esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Structure
### Voltage and Current Regulation Module  
This module ensures stable voltage and current control for the GFMC APF. The voltage controller combines a repetitive and proportional-integral (PI) controller to regulate \(v_{\rm gfdbc}\) and suppress harmonics effectively. These capacitor voltages are transformed into the \(dq\) reference frame, and the harmonic voltage references (\(v_{\rm ghd\_ref}\) and \(v_{\rm gfhq\_ref}\)) are generated from the harmonic voltage command, ensuring precise compensation of voltage harmonics. These values are compared with their respective reference values to calculate voltage errors.  
\[e_{\rm vd}=(v_{\rm gfd\_ref}-v_{\rm ghd\_ref})-v_{\rm gfd} \tag{3}\]  
\[e_{\rm vq}=-v_{\rm gfhq\_ref}-v_{\rm gfd} \tag{4}\]  
These errors are processed by the repetitive controller (RC), which uses a delay operator \(Z^{-\rm N}\) to capture the periodic nature of the harmonics. A scaling factor \(K\) adjusts the amplitude of harmonic compensation, while the RC gain \(K_{\rm r}\) fine-tunes the controller's output for stable and accurate harmonic suppression, and the filter \(Q_{\rm z}\) shapes the harmonic response to balance compensation and stability. The following equation defines the output of the RC:  
\[Y(z)=\frac{K_{\rm r}\cdot Z^{-(N-K)}}{1-Q_{\rm z}\cdot Z^{-N}}\cdot E(z) \tag{5}\]  
Additionally, the PI controller (\(K_{\rm vp}+K_{\rm vi}/\rm s\)) is carefully tuned to adjust the gains \(K_{\rm vp}\) and \(K_{\rm vi}\) to manage steady-state and dynamic responses effectively.  
For current control, the GFMC APF incorporates a measured current feedback loop to address harmonic currents. The converter output currents \(i_{\rm gidbc}\) are measured, transformed into the \(dq\) reference frame, and compared with the reference currents (\(i_{\rm gid\_ref}\) and \(i_{\rm giq\_ref}\)) generated by the voltage controller.  
\[e_{\rm id}=i_{\rm gid\_ref}-i_{\rm gid}\,\ e_{\rm iq}=i_{\rm giq\_ref}-i_{\rm giq} \tag{6}\]  
The current controller processes the resulting errors, which employs the proportional gains \(K_{\rm cp}\) to regulate the compensating currents. These compensating currents are injectedinto the grid to neutralize harmonic distortions, resulting in sinusoidal current profiles and reduced total harmonic distortion.
## 4 Inversion of Fatigue Damage Parameters
### Parameter Inversion  
Figure 8 illustrates the relationship curve between the number of training iterations and the training mean square error of the neural network. It is evident that during the third training iteration, the mean square error of the validation set reached the minimum value of 0.0026236, with an accuracy of the validation set reaching 0.986. The corresponding relationship function at this point is selected as the hidden layer function of the neural network. After constructing the corresponding BP neural network, the experimentally obtained fatigue life and initial normal strength are input into the neural network, and the fatigue damage parameters corresponding to different pouring interval cold joints are inversely calculated, as presented in Table 1.  
Figure 7: BP neural network.  
From Table 1, it can be observed that the initial cohesive strength \(\sigma_{max,0}\), fatigue limit \(C_{f}\), and initial cohesive strength corresponding separation quantity \(\delta_{0}\) of the cold joint interface decrease with the increase of pouring interval. The pouring interval not only affects the initial strength of the cold joint interface but also influences the starting conditions of fatigue damage on the cold joint interface. Therefore, for cold joint components with longer pouring intervals, the effect of fatigue loading needs to be taken into account, as even minimal fatigue loading may lead to damage or even cracking of the cold joint interface.
