esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Compared to conventional APDP-AES, nebulized discharge techniques demonstrated improved sensitivity and stability and could also be applied to build a portable device. Nebulized discharge takes the form of analytes nebulization to transport more liquid into the plasma area, which can effectively increase the contact area and duration, improve the sensitivity of heavy metal detection, and reduce device volume. Therefore, APDP as an excitation source coupled with nebulization introduction is considered to have a good application prospect in reducing LODs and in situ and rapid detection of heavy metal [22]. However, there are few studies on heavy metal detection by nebulized discharge in atmospheric pressure, and the plasma characteristics and heavy metal detection mechanism are not studied in depth. In addition, the pneumatic nebulizer is directly used as the jet electrode [23], which can affect the service lifetime and testing accuracy of the device. In this work, a novel atmospheric pressure nebulized discharge plasma (APNDP) coupled with AES is employed for detecting trace Cu. Plasma is generated near needle electrodes and aerosol is introduced from above the electrode gap by pneumatic nebulizer, which avoids damage to the fragile device. The waveforms of applied voltage and discharge current, optical emission spectrometry, the rotational and vibrational temperature of N\({}_{2}\) (C\({}^{3}\)\(\uparrow\)\(\mathrm{I}\)u), and temperature evolution of nebulized discharge are measured. In addition, the heavy metal detection mechanism is studied in depth, and the effects of applied voltage, gas flow rate, pH value, and organic matter on the atomic emission intensity of Cu are investigated to determine the optimal experimental conditions. Eventually, the working curve and LOD of Cu are calculated, and the LOD of Cu is compared with the present studies.
## 2 Design of WSNs with Non-Binary LDPC Coding
### The Basic Principles of WSNs Coding with Non-Binary LDPC Codes
The advantage of Gaussian elimination coding algorithm is that the coding principle is simple but the computational complexity is large and the computational complexity is high. Therefore, the non-binary iterative coding algorithm is studied in this paper [25; 26; 27]. If the check matrix of the non-binary LDPC code has a lower triangular structure as shown in Figure 2, the elements on the diagonal lines are all non-zero elements on the Galois Field and the remaining non-zero elements are all on the left side of the diagonal, iterative encoding algorithm can be used directly for encoding. Set the code word vector as \(\mathbf{c}\in F^{n}\), divide it into two parts, namely information vector \(\mathbf{s}\in F^{n-m}\) and check bit vector \(\mathbf{p}\in F^{m}\), namely \(\mathbf{c}=(\mathbf{s},\mathbf{p})\), the n the encoding algorithm is specifically shown in Equation (1). \[p_{l}=\left(\sum_{i=0}^{k-1}h_{i,j}\cdot s_{j}+\sum_{i=0}^{l-1}h_{i,j+k}\cdot p _{j}\right)/h_{l,k+l} \tag{1}\]  
Among them, \(l\in[0,n-k-1]\), \(h_{i,j}\) represents the element on the \(i\)-th row and \(j\)-th column in the check matrix \(\mathbf{H}\) and \(k=n-m\). According to Equation (1), the process of the encoding algorithm is to use the constraint relationship between the rows in the check matrix \(\mathbf{H}\) to calculate the symbol value of each check bit in turn using the backward iterative method. The binary iterative encoding algorithm is improved and AND and XOR operations used in binary are improved to be multiplication and addition operations in Galois Field. In order to simplify the calculation amount, set all the elements on the diagonal to be 1 and in this case, Equation (1) is improved to Equation (2). \[p_{l}=\sum_{i=0}^{k-1}h_{i,j}\cdot s_{j}+\sum_{i=0}^{l-1}h_{i,j+k}\cdot p_{j} \tag{2}\]  
When \(w\) can be regarded as a small constant with respect to \(n\), the non-binary iterative encoding algorithm has a linear complexity, where \(n\) is the code length and \(w\) is the average column weight of the generated matrix.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 4 Performance Comparison  
### Metrics  
We adopt the commonly used metrics in the community of MDE to compare the performance of the typical algorithms. These metrics are root mean square error (RMSE), a traditional method for measuring regression errors; logarithm RMSE (RMSE\({}_{\text{log}}\)), the logarithm makes this error relative, reducing the effect of large errors with the distance; absolute relative error (AbsRel), which normalize per-pixel errors, reducing the effect of large errors with the distance; square relative error (SqRel), the squared penalizes for larger depth error; and accuracy rate metric \(\delta_{t}\), which are defined as follows [1]:  
\[\text{RMSE}:\ \sqrt{\frac{1}{N}\sum_{i\in N}\parallel d_{i}-d_{i}^{g}\parallel ^{2}} \tag{7}\]  
\[\text{RMSE}_{\text{log}}:\ \sqrt{\frac{1}{N}\sum_{i\in N}\parallel\log(d_{i})- \log\left(d_{i}^{g}\right)\parallel^{2}} \tag{8}\]  
\[\text{AbsRel}:\ \frac{1}{N}\sum_{i\in N}\frac{d_{i}-d_{i}^{g}}{d_{i}^{g}} \tag{9}\]  
\[\text{SqRel}:\ \frac{1}{N}\sum_{i\in N}\frac{\parallel d_{i}-d_{i}^{g}\parallel ^{2}}{d_{i}^{g}} \tag{10}\]  
\[\delta_{t}:\ \%\ \text{of}\ d_{i}\ \text{s.t.}\ max\left(\frac{d_{i}}{d_{i}^{g}}, \frac{d_{i}^{g}}{d_{i}}\right)<1.25^{t} \tag{11}\]  
where \(d_{i}\) and \(d_{i}^{g}\) are predictive depth and ground truth of pixel \(i\), \(N\) is the number of total pixels.
