esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The accuracy of an acoustic emission (AE) source location is always corrupted by outliers due to the complexity of engineering practice. To this end, a preconditioned closed-form solution based on weight estimation (PCFWE) is proposed in this study. Firstly, nonlinear equations are linearized, and initial source coordinates are obtained by using equal weights. Residuals, which are calculated by source coordinates, are divided into three regions according to normal distribution. Secondly, the weight estimation is developed by establishing the relationship between residuals and weights. Outliers are filtered by the iteration between the weight estimation and source location. Subsequently, linear equations are reconstructed with the remaining measurements containing no outliers, while they are ill-conditioned. Finally, the preconditioning method is applied to weaken the ill condition of the reconstructed linear equations, so as to improve the location accuracy. This new method is verified by a pencil-lead break experiment. Tests results show that the location accuracy and stability of the new method are superior to traditional methods. In addition, outlier tolerance and the velocity sensibility of the new method are investigated by simulating tests.  
acoustic emission; closed-form solution; outlier; time difference of arrival; weight estimation 2018
###### Abstract  
Tolerance analysis is a key engineering task that is usually supported by domain-specific analysis models and tools that are generally not connected to the system functionality. The model-based system engineering (MBSE) approach is a potential solution to this limitation, but it has not yet been deeply explored in this type of mechanical analysis, for which some problems need to be explored. One of these issues is the capacity of languages such as SysML to describe solution principles based on active surfaces that participate in functionality and are present for tolerance analysis. Thus, this study explored the possibilities that enable SysML to represent these geometries and their mathematical relationships based on Topologically and Technologically Related Surfaces (TTRS) theory and aligned with Geometric Dimensioning and Tolerancing (GD&T) standards. Additionally, the capacity of SysML to assure the consistency of tolerance analysis models is also explored, due to the limitations identified in analysis languages like Modelica. In this context, this paper presents a SysML profile for tolerance analysis modeling (SysML4 TA), containing domain-specific semantics (concepts and constraints) to assure the completeness of the analysis models and consistency between the different models considered in the integrated model of the system. Finally, a case study applied to a manufacturing context is presented to validate the capacity of SysML to solve the identified problems.  
consistency; SysML; OCL; assembly modeling; tolerance analysis; TTRS Article  
SYSML4 TA: A SysML Profile for  
in a Manufacturing System Case
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 2 Materials and Methods
### Measurements with the Tested Scanners  
Due to the time-consuming nature of this measurement, measurements using static scanners were performed only once with each scanner. The approximate locations of individual scanning stations are shown in Figure 4.  
The measurements with the Trimble X7 static scanner were carried out with a scanning time of two minutes, scanning option Standard, and a point density of 11 mm per 10 m. In view of the large number of stations (22) and the small distances between them, this point density was sufficient. The scanned clouds were automatically registered to each other during the measurements thanks to the sufficient overlap. The final registration was performed automatically during export in the Trimble Perspective software ver. 2024.00, with a mean error 1.3 mm as reported by the software.  
Measurements with the Leica RTC 360 scanner were taken from a total of 19 sites with the minimum point density set to 12 mm at 10 m. The net scanning time was 26 s per station (image acquisition and moving object filtering were disabled). During the measurements, the data were automatically synchronized with an iOS tablet and the Leica Cyclone FIELD 360 app, where the data were manually checked and overlap registration was performed. The final registration was performed in the Cyclone REGISTER 360 PLUS software ver. 2024.0.2.  
Thanks to the rapid scanning using SLAM scanners, measurement with each scanner was performed three times to also allow the evaluation of the consistency between passes. A schematic of individual measurement passes is shown in Figure 5.  
Figure 4: Approximate positions (black crosses) of the scanning stations of the static scanners.  
Figure 5: Schematic drawing of the SLAM scanner trajectories (black arrows indicate the movement direction).  
Measurements with the Emesent Hovermap ST-X scanner were performed in manual mode after initialization of 10 s, with a single back-and-forth-pass taking less than 4 min. Data registration was performed in the Aura software ver. 1.8.1.  
Similarly, the measurements with GeoSLAM ZEB Horizon RT and FARO Orbis were performed after a 10 s initialization (in a stationary position on a solid base) for approx. 10 s, each scanning pass took approx. 4.5 min.