## 3 Proposed Method
### Framework Design
The output is a proof \(\pi\) that demonstrates that the value \(x\) is within a specified range, specifically proving that \(x\) is non-negative. 9. \(1/0\leftarrow\text{{RangeVerify}}(\pi,C)\) : Verification of the correctness of a range proof based on Bulletproofs. The input consists of the range proof \(\pi\) and the corresponding Pedersen commitment \(C\). If \(\pi\) matches \(C\) and the hidden value in commitment \(C\) is a non-negative integer, the verification passes. Otherwise, the verification fails. 10. \(1/0\leftarrow\text{{Overify}}(t,T)\) : Ownership verification of a note. The input consists of the stealth address \(T\) and its corresponding address confirmation key \(t\). The _OVerify_ algorithm determines whether the confirmation key \(t\) can successfully confirm the stealth address \(T\). The output indicates whether the owner of the confirmation key \(t\) has ownership of the note bound to the stealth address \(T\). 11. \(1/0\gets NfVerify(nullifier,T)\) : Note validity verification. The _NfVerify_ algorithm is primarily responsible for verifying whether the input note has a \"double spend\" issue. It takes the Nullifier table, which contains nullifiers of invalidated notes in the current note world state, and the nullifier \(T\) corresponding to the note, as input. The _NfVerify_ algorithm checks whether \(T\) exists in the Nullifier table. The output indicates whether the note bound to the stealth address \(T\) has been double-spent. 12. \(1/0\leftarrow\text{{TVerify}}\big{(}C_{\text{p}},C_{\text{q}},r_{\text{p}},r_{ \text{q}}\big{)}\) : Transaction input-output balance verification. The _TVerify_ algorithm is primarily responsible for verifying whether the transaction input and output amounts are balanced, meaning that the sum of input amounts equals the sum of output amounts. The algorithm takes the sender's commitment \(C_{\text{p}}\) to the amounts and chosen blinding factors \(r_{\text{p}}\), as well as the receiver's commitment \(C_{\text{q}}\) to the amounts and chosen blinding factors \(r_{q}\), as input. The output indicates whether the transaction inputs and outputs are balanced.
## 4 Results
### Results of Expert Interviews
#### 4.2.3 Data Analysis, the Critical Point  
The criticality of collecting and analyzing data was stressed by all interviewees. Even if they have not completely overhauled their production systems, some of their machines are already equipped with sensors, scanners, and 3D cameras to get a fuller picture of the processes taking place and to gather the data. Most of the data is stored with their own company, or (also) at the level of a group of companies. In one case, an interviewee referred to a cloud at the group of companies level, and in one case there was an outside cloud service provider (V1, V2, V3, V4 interviews). Software used for analysis and information, such as ERP system interfaces and the platforms and applications used by workers, are developed in-house, or with the involvement of consulting firms: \"We develop our own interfaces and data mining and simulation software with the help of a consulting company. It costs a lot of money\" (V3 interview). All interviewees referred to this knowledge as critical within the organization and expressed their concerns about ensuring a supply of professional expertise (V1, V2, V3, V4 interviews).  
A data mining department is operating in the Madrid unit of the V4 group, which systematizes the production of ultrasonic sensors. Data is generated in three places in the production: In the MES, in the testing and in the production of machines. This produces 170 GB of data per day, in this section alone. They process data into a special computer cluster, and accelerate the analysis using search engines in web browsers. The system is able to connect virtually all information with everything, so very complex cause-and-effect relationships can be observed (V4 interview).  
Information generated as a result of data analysis should be used in decision making: \"It is a huge task to select which data to analyze, which will be useful in a given decision\" (V2 interview). This can support not only the decisions made by humans, but those by autonomous robots, which is another example of the benefits of an electronic network linking everything with everything. Such an autonomous robot is already used in Hungary, in the V4 company. The machine in question engages with a metal plate. The 3D scanner detects when the disk is moved and if it is anticipated that the coating will not completely cover the next disk, it informs the robot, which adjusts it (closed loop M2M--the two machines communicate on the basis of data and then intervene) (V4 interview).