## 4 Problem Formulation and Brief System Architecture
### Prediction Algorithm  
The predictive model is a function of input feature of a disease and output target disease, which is represented mathematically in Equation (7) as  
\[T=F(\psi_{f})+e \tag{7}\]  
Where T is the predicted target disease, \(\psi_{f}\) is the set of target features which is extracted from all clinical features, and e denotes error. Equation (7) shows that if we know the clinical features, past medication, and the medical history, we can identify the disease. This equation also shows the prediction model is either a regression problem or a classification problem. If a patient wants to predict the cost of the disease, which makes an impact on his biological condition, then T is continuous, and it is a regression problem. Popular data-mining techniques such as linear regression or the generalized additive model can be used for regression analysis. In the classification problem, T is categorical, which denotes whether a patient suffers a disease or not. Some well-known techniques for predicting classification problems are Decision Tree (C5.0), Naive Bayes (NB), Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM).
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Parallel Model Training and HPC Integration  
After preprocessing, the pipeline trains multiple forecasting models in parallel. The study retains the core models with the best performance from our prior study's accuracy tests--bi-LSTM for imputation, f-LSTM for forecasting, and Random Forest Regressor (RFR) as a benchmark--optimized for the available hardware. The deep learning model, referred to as f-LSTM, is a univariate LSTM network optimized for forecasting next-day water demand based on recent historical data. The classical model is a Random Forest (RF) regression model, which serves as a baseline to assess the trade-offs between advanced deep learning techniques and traditional machine learning methods in HPC settings. All model training procedures were accelerated or distributed using HPC tools, as detailed below.
## 2 Materials and Methods  
### Experimental Study  
The experimental setup was previously used by a co-author of present study [12; 13]. The general setup included a solar air heater, namely a solar collector that was 120 cm in length, 80 cm in width, and 40 cm in height. It should be noted that, in order to make the values realistic, the dimensions of a frequently sold collector produced by a collector company were taken as basis. Its transparent cover, casing material, and insulation material consisted of 4 mm-thick window glass, 3 cm-thick Styrofoam, and chipboard wood, correspondingly. The collector faced south and was tilted at a 38\({}^{\circ}\) horizontal inclination. Figure 1 depicts a picture of the experimental arrangement.  
As mentioned above, this study aimed to analyze a solar collector system with stainless-steel scourers on the absorber surface. Stainless-steel scourers are mostly used in homes for cleaning and scrubbing tasks. Three \"handfuls\" in a packet cost only around USD 1, indicating that the inclusion of scourers has no financial impact on the overall collector system cost. This substance is structured like metal \"swarf\", which is metal cuttings from metal turning operations. It has a very regular rectangular 400-500 \(\upmu\)m by 20-30 \(\upmu\)m, and is fashioned into \"disks\" that fit in the palm of a human hand. Since this is a \"random structure\" material, one must carefully press the material to the proper bulk density (or porosity) while accounting for the \"rectangular wire's\" actual average cross section in order to manage the hydraulic radius [14; 15].  
The most important part of the solar air collector is the absorber surface. Three different absorbing surfaces were used in the experimental study. The construction of absorber surfaces guarantees the availability of airflow on both sides of the surface to increase efficiency. The absorber surfaces were created using 180 porous steel wool, which was attached to the top and bottom of a 1.5 mm-thick galvanized sheet with a type of silicone glue. This sheet was coated with black matte paint to form a surface that absorbs radiation, consistent with the bonding procedure. The first of the absorber surfaces contained 102 scourers in a complex arrangement (C1), as displayed in Figure 2a, and the second contained 78 scourers in a regular row (C2), as presented in Figure 2b. No scourer was used on the third absorber surface (C3), as shown in Figure 2c.  
