esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Solution of The Digital Beamforming Matrix and The Analog Beamforming Matrix
### Solution of The Analog Beamforming Matrix Based on The MM Algorithm
\begin{array}{ll}\textbf{F}_{RF,i}^{H}\textbf{F}_{BB}^{(n)}\textbf{F}_{BB}^{ (n)H}\textbf{F}_{RF,i}&\leq\lambda_{\max}\Big{(}\textbf{F}_{BB}^{(n)}\textbf{ F}_{BB}^{(n)H}\Big{)}\textbf{F}_{RF,i}^{H}\textbf{F}_{RF,i}+2\Re\Big{(}\textbf{F}_{ RF,i}^{H}\Big{(}\textbf{F}_{BB}^{(n)H}\textbf{F}_{BB}^{(n)H}-\lambda_{\max} \Big{(}\textbf{F}_{BB}^{(n)}\textbf{F}_{BB}^{(n)H}\Big{)}\textbf{I}\Big{)} \textbf{F}_{RF,i}\Big{)}\\ &+\textbf{F}_{RF,i}^{(q)H}\Big{(}\lambda_{\max}\Big{(}\textbf{F}_{BB}^{(n)} \textbf{F}_{BB}^{(n)H}\Big{)}\textbf{I}-\textbf{F}_{BB}^{(n)}\textbf{F}_{BB}^{ (n)H}\Big{)}\textbf{F}_{RF,i}\end{array}  \tag{91}\]  
According to (90), the surrogate function of the objective function in the problem \(P_{3}\) can be expressed as:  
\[ \begin{array}{ll}P_{3}:\underset{F_{RF}}{min}&\sum\limits_{i=1}^{N}F_{i}^{H} F_{i}-2\Re\Big{(}F_{i}^{H}F_{BB}^{(n)}F_{RF,i}\Big{)}+F_{RF,i}^{H}F_{BB}^{(n)}F_{BB}^{ (n)H}F_{RF,i}\Rightarrow\\ P_{4}:\underset{F_{RF}}{min}&\sum\limits_{i=1}^{N}F_{i}^{H}F_{i}-2\Re\Big{(}F_{i} ^{H}F_{BB}^{(n)}F_{RF,i}\Big{)}+\lambda\Big{(}\textbf{F}_{BB}^{(n)}F_{BB}^{(n)H }\Big{)}_{RF,i}^{H}\textbf{F}_{RF,i}\textit{max}\\ &+2\Re(F_{RF,i}^{H}(F_{BB}^{(n)H}F_{BB}^{(n)H}-\\ \end{array}  \tag{92}\]  
\[\lambda(F_{BB}^{(n)}F_{BB}^{(n)H})_{max}()_{RF,i})()^{(q)H}_{RF,i}(\lambda(F_{ BB}^{(n)}F_{BB}^{(n)H})_{BBBB}^{(n)(n)H}\textbf{max}()_{RF,i})),\end{array} \tag{93}\]  
It is worth noting that the first and third terms of the objective function in the problem \(P_{4}\) are constant terms, and the last term is independent of the variable \(\textbf{F}_{RF,i}^{H}\). After ignoring the above three items, the problem \(P_{4}\) can be converted to the following projection problem:  
\[ \begin{array}{ll}P_{5}:&\underset{F_{RF}}{min}&\sum\limits_{i=1}^{N}\Big{\|} \textbf{F}_{RF,i}-\textbf{c}_{i}^{(q)}\Big{\|}_{2}^{2},\\ &s.t.\ C_{1}:\textbf{F}_{RF}\in\mathbb{F},\end{array}  \tag{94}\]  
where \(\textbf{c}_{i}^{(q)}=\textbf{F}_{BB}^{(n)}\textbf{F}_{i}-\Big{(}\textbf{F}_{BB}^{ (n)}\textbf{F}_{BB}^{(n)H}-\lambda_{\max}\Big{(}\textbf{F}_{BB}^{(n)}\textbf{ F}_{BB}^{(n)H}\Big{)}\textbf{I}\Big{)}\textbf{F}_{RF,i}^{(q)H}\). Therefore, the following closed form solution can be obtained for the problem \(P_{5}\), which is reflected in line three of the **inner algorithm** in Algorithm 3:  
\[ \begin{array}{ll}\textbf{F}_{RF,i}=e^{\text{iarg}(c_{i}^{(q)})},\forall i, \end{array}  \tag{95}\]  
\[ \begin{array}{ll}\textbf{F}_{RF}=e^{-\text{iarg}(C^{(q)T})},\end{array}  \tag{96}\]  
where \(C^{(q)}=\textbf{F}_{BB}^{(n)}\textbf{F}^{H}-\Big{(}\textbf{F}_{BB}^{(n)}\textbf{ F}_{BB}^{(n)H}-\lambda_{\max}\Big{(}\textbf{F}_{BB}^{(n)}\textbf{F}_{BB}^{(n)H}\Big{)} \textbf{I}\Big{)}\textbf{F}_{RF}^{(q)H}\), which is reflected in line two of the **inner algorithm** in Algorithm 3.
# Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization
## 5 Results and Discussion  
### Tests on the Dataset  
In this section, the proposed wheel odometry error prediction model based on Transformer was compared with the LSTM model proposed in [21] and the WhONet model proposed in [22] to evaluate the model performance using the metrics described in Section 4.2.
## 2 Materials and Methods
### Marginal Adaptation  
All segments from both groups were incrementally sectioned (1 mm and 2 mm from the apex) using a carborundum disc (Dentorium Products Co., Inc., Farmingdale, NY, USA) mounted on a handpiece, with constant irrigation and perpendicular to the long axis of the tooth. This procedure resulted in 48 cuts, corresponding to two fragments for each sample--one apical and one cervical of the plug. Thus, for the evaluation of marginal adaptation, a total of 12 apical samples (1 mm from the apex) and 12 cervical samples (2 mm from the apex) were included for both White ProRoot MTA(r) and TotalFill(r) BC RRM Fast Set Putty(tm).  
The fragments were polished with an 800 \(\upmu\)m carbide silicone disc (Hermes Schleifmitt GmbH, Hamburg, Germany) under water irrigation. This step aimed to eliminate debris resulting from cutting and optimizing the visualization of the samples.  
The assessment of marginal adaptation between the endodontic cements used and the canal walls was conducted using a scanning electron microscope (SEM, Hitachi FLEXSEM 1000-Higashitoyoi, Kudamatsu City, Yamaguchi Prefecture, Tokyo, Japan). Images were captured at different magnifications: 190\(\times\), 250\(\times\), 270\(\times\), 500\(\times\) and 1000\(\times\) at 10 kV (Figure 2). For each tooth's apical plug, two SEM images were collected--one for the apical cut and another for the cervical cut--allowing for the evaluation of any possible differences in material adaptation between the two locations.  
The determination of the perimeter of gaps and the perimeter of the root canal in the sections was carried out by two independent evaluators ([PERSON] and [PERSON]) using the ImageJ(r) 1.3 software (National Institutes of Health, Madison, WI, USA). The marginal adaptation discrepancy was calculated for each sample following the formula:  
\[\text{Percentage of marginal adaptation discrepancy}=(\text{perimeter of gaps}\times 100)/\text{perimeter of the root canal.}\]  
Figure 1: Radiographic image after placement of apical plug (**A**) with White ProRoot MTA® and with TotalFill® BC RRM Fast Set PuttyTM (**B**).
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 2 Related Works  
### Traditional Point Cloud Completion Methods  
Traditional point cloud completion methods can typically be categorized into geometry-based methods and alignment-based methods [16]. Geometry-based methods leverage the geometric features of the original point cloud for shape completion. In geometry-based point cloud completion methods, [PERSON] et al. [5] used interpolation to fill gaps in the incomplete point cloud. [PERSON] et al. [17] based on regular structural repetitions, identified symmetry in the input point cloud, and predicted the missing pieces. These methods, are sometimes useful in specific situations; however, the input point cloud is required to have symmetry with a low missing rate and may result in the loss of some fine-grained details.  
Alignment-based methods aim to achieve point cloud shape completion by finding matching primitives from a model database. In alignment-based point cloud completion methods, [PERSON] et al. [18] segmented building point clouds into planar patches and used an improved global fitting approach to estimate the parameters of corresponding building primitives, progressively completing the missing regions. [PERSON] et al. [19] employed geometric attributes and a set of semantic rules combined with Markov random fields to classify building scenes and fit corresponding planar structures to complete missing areas. Although these methods are effective, retrieving matching building primitives from a model library often requires significant computational resources, and maintaining the reliability and accuracy of retrieval necessitates regular updates to the model library to reflect new building elements and their variations.