## 3 Results and Discussion
The mean of each statement and category (SYSUSE, INFOQUAL, ITERQUAL, OVERALL) is reported. Considering the device usefulness category (SYSUSE), for statements four, five and eight, concerning efficiency, speed and productivity to complete the task with the SG, respectively, the participants gave a neutral score (i.e., neither agreed nor disagreed). \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Information Composition Type**} \\ \hline  & Text & Graphic \\ \hline Mental Demand & 47.50 \(\pm\) 28.75 & 37.50 \(\pm\) 29.21 \\ Physical Demand & 39.06 \(\pm\) 32.10 & 36.56 \(\pm\) 29.31 \\ Temporal Demand & 47.50 \(\pm\) 28.93 & 38.13 \(\pm\) 28.51 \\ Performance & 35.00 \(\pm\) 29.94 & 33.13 \(\pm\) 28.63 \\ Effort & 32.50 \(\pm\) 30.39 & 30.63 \(\pm\) 31.30 \\ Frustration & 24.38 \(\pm\) 28.45 & 22.50 \(\pm\) 27.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of NASA Task Load Index (NASA-TLX) subscales scores (mean and standard deviation). Figure 4: Overall workload by information composition type (text and graphic-based) per milker. This score was likely related to SG QR code scanning and file opening time since this aspect slowed down the milking procedures. However, the use of SG enabled the completion of the overall task (identification, selection and milking) employing only one worker (statement 3). The seventh statement suggested that the SG are relatively easy to learn and use; in fact, participants strongly agreed with this statement, receiving the highest score (\(4.69\pm 0.48\)). In the information quality section (INFOQUAL), we observed that participants strongly agreed with statements nine and ten regarding the ease of finding the information in the document. Moreover, the milkers agreed that this information is helpful for task completion. As expected, the information in text-format displayed on the SG is less clear than information in graphic-format (statements 12 and 13), in accordance with the results highlighted in the NASA-TLX. Thus, these results confirmed that the graphic composition was more usable by the workers. Specifically, the farmers usually find the information related to the evolution of milk production and body condition score as a graph representation. The overall satisfaction on the usability of the SG was positive, with a mean score of \(4.38\pm 0.81\). This suggested that the device is helpful for the identification and selection of the animals during the milking session.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 2 Problem Statement
### Stream Mining  
A data stream refers to a continuous, potentially infinite, flow of data that arrives at high speed and rapidly and unpredictably. In contrast to traditional batch data processing, data stream processing involves analyzing data in real-time as they flow into the system. Data streams are often generated by a variety of sources, such as sensors, social media, financial transactions, network traffic, etc. Due to their continuous and high-volume nature, data streams present unique challenges for data processing, storage, and analysis. The generation of a data stream can be modeled as a random variable \(X\), from which the objects \(o\in dom(X)\) are randomly drawn. Here, \(dom(\cdot)\) represents the domain of a random variable [20]. Let \(X_{t}\) denote a random variable representing the data stream at time \(t\), where \(t\) is a positive integer. Each data point \(x_{t}\in dom(X_{t})\) is generated by the joint distribution \(P_{t}(x_{t},y_{t})\), where \(y_{t}\) is the corresponding class label of \(x_{t}\). The goal is to learn a classifier \(f_{t}:dom(X_{1})\times\cdots\times dom(X_{t})\rightarrow\mathcal{Y}\) that maps the current and past data instances to their respective class labels, where \(\mathcal{Y}\) is the set of possible class labels.  
The challenge in classification stream problems is that the joint distribution \(P_{t}(x_{t},y_{t})\) may change over time, a phenomenon known as concept drift. As a result, the classifier needs to be continuously updated to adapt to the changing distribution and maintain its accuracy over time.
