there are several algorithms to reserve resources for transmission as presented above, in the receiving UE, the PSCCH searching is completed in a pure blind way, trying all the possibilities, or at most is considered some kind of threshold to try to eliminate candidates. The threshold approach is not efficient because if the threshold is low, we still have a high number of candidates, or if the threshold is high, we could be eliminating candidates that contained a PSCCH and thus deteriorating the overall system performance. In a semi-persistent subchannel selection we can perform an optimization in the PSCCH searching because allocation is constant over a period of time, however this approach can lead to persistent collisions during this period, mainly in the Mode 2 of the 5G-NR V2X [14]. Therefore, the Blind algorithm is the one that guarantees the best system performance in terms of PSCCH detection, but at the expense of longer runtime as all possibilities are tested. Then, a new algorithm with the same performance as the Blind algorithm, but with more efficient processing to reduce the runtime, is crucial for practical 5G-NR V2X systems.
# Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
## 3 Results  
### Results of Lumped Parameter Model  
#### 3.1.1 Comparison of Results of Fault Diagnosis Models  
In order to verify the effectiveness of the fault diagnosis model in this paper, Gaussian Process (GP), Support Vector Machines (SVMs), eXtreme Gradient Boosting (Xgboost), K-Nearest Neighbor (KNN), Random Forests (RFs) are compared with the CNN model proposed in this paper.  
A total of four different cases are designed for the fault detection task based on whether system deviation and noise are factored in (conditions for system deviation and noise are described in Section 2.1). Figure 7 shows the accuracy results of the CNN model in this paper and five other machine learning algorithms for blade crack location detection. Compared with the other five machine learning models, the CNN model achieves the highest level of prediction accuracy in four cases: 91.7%, 88.0%, 91.1%, and 86.7%, respectively. The prediction accuracy of the other five machine learning models is significantly reduced, especially under complex conditions with system deviation and noise, while the CNN model in this paper still performs well.  
Figure 8 shows the accuracy results of the CNN and five other machine learning methods for blade crack depth prediction. Similarly, in four cases, the CNN model developed in this study outperforms the other five models in predicting blade crack depth by 95.4%, 90.7%, 94.6%, and 87.5%, respectively.  
Figure 7: Prediction accuracy of blade crack location by different deep learning models (case 1: No system deviation and No noise; case 2: System deviation and No noise; case 3: Noise and No system deviation; case 4: System deviation and Noise).  
Through comparison, the CNN model proposed in this research is better than the traditional machine learning model in multiple ways. The traditional machine learning model typically can only utilize a single task detection problem, whereas the CNN model can utilize a multi-task problem of crack location and depth at the same time. The detection accuracy of the CNN model established in this paper is significantly higher than that of the five different traditional machine learning models, and the CNN model shows stronger adaptability under system deviation and noisy working conditions.
# Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment
## 2 Materials and Methods
### Hardware Architecture
#### 2.1.3 Field Programmable Gate Array  
The on-site field programmable gate array (FPGA) utilizes the Xilinx Spartan-6, which allows for programmable routing using logic blocks, creating custom hardware functions from configured chips. FPGA possesses the property of reconfigurability and can be modified according to specific usage requirements.  
FPGA integrates application-specific integrated circuit (ASIC) and processor architecture systems, offering hardware clock speed and reliability, reducing the cost of custom ASIC design, and allowing for chip reprogramming in a manner similar to software flexibility. It is not limited by the number of processor cores. Additionally, FPGA is a parallel architecture, so different processing tasks do not occupy the same resources. Each independent processing task is assigned to a dedicated chip block, which does not affect other logic blocks and can automatically generate functions. Therefore, when adding other processing tasks, the performance of the application section is not affected.  
FPGA is a powerful and flexible hardware platform, as shown in Figure 4, and is particularly suitable for scenarios that require high customization and high-performance computing. However, due to its relatively complex development process, it typically requires specialized hardware design and electronic engineering knowledge.  
Figure 3: AD9226 analog-to-digital converter.