## 3 Software Design of Frequency Stabilization System  
The software is written based on the LabVIEW platform and adopts the production-consumer and finite state machine (FSM) structures. The producer-consumer structure completes the data transfer between multi-While loops through queues. It saves the SAS signal, error signal, and scanning signal as technical data management streaming (TDMS) files, which is convenient for subsequent analysis and processing. The FSM structure mainly jumps or loops between the states of initialization (S0), waiting for the key to be pressed (S1), obtaining the reference voltage and error fragment (S2), incremental PI control (S3), and automatic relock (S4), where the program for S2 is shown in Figure 5. After the program is powered on, the system enters S0 to recover the key state and clear the interface data. When the \"Obtain reference\" key is pressed, the state jumps from S1 to S2, obtaining the reference voltage and error fragment. When the \"Automatic\" key is pressed, the state jumps to S3 to realize closed-loop control. If the system is unlocked, it will jump to S4 to rescan. When scanning to the desired lock point, jump back to S3 to realize the automatic frequency stabilization of the system. The following mainly analyzes S2, S3, and S4.
## 2 Literature Review
PCA has been widely used in the field of active thermography to enhance defect detection. Among them are [PERSON] et al. [31], where they leveraged PCA to process infrared (IR) image sequences gathered from the application of optical thermography (flash thermography and harmonic thermography) to the steel plate, opaque plastic, and carbon fibre-reinforced plastic, aiming to extract meaningful features and reduce redundancy in thermal NDT. [PERSON] et al. [32] introduced principal component thermography to improve the defect contrast and depth characterisation. Although principal component thermography is effective for feature extraction and dimensionality reduction, it does not promote sparsity, complicating result interpretation [33]. It is also sensitive to the noise and outliers in the dataset [34]. To address these limitations, various PCA variants have been introduced. [PERSON] et al. [35] applied low-rank sparse principal component thermography (Sparse-PCT) to enhance defect segmentation in infrared thermographic data, demonstrating significantly improved accuracy in detecting subsurface defects, particularly in composite materials. [PERSON] et al. [36] demonstrated RPCA outperformed conventional PCA, or principal component thermography (PCT), in terms of contrast-to-noise ratio when applied to PTdata from CFRP samples containing artificial defects. [PERSON] et al. [37] demonstrated that RPCA outperformed conventional PCA, sparse PCA, and kernel PCA in detecting defects on building surfaces, particularly under challenging conditions such as shadow interference and image noise. While prior studies have demonstrated the potential of PCA and RPCA in thermographic inspection, their comparative performance in LVT for detecting barely visible impact damage (BVID) in CFRP materials remains underexplored. This study addresses that gap by evaluating and comparing PCA and RPCA for thermal data enhancement in LVT and quantifying their effectiveness using SNR. To ensure the reliability of the findings, the results are also cross-validated using PT and PAUT.
## 1 Introduction
The main goal of the present study is to analyze the influence of the EV charging process on the quality parameters of electricity, especially generated harmonics. In order to analyze these influences, tests were carried out using different cars (EV and PHEV) and electric scooters. In the literature, one can find studies directly related to the subject of the article, but they mainly concern the impact of higher-power chargers on the operation of the network, e.g., analysis of energy quality parameters for a 160 kW charger at various load levels [36] and analysis of the presence of harmonics in a 33 kV AC line supplying a charging station near a motorway [37]. One can also find information on the analysis of charging profiles and current parameters for a selected point in the network, e.g., for a town in Canada [38]. This article supplements the available works with an analysis of the possible impact of the charging process on the quality parameters of electricity supplying other loads in the home electrical installation system. This topic seems to be particularly important because due to the smaller dimensions and weight of on-board chargers when compared to off-board ones, it seems significantly more difficult to ensure high operating parameters and compliance with standards regarding the quality of electricity. Among the most important parts are the rectifier and filters, which can influence current harmonics the most. The structure of the article is as follows: In Section 2, background information about harmonics and a description of the test setup can be found. In Section 3, information about the tests conducted and the results obtained from experimental studies are presented. THD, which is an important parameter in identifying power quality issues, was measured and analyzed for the EV charging process. The final section contains conclusions from the conducted experiments and analyses.