# Cell Selection Technique for Millimeter-Wave Cellular Systems with Hybrid Beamforming
## Preliminaries
### System Model
Here, the delay-\(\tau\) channel matrix can be considered as a geometric wideband channel model, which can be viewed as the sum of \(N_{M}\) scattering clusters each with time delay \(\tau_{m}\). Each cluster consists of \(N_{L}\) propagation rays with relative time delay \(\tau_{l}\). The delay-\(\tau\) channel matrix \(\mathbf{H}^{c}(\tau)\) can be written as [22]:  
\[\mathbf{H}^{c}(\tau)=\sqrt{\frac{N_{TX}N_{RX}}{N_{M}N_{L}}}\sum_{m=1}^{N_{M}} \sum_{l=1}^{N_{l}}\beta_{ml}^{c}p_{rc}(dT_{S}-\tau_{m}-\tau_{l})\mathbf{a}_{ RX}(\alpha_{ml}^{c},\varphi_{ml}^{c})\mathbf{a}_{TX}{}^{\prime}(\phi_{ml}^{c}, \theta_{ml}^{c}) \tag{2}\]  
and the channel at subcarrier \(k\), \(\mathbf{H}^{c}(k)\), can be expressed as [23]:  
\[\mathbf{H}^{c}(k)=\sum_{\tau=0}^{N_{\tau}-1}H^{c}(\tau)e^{\frac{-2\pi k}{N_{L} }\tau} \tag{3}\]  
where \(N_{L}\) and \(N_{M}\) denote the number of propagation rays and the number of clusters, respectively. \(p_{rc}(\tau)\) is the pulse shaping function for \(T_{S}\)-spaced signaling evaluated at \(\tau\) second, \(\beta_{ml}^{c}\) is the complex gain of the \(l\)-th ray in the \(m\)-th cluster, whereas \(\mathbf{a}_{RX}(\alpha_{ml}^{c},\varphi_{ml}^{c})\) and \(\mathbf{a}_{TX}(\phi_{ml}^{c},\phi_{ml}^{c})\) represent the Rx and Tx array response vectors at an azimuth (elevation) angle of \(\alpha_{ml}^{c}\big{(}\varphi_{ml}^{c}\big{)}\) and \(\phi_{ml}^{c}\big{(}\theta_{ml}^{c}\big{)}\), respectively. The array response vector for the \(N\)-element uniform linear array (ULA) on the \(y\)-axis and uniform planar array (UPA) in the \(xy\)-plane with \(N_{e}\) and \(N_{f}\) elements on the \(x\) and \(y\) axes, respectively, is given by [24]:  
\[\mathbf{a}_{TX}^{\text{ULA}}(\phi)= \frac{1}{\sqrt{N}}\Big{[}1,\ e^{i\frac{2\pi}{\lambda}d\sin{(\phi)}},\ \ldots,\ e^{i\frac{2\pi}{\lambda}d(N-1)\sin{(\phi)}}\Big{]}^{T} \tag{4}\] \[\mathbf{a}_{TX}^{\text{UPA}}(\phi,\theta)= \frac{1}{\sqrt{N_{r}N_{r}}}[1,\ \ldots,\ e^{i\frac{2\pi}{\lambda}d(e\cos{(\phi)} \sin{(\theta)}+f\sin{(\phi)}\sin{(\theta)})},\] (5) \[\ldots,\ e^{i\frac{2\pi}{\lambda}d((N_{e}-1)\cos{(\phi)}\sin{( \theta)}+(N_{f}-1)\sin{(\phi)}\sin{(\theta)})}]^{T}\]  
where \(d\) is the element spacing and \(\lambda\) is the wavelength. Also, \(0\leq e<N_{e}\) and \(0\leq f<N_{f}\) are antenna element indices for \(x\) and \(y\), respectively.
# Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation
## 4 Simulation Results
According to the above, the simulation results show the unbiasedness, low uncertainty, and high convergence speed of the displacement computation. It should be noted that according to Algorithm 2 in Section 3.3, the amount of computation is proportional to the number of iterations, equations, and unknowns, which depend on the number of interferometers and measurement positions. Therefore, compared with traditional linear multi-axis interferometer models, the proposed iterative-based modeling and solving algorithm has a greater computational complexity. Nevertheless, in practical engineering, the computational time can be decreased by adopting a high-efficiency processing unit such as the FPGA with a parallel computing mode.
# Nucleobase-Containing Polymers: Structure, Synthesis, and Applications
## 2 Nucleobase-Containing Polymers in Nature  
DNA and RNA are the nucleobase-containing polymers in nature. The central molecular interaction between these polymers is a hydrogen bond between corresponding nucleobases; these nitrogenous bases are adenine (A), guanine (G), cytosine (C), thymine (T), and uracil (U).  