The experiments were carried out in the city of Elazig, located in the Middle East region of Turkey, in a one-month period between the 1 st and 30 th of July in 2020. The measurements were conducted with two air flow rates (0.05 and 0.025 kg/s) and three distinct absorber surface geometries (C1, C2, and C3). The inlet and outlet air temperature and 4-point surface temperatures were the obtained parameters that were thought to affect efficiency. Hence, a comprehensive numerical analysis was performed to obtain more information. The following section introduces the numerical analysis.  
Figure 1: Experimental setup.
## 2 Related Work
### Transfer Learning  
Supervised techniques necessitate a substantial amount of labeled data, while they demand significant labor and time when gathering data within an organization's internal network. Furthermore, because cyberattacks exhibit diverse patterns, the network behavior distribution fluctuates, rendering pre-built models ineffective, and thus it requires repeatedly retraining models with fresh labeled data. To overcome the scarcity of labeled data in the target domain, transfer learning has emerged as an effective approach. Transfer learning aims to transfer knowledge from a source domain with abundant labeled data to a target domain with limited labeled data [31; 32].  
Transfer learning approaches are categorized into three classes based on the nature of knowledge transfer: instance-based, model-based, and feature-based. In the realm of instance-based methods, the objective is to harness the potential of data samples from a source domain to enhance the learning process in a target domain. A notable illustration of this approach is the TrAdaBoost framework, which employs a small volume of fresh data to selectively filter out outdated data distributions [33]. This is achieved through iterative updates of sample weights, guided by the predictive errors of a basic learner. Model-based methods, on the other hand, focus on extracting deep learning model parameters that can be effectively shared across different domains. In the realm of feature-based methods, the goal is to identify a common latent feature space where the mapped samples from each domain exhibit closely aligned probability distributions. [PERSON] et al. explore this approach by selecting covariate invariant features between training and testing datasetsand subsequently employing linear discriminant analysis (LDA) for dimensionality reduction [34]. Meanwhile, [PERSON] et al. propose embedding data samples into a reproducing kernel Hilbert space (RKHS) while minimizing the maximum mean discrepancy (MMD) between domains [35]. This process leads to the derivation of a kernel matrix, which, in turn, yields low-dimensional representations of data samples using kernel-PCA. Moreover, the authors also proposed the transfer component analysis (TCA) method that takes a unified approach to kernel learning, aiming to attain a low-rank representation by minimizing distribution disparities while preserving data variance [36]. This technique offers a comprehensive means of knowledge transfer by combining distribution distance minimization with data variance preservation.
###### Abstract  
This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms. Radio clustering is a method of searching for similar signals within the analyzed part of the radio spectrum. Typically, it is based on one or several specific parameters processed from the signal in a given channel. The authors propose a slightly different, innovative approach; thanks to the self-supervised learning of neural networks, there is no need to define specific parameters, and the feature vector, enabling comparison of Euclidean distances between signals, is generated by a deep neural network trained using a contrastive loss function on a dataset containing different radio modulations. The authors describe self-supervised solutions based on contrastive learning and the methods of signal segmentation and augmentation. The training process utilizes a custom database and the Resnet-50 network with a contrastive cost function. Radio clustering is used for autonomous spectrum analysis across wide frequency ranges and enables, among other things, the detection of tactical radio stations operating with widely dispersed frequency-hopping or a significant reduction in computational power required for real-time analysis of a large number of radio signals.  
RFML; radio frequency machine learning; unsupervised deep learning; AI; contrastive loss; cognitive radio; RF clustering; CNN; SimCLR +
Footnote †: journal: Medical Informatics
only statically but also dynamically, depending on the radio conditions. Furthermore, with increasing challenges in managing radio spectrum occupancy, there is a growing demand for cognitive and intelligent radio station solutions that rely on SDR and machine learning elements.  
The article explores the use of radio spectrograms in machine learning, specifically in the radio signal clustering present in spectrograms, using deep neural networks.
## 4 Discussion  
Estimating the parameters of articulated objects without observing their various states is a challenging task, even for humans who have experience in understanding how such objects behave. When faced with a new and unfamiliar object, humans can make mistakes in predicting its nature, such as mistaking a cabinet for a drawer or incorrectly determining the rotation axis of a cabinet. Therefore, this task is even more difficult for computers.  
However, the results presented in this paper demonstrate that it is indeed possible to create a system capable of building an environment model in the form of a point cloud using information about articulated objects without the need to observe them in different states. This achievement is made possible by leveraging the latest advancements in convolutional neural networks, combined with the development of a large dataset required for training the network and employing classical image processing methods. Our experience shows that around 2000 samples for rotational and translational fronts, 5000 samples of handles, and 1700 samples of rotational axes are sufficient to obtain a neural network that generalizes well to previously unseen examples. By utilizing neural networks, computers can emulate the behavior of humans who estimate the parameters of articulated objects by analyzing the shape, dimensions of the fronts, and handle positions and comparing them with past observations.  