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 2 Materials and Methods
### Measurement-Level Hybridization Method  
This method operates in the domain of measurements, combining GNSS pseudoranges (between GNSS satellites and the UE) with 4G/5G pseudoranges (between the BS and the UE) within a unified system of measurement equations. GNSS pseudoranges are obtained from RINEX files recorded using the Android-based application Geo++ RINEX Logger.
## 6 Conclusions  
The CSI feedback for FDD transmission is an open problem and DL-based techniques have made a new tool available to attempt to solve it. The channel state information availability is of crucial importance for MIMO communications and for 5G New Radio. In this paper, we investigated the potential of a neural network technique to serve as an alternative to the traditional feedback reporting block. From the work of the authors reported in [12], we obtained a NR-CsiNet model, able to deal with SU-MIMO systems and providing channel estimation capabilities. Simulations were performed by exploiting a 5G NR-compliant physical layer simulator with a CDL channel model, which enables modelling of the complex environment envisioned by the 5G network. The results showed that our DL-based feedback system can achieve performances comparable to those obtained with a traditional PMI-based reporting technique and clearly outperformed it in some cases. The framework and the data collected in this study provide a solid basis for more in-depth tests of the capabilities of DL for CSI feedback in 5G NR, enabling investigation of a larger number of scenarios and different configurations, such as larger MIMO systems (i.e., massive MIMO), and exploitation of the channel matrix to support spatial division multiplexing solutions, that are currently difficult to obtain in FDD mode.  
Moreover, our objective was to highlight useful aspects of research on this topic, which is of significant interest considering the fundamental role that MIMO systems play in network performance improvement.  
Conceptualization, R.G. and R.F.; formal analysis, E.Z., D.G.R. and R.G.; investigation, E.Z., D.G.R. and R.T., software, E.Z. and R.F.; supervision, D.G.R. and R.G.; writing--original draft preparation, R.T. and E.Z.; writing--review and editing, D.G.R., R.T. and R.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available from TIM S.p.A. Restrictions apply to the availability of these data, which were used under licence for this study. Data are available from the corresponding author, D.G.R., with the permission of TIM S.p.A., upon reasonable request.  
[PERSON] acknowledges support from TIM S.p.A. through the Ph.D. scholarship.  
The authors declare no conflict of interest.
## 4 Experimental Analysis
### Datasets
#### 4.1.1 Mai  
The mathematical analysis of images (MAI) ear dataset [30] comprises 700 images sourced from 100 individuals spanning ages 19 to 65 years. For each participant, seven images are taken, including one of the left ear and six of the right ear. The data collection is conducted indoors, ensuring consistent illumination conditions for all acquisitions. The images are formatted as JPEGs and possess a resolution of 492 \(\times\) 702 pixels.
# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 2 Methodology
### Cnn  
The CNN structure is shown in Figure 1, which mainly consists of three parts: convolutional layer, pooling layer and fully connected layer [21]. It can use convolutional and pooling layers to extract feature information [22].  
The convolution layer is the core of the CNN model. The convolution calculation uses the convolution kernel to perform a sliding window operation on the input data to extract  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\left|\text{PCC}_{xy}\right|\) & **[0, 0.2]** & **[0.2, 0.4]** & **[0.4, 0.6]** & **[0.6, 0.8]** & **[0.8, 1.0]** \\ \hline \multirow{2}{*}{Classification} & Extremely low or & \multirow{2}{*}{Low correlation} & Moderate correlation & Strong correlation & Extremely strong correlation \\  & irrelevant & & correlation & correlation & correlation \\ \hline \end{tabular}
\end{table}
Table 1: Classification Criteria for Correlation Coefficients.  
Figure 1: The structure of CNN.  
features. In addition, this process considers the local variance of the input data in order to capture more comprehensive information about the variation and structure of localized regions, as shown in Equation (2) [23].  
\[c_{n}=f(F\cdot\mathrm{X}_{n}+b) \tag{2}\]  
In Equation (2), the convolution kernel is \(F,F\in R^{\text{1-}d}\). \(l\) is the height of the convolution kernel, and \(d\) is the width of the convolution kernel. \(b\) is the bias parameter, \(f\) is a ReLU nonlinear function, \(\mathrm{X}_{n}\) is a key parameter related to the power consumption of the SAG mill, \(n\in[1,\infty)\) is the number of key parameters, \(\cdot\) is the convolution operation, and \(c_{n}\) is the result of the convolution calculation. The convolution kernel selected in this paper is \(3\times 1\); that is, the height is 3 and the width is 1. The convolution window slides down at a step size of 1 to obtain the local feature vector \(\mathrm{C}=(c_{1},c_{2},\ldots,c_{n})\) of the input parameter.  