### Main Contributions
* Performing deep research, considering a real network, representing a zone of the Romanian transmission and distribution system, where two scenarios have been analyzed (without and with wind farms connected) to quantify the benefits on the operation of the network with the wind power sources integrated overlapping the coordinated control of the OLTCs. The considered scenarios (together with the three cases analyzed within the second scenario) cover all situations associated with the steady-state of the electrical networks. The methodology can lead to significant technical and economic benefits in the operation of the electric networks, with and without the renewable sources connected, considering mainly the wind farms, if the requirements regarding the integration of a wide-area communication network with high-speed data transmission, an OPF module with a fast processing speed, and the OLTC of the transformer that performs fast-changing of the taps are met.
# Microwave and Ultrasound Based Methods in Sludge Treatment: A Review
## 1 Introduction--General Aspects of Sludge  
The waste residue which is generated during a variety of processes in a wastewater treatment plant is called sludge. Since it contains residual (mostly organic) pollutants, pathogenic microorganisms and other toxic compounds that originated from the treated wastewater, sludge is potentially harmful to both the environment and health. Moreover, it has been shown that during the conventional activated sludge process, the extent of waste activated sludge (WAS)--which is the by-product of the microbial organic pollutant removal subprocess--have been continuously increasing annually in the last few decades, and definitely will further in the near future [1]. Therefore, the proper treatment and stabilization of it are inevitable, however, the most significant problem is that these processes have high energy demands and costs. During a conventional activated sludge process, the expense for WAS treatment is costly, and the cost of its treatment can cover 50-60% of the total cost of a wastewater treatment plant [2, 3].  
The main objective of the activated sludge process is to generate a mixture of primary sludge (PS) and WAS which can be used for fertilization or can be stabilized and utilized afterwards via anaerobic digestion (AD). Anaerobic digestion or fermentation uses specific microorganism which converts organic substances to methane and carbon dioxide through different biochemical processes, which involves the hydrolysis of proteins, lipids and carbohydrates; fermentation of amino acids and sugars to acids (acidogenesis); oxidation of fatty acids and alcohols into acetic acid, CO\({}_{2}\) and hydrogen, and the conversion of acetic acid and hydrogen to methane [4].  
Due to the relatively high time and cost demand of the AD process, it has been shown in several studies that pre-treatment methods should be implemented to accelerate the process and/or increase its effectiveness. Primarily, these pre-treatment methods aim to increase the disintegration degree (DD) of the sludge by bursting microbial cell structures and disintegrating microbial flocks [5]. There are many different kinds of pre-treatment processes, e.g., biological (enzymatic hydrolysis), physical (microwave, ultrasound, thermal, gamma-radiation), chemical (alkaline, H\({}_{2}\)O\({}_{2}\), ozone), but it has been shown that these methods regardless of their type can disrupt the structure of the sludge, which leads to--among others higher solubilized organic matter content (and thus increasing the soluble chemical oxygen demand, SCOD), and therefore accelerating the following anaerobic digestion step [6; 7]. In this literature review article, we would like to primarily focus on the standalone and combined pre-treatment processes based on microwave and ultrasonic irradiation; two advanced physical treatment methods that have been widely investigated in the last few decades, and which have been already found to be effectively applied in certain environmental and biotechnological procedures, such as sludge treatment [8; 9].
## 4 Experiment
### Evaluation Metrics  
In our experiments, we set the number of clusters equal to the number of classes for all algorithms. To evaluate the performance of clustering, we use Accuracy and Normalized Mutual Information (NMI) to measure the clustering results.  
Accuracy is defined as follows:  
\[Accuracy=\frac{\sum_{i=1}^{n}\delta(s_{i},map(r_{i}))}{n}, \tag{27}\]  
where \(r_{i}\) and \(s_{i}\) are cluster labels of item \(i\) in the clustering results and ground truth, respectively. If \(x=y\), \(\delta(x,y)\) equals 1 and otherwise equals 0, and \(map(r_{i})\) is the permutation mapping function which maps \(r_{i}\) to the equivalent cluster label in ground truth.  
The NMI is defined as follows:  
\[NMI(C,C^{\dagger})=\frac{MI(C,C^{\dagger})}{max(H(C),H(C^{\dagger}))}, \tag{28}\]  
where \(MI(C,C^{\dagger})\) is the mutual information between \(C\) and \(C^{\dagger}\). If \(C\) is identical with \(C^{\dagger}\), \(NMI(C,C^{\dagger})=1\). If the two cluster sets are completely independent, \(NMI(C,C^{\dagger})=0\).