DNA usually forms duplexes that are stabilized by hydrogen bonding between adenine (A) and thymine (T); cytosine (C) and guanine (G). RNA, in most cases, consists one strand of nucleic acids, using uracil (U) instead of thymine (T) in its nucleobase pairing [20]. Besides the biosynthesis of DNA/RNA in nature, oligonucleotide synthesis is based on phosphoramidite chemistry, which was developed in the 1980s and later combined with solid-phase supports and automation [21; 22] (Figure 1). However, the length of synthetic oligonucleotide is limited by the coupling efficiency and the following purification steps, which resulted in a typical length of synthetic single-stranded DNA/RNA of around 70-100 bases, with low error rates [23]. Most recently, [PERSON] et al. utilized transferase-catalyzed living chain-growth polycondensation to synthesize well-defined natural DNA sequences such as homopolynucleotides (poly(dT), poly(dA)), random polynucleotides, and block polynucleotides with more than 1000 bases. This work is an excellent example of employing enzymatic living polymerization to achieve DNA synthesis in the absence of a DNA template [24].
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 4 Numerical Results
### Simulation and Experiment Setup with Data Processing
This training process produces simulation accuracy, a trained CNN model, and extracted feature information. In the online phase, mobile devices at each RP capture real-time RSSI signals and transmit them to a server via a designated network. The server processes these signals by matching them with the MAC address list and utilizing the feature information obtained during the offline phase. A custom Python script integrates these elements, enabling the system to determine the predicted location. The final output includes the experimental results, which provide the estimated device location based on the most suitable RPs. This two-phase workflow ensures that the system effectively combines preprocessed data and real-time inputs to achieve accurate location predictions.
# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
### Background  
However, the task of visual inspection of the weld begins once the weld seam has already been created, so for a study of this type, what is truly important is to apply some type of technique to check the goodness of the created seam.In this sense, there are several techniques to carry out this inspection work, within what are called non-destructive tests: ultrasonic testing, radiographic testing, magnetic testing, and the simplest technique that involves visually observing the welding carried out by an expert, among others.  
Although radiographic testing is the most widely used technique, direct visual inspection by an expert is still common. In any case, due to the number of meters of welding that must be checked and the large amount of time that must be devoted to this task, it is necessary to automate it to the maximum, achieving acceptable automation reliability.  
Given the significance and pertinence of the processes, as well as their extensive utilization within the industry, this study concentrates on the examination of the FCAW and GMAW welding processes.  
In this way, FCAW is an electric arc welding process in which the arc is established between the continuous tubular electrode and the piece to be welded. The protection is carried out through the decomposition of raw materials contained in the tube, with or without external gas protection and without applying pressure [2], as illustrated in Figure 1. On the other hand, GMAW is an arc welding process that requires automatic feeding of an electrode protected by a gas that is supplied externally [3], as demonstrated in Figure 2.  
Undoubtedly, the study of the input parameters in robotic welding, as is the case with the works presented in [4; 5], the quality of the weld bead is contingent upon the presence of a particular factor, as the authors mention in [4; 6; 5]. However, the present study will concentrate on the implementation of a neural network in the analysis of welding seams, with the objective of detecting potential defects and identifying seams that have been executed with a high standard of quality.  
The work carried out can be aligned with a geometric deep learning model in accordance with the indications presented by the authors in [7]. It has been demonstrated that these models encompass those dedicated to detecting objects based on a CNN when the objects must also be detected in different positions and angles, as discussed in this article. This process facilitates the identification of works related to the presented topic that have been resolved using a CNN approach.  
In order to achieve a correct vision test of the welding parts, it is necessary to prepare the area to be welded beforehand, taking into account the consumable to be used in the welding, as well as the type of steel to be used in it.The proportion of gas is also a  
Figure 1: Scheme of the FCAW welding process.  
critical factor that must be taken into consideration in each of the welding processes to be carried out.