The role of the pooling layer is to sample the convolution results, reduce the size of the convolutional vectors, and prevent overfitting. All sampled eigenvalues are combined into the output \(M=(\mathrm{M}_{1},M_{2},\ldots,M_{n})\) of the CNN. This paper adopts the maximum pooling method, and the formula is as follows:  
\[M_{i}=\max(C_{i}) \tag{3}\]  
Here, \(M_{i}\) refers to the result of applying max pooling to the \(C_{i}\)-th convolutional result, \(i\in[1,n]\).
## 4 Computational Experiments  
### Stability of the Results  
Local descent algorithms have a significant disadvantage. They converge to the nearest local optimum of the objective function. Therefore, it is necessary to investigate the extent to which the values of the resulting solutions depend on the starting points. For this reason, in the first iteration of the optimization algorithm random routes should be used, rather than the shortest ones.  
The developed algorithm was tested on data of a network consisting of 40 nodes and 2000 target requests. The network structure was fully connected. During the first iteration, a route consisting of approximately 25 nodes was added to the archive of each target request. Such a length of the route is sufficient, since the route length will not exceed 4 nodes in the optimal solution (if we discard unimportant routes). The starting routes can contain loops and channels with low bandwidth capacity.  
Having carrying out 1000 numerical experiments on our algorithm with starting routes of random length, we obtained the results presented in the histogram (Figure 5).  
From Figure 5, the diversity of values within the objective function is approximately 0.1% of its average value. When using the shortest starting routes, the value approaches the average obtained value of the objective function.  
The objective function may have numerous local optima with similar values. For each solution, the part of \"important\" routes located in the main solution is calculated. We define \"important\" as a route transmitting more than 5% of all information of the target request. The distribution of the ratio of the matching routes is presented in Figure 6. It can be seen from the figure that the main solution, obtained with the shortest routes taken as the starting ones, forms 92-94% of the resulting solution. The slight difference in the compared routes can be explained by the fact that a small part of them in the framework of the algorithm may be defined as \"important\" or as \"unimportant\" routes. This is caused by data oscillations observed at the end of the algorithm and the fixed step of gradient descent.  
It is important to mention that the optimization algorithm proposed in this work consists of two parts. The first is estimating derivatives (finding the gradient), and the second is finding the shortest routes using [PERSON]'s algorithm, which belongs to the category of greedy algorithms. Therefore, the proposed algorithm can be classified as a greedy-gradient algorithm.
###### Abstract  
This paper proposes a hybrid WAAM (wire arc additive manufacturing) and milling process (HWMP), and highlights its application in the fabrication of stiffened panels that have wide applications in aviation, aerospace, and automotive industries, etc. due to their light weight and strong load-bearing capability. In contrast to existing joining or machining methods, HWMP only deposits stiffeners layer-by-layer onto an existing thin plate, followed by minor milling of the irregular surfaces, which provides the possibility to significantly improve material utilization and efficiency without any loss of surface quality. In this paper, the key performances of HWMP in terms of surface quality, material utilization and efficiency are evaluated systematically, which are the results of the comprehensive effects of the deposition parameters (e.g., travel speed, wire-feed rate) and the milling parameters (e.g., spindle speed, tool-feed rate). In order to maximize its performances, the optimization is also performed to find the best combination of the deposition and the milling parameters. The case study shows that HWMP with the optimal process parameters improves the material utilization by 57% and the efficiency by 32% compared against the traditional machining method. Thus, HWMP is believed to be a more environmental friendly and sustainable method for the fabrication of stiffened panels or other similar structures.  
additive manufacturing; wire arc additive manufacturing; 3D printing; hybrid manufacturing; milling; stiffened panel +
Footnote †: journal: Applied Soft. **2017**, \(7\), 1233; doi:10.3390/app7212233