The results obtained on test sets illustrate that the system can successfully detect an object, identify the handles for manipulating the object, determine the object's class, and estimate either the rotation axis or translation based on the assigned class. Remarkably, all of this can be achieved using a single RGB-D pair of images. Consequently, the model estimation does not necessitate interacting with the object or observing it in various states. This approach minimizes the risk of damaging an object with unknown parameters and enables faster determination of the object's parameters.  
The article introduces four modules, two of which are independent and responsible for handle detection and front detection and segmentation. The other two modules are dependent on the outputs of the preceding nodes. The first dependent node focuses on rotation axis detection and utilizes the results from handle and front detection. The second dependent node constructs an environment model by enriching the point cloud with the outputs of the other three nodes.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 5 Finite Element Model Updating of Ningbo Bund Bridge
### Establishment of WNN  
Seven updating parameters were divided into 31 levels within the range of \(-\)15% to 15% with a step size of 1%, and the uniform design table U31(31\({}^{7}\)) was employed for experimental design to obtain input samples. The parameters were brought into the initial finite element model of the bridge in turn, and the corresponding response output samples were obtained. Then, the input and output samples were, respectively, normalized and used as training samples of the neural network.  
The number of nodes in the input layer of the network was set to seven and that of nodes in the output layer to eight; according to Equation (22), the number of nodes in the hidden layer of the network was selected as 15; the learning rate \(\eta\) of the network was selected as 0.01, and the momentum factor \(a\) was chosen as 0.5. In addition, the excitation function of the hidden layer adopts the Morlet wavelet function, and the excitation function of the output layer adopts the tansig function.  
When the network is established, the WDO algorithm and the normalized training samples are used to optimize the connection coefficient matrix of the WNN preliminarily. Secondly, the MSE function is adopted as the network performance function to train the WNN further. The learning and training process of the WNN is shown in Figure 13.  
It can be seen from the figure that the WNN obtained a small network error after 100 times of training and learning, and the MSE value is only 3.7 \(\times\) 10\({}^{-5}\). The network training is efficient, and the network error is small; thus, it can be used as the surrogate model for the initial finite element of the bridge.  
Figure 13: The training process of WNN.
## 5 Conclusions  
The main target of the study was to determine if, from the beneficiary point of view, the modification of fuel composition from 100% CH\({}_{4}\) to a mixture of up to 20% H\({}_{2}\) and 80% CH\({}_{4}\) will imply operational difficulties, when using small condensing boilers (water-tube corrugated coil and finned pipes boilers). This measure may be useful even in the current international context in which the volume of gas available and imported by the European Union is expected to decrease.  
The study conclusion is that an increase in the combustible flow with 16% will compensate the maximum H\({}_{2}\) concentration situation with no other implications over the boiler's thermal efficiency, either from sensible, latent, or overall point of view, while also maintaining the useful boiler's thermal power output. At the same time, for the previously specified domain, the boilers' CO\({}_{2}\) emissions will decrease with nearly 7%.  
Considering an example of boiler's conventional yearly functioning of 2800 h at nominal thermal load, an estimate reduction of 900 to 1000 kg of CO\({}_{2}\) emitted within the flue gases is attainable. As a main conclusion, from the boiler's operating perspective, the reduction in CO\({}_{2}\) emissions by replacing CH\({}_{4}\) with H\({}_{2}\) is perfectly feasible under the conditions outlined in the present study.  
The novelty of the study comes from the fact that the literature up to date do not approach thoroughly the effect of mixtures made up from 0 \(\div\) 20% H\({}_{2}\) and 80% CH\({}_{4}\) on condensing boilers. Particularly, experimental and theoretical studies have been conducted aiming at other type of burning equipment or boilers without condensing. The current paper combines experimental measurement with modelling programs in order to have a much broader and clearer view of the process.  
This paper provides for the first time in the available literature important conclusions that can be successfully used in further research. A 254,700-ton CO\({}_{2}\)/year reduction may result if all newly purchased wall-hung condensing boilers from Romania between 2019 and 2024 would be fed with a 20% H\({}_{2}\) and 80% CH\({}_{4}\) gaseous mixture.  
Moreover, another novelty is represented by the employment of experimentally obtained data, measured in a European Certified Laboratory in order to develop and validate a computational modelling program, which can be further used in order to evaluate the boiler's response to various changes in operational parameters.  
Conceptualization, R.C.; Investigation, R.C.; Methodology, N.N.A.; Supervision, D.-P.S.; Writing--original draft, R.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.