# The Application of Fault-Tolerant Partition Resolvability in Cycle-Related Graphs
###### Abstract  
The concept of metric-related parameters permeates all of graph theory and plays an important role in diverse networks, such as social networks, computer networks, biological networks and neural networks. The graph parameters include an incredible tool for analyzing the abstract structures of networks. An important metric-related parameter is the partition dimension of a graph holding auspicious applications in telecommunication, robot navigation and geographical routing protocols. A fault-tolerant resolving partition is a preference for the concept of a partition dimension. A system is fault-tolerant if failure of any single unit in the originally used chain is replaced by another chain of units not containing the faulty unit. Due to the optimal fault tolerance, cycle-related graphs have applications in network analysis, periodic scheduling and surface reconstruction. In this paper, it is shown that the partition dimension (PD) and fault-tolerant partition dimension (FTPD) of cycle-related graphs, including kayak paddle and flower graphs, are constant and free from the order of these graphs. More explicitly, the FTPD of kayak paddle and flower graphs is four, whereas the PD of flower graphs is three. Finally, an application of these parameters in a scenario of installing water reservoirs in a locality has also been furnished in order to verify our findings.  
kayak paddle; flower graph; metric dimension; partition dimension; fault-tolerant partition dimension +
Footnote †: journal: Applied sciences
# Advances in Permeation of Solutes into Hair: Influencing Factors and Theoretical Models
## 4 Permeation Models of Solutes in Hair
### Homogeneous Diffusion Model  
The homogeneous diffusion model treats a hair as an infinitely long homogeneous cylinder with a radius \(R\). The permeation process of a solute in hair can be described by [PERSON]'s diffusion law [44]:  
\[\frac{d\mathcal{C}}{dt}=\frac{d}{dr}\bigg{(}D\frac{d\mathcal{C}}{dr}\bigg{)} \tag{4}\]  
where \(\mathcal{C}\) is the concentration of the solute in the hair at time \(t\), \(r\) is the distance from the center of the hair, and \(D\) is the diffusion coefficient of the solute in the hair.  
Generally, the data obtained from the solute permeation experiment of hair can be divided into two categories. The first category is data obtained using the concentration difference method (soak the hair in a solvent containing a certain concentration of a certain solute and calculate the amount of solute absorbed by the hair based on the change in the concentration of the solute in the solvent before and after soaking) or extraction method (the solute extracted from soaked hair) to determine solute absorption in hair with absorption time data. The second category is data obtained using spectral imaging or microscopy techniques to obtain experimental data about the concentration distribution of the solute in hair.  
For the two types of data, using Equation (4), two types of solutions can be obtained [56]:  
(1) The change in absorption of the solute in hair with time:  
\[\frac{M_{\text{t}}}{M_{\infty}}\approx 4\sqrt{\frac{Dt}{\pi R^{2}}}\qquad\text{ Early solute absorption process} \tag{5}\]  
\[\frac{M_{\text{t}}}{M_{\infty}}\approx 1-0.692\text{exp}\bigg{(}-5.785\frac{ Dt}{R^{2}}\bigg{)}\qquad\quad\frac{M_{\text{t}}}{M_{\infty}}>0.31 \tag{6}\]  
where \(M_{\text{t}}\) is the absorption of the solute by hair at time \(t\), and \(M_{\infty}\) is the maximum absorption of the solute when the absorption equilibrium is reached.  
Substituting the experimental data (\(M_{\text{t}}\) and \(t\)) into Equation (5) or (6), the diffusion coefficient of the solute in hair (\(D\)) and the maximum equilibrium absorption (\(M_{\infty}\)) can be calculated.  
(2) The concentration distribution of the solute in hair:  
\[D_{(C=C_{1})}=-\frac{1}{2t}\frac{dx}{d\mathcal{C}}\int_{0}^{C_{1}}xd\mathcal{C} \tag{7}\]where \(D_{\rm(C=C1)}\) is the diffusion coefficient of the solute in the hair when the concentration of the solute in the hair is \(C_{1}\), and \(x\) is the distance from the inside to the surface of the hair.  
Substituting the experimental data (\(C_{1}\), \(x\), and \(t\)) into Equation (7), the diffusion coefficient of the solute in the hair (\(D\)) can be calculated.  
This model mainly relies on experimental data and fits Equations (5)-(7) to obtain the diffusion coefficient and maximum absorption of the solute in the hair. However, the model does not consider the microstructure and composition of the hair, the physicochemical characteristics of the solute, the pH of the solvent, and other factors.
# Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
## 4 Reinforcement Learning Control of the AUV for the Docking Manoeuvre
### Deep Deterministic Policy Gradient
Since the policy provided by the actor is deterministic, noise needs to be added to the action during training to ensure sufficient levels of exploration. Ornstein-Uhlenbeck noise is selected as in [45]. The DDPG algorithm is summarised in Algorithm 2. ```
Output: state-action value function \(Q(\mathbf{s},\mathbf{a}|\mathbf{\theta}_{Q})\) and policy \(\mu(\mathbf{s}|\mathbf{\theta}_{\mu})\) randomly initialise the critic \(Q(\mathbf{s},\mathbf{a}|\mathbf{\theta}_{Q})\) and actor \(\mu(\mathbf{s}|\mathbf{\theta}_{\mu})\) networks with weights \(\mathbf{\theta}_{Q}\) and \(\mathbf{\theta}_{\mu}\); initialise the target critic \(Q^{\prime}\) and actor \(\mu^{\prime}\) networks with weights \(\mathbf{\theta}^{\prime}_{Q}\leftarrow\mathbf{\theta}_{Q}\) and \(\mathbf{\theta}^{\prime}_{\mu}\leftarrow\mathbf{\theta}_{\mu}\); initialise the replay buffer \(\mathcal{R}\); for each episodedo  initialise a random process \(\mathcal{N}\) for action exploration;  start from an initialise state \(\mathbf{s}\); for each time step up to maximum number of time stepsdo  select action \(\mathbf{a}=\text{clip}(\mu(\mathbf{s}|\mathbf{\theta}_{\mu})+\mathbf{e},\mathbf{a}_{\text{min}}, \mathbf{a}_{\text{max}})\) with \(\mathbf{e}\in\mathcal{N}\);  apply action \(\mathbf{a}\), get the reward \(r\) and observe the new state \(\mathbf{s}^{\prime}\);  store the transition \((\mathbf{s},\mathbf{a},r,\mathbf{s}^{\prime},d)\) in \(\mathcal{R}\) if different enough from existing data; if\(\mathbf{s}^{\prime}\) is terminalthen  reset the environment state;  end if  sample a random data minibatch \(\mathcal{M}\in\mathbb{R}\); for all transitions \(i=1,I\) in \(\mathcal{M}\)do  set the transition value to \(u_{i}=r_{i}+\gamma(1-d_{i})Q^{\prime}(\mathbf{s}^{\prime}_{i},\mu^{\prime}(\mathbf{s}^ {\prime}_{i}|\mathbf{\theta}^{\prime}_{\mu})|\mathbf{\theta}_{Q^{\prime}})\) with Equation (37);  update the critic by minimising the loss \(L=\frac{1}{I}\sum_{i}\big{[}y_{i}-Q(s_{i},a_{i}|\mathbf{\theta}_{Q})\big{]}^{2}\) with Equation (39);  update the actor policy with one step of gradient ascent with Equation (40);  update the target networks with Polyak averaging with Equations (35) and (38);  end if  update the state \(\mathbf{s}\leftarrow\mathbf{s}^{\prime}\);  end for  end for
```  
**Algorithm 2**DDPG algorithm adapted from [45].
## 2 Design Section
### Design Evolution
#### 2.2.1 The Influence of the Coupling Aperture  
Compared with classical edge-fed or probe-fed microstrip antennas, the slot-coupled microstrip antenna has many advantages, such as easy integration into arrays with active circuits. In [13], an H-shaped slot is used to couple a microstrip antenna for a wider bandwidth and easy integration with active circuits. The use of metamaterials has been proven to be an efficient approach to reducing the antenna size while maintaining satisfactory antenna performance [10].  
In the process of antenna optimization, a CSRR slot based on the metamaterial concept has been adopted to couple the [PERSON] fractal radiation patch. The dimensions of the CSRR shape (a) are the same as those in Figure 1. The H-shaped (b) slot has two 0.8-millimeter-wide and 2.5-millimeter-high apertures with the same width as that of the CSRR. Two coupled structures are located at the same position. The comparative study was carried out using HFSS to discuss the effects of different coupling apertures, as shown in Figure 2. The conventional H-shaped slot-fed antenna has dual-band resonant frequencies at 4.9 GHz and 5.1 GHz. In contrast, the proposed CSRR-fed patch has dual-band resonant frequencies at 3.9 GHz and 4.3 GHz. It can be seen that the fractal patch fed with the proposed CSRR structure achieves a dual-band resonance at much lower resonant frequencies. This novel coupled structure provides an excellent strategy for minimization of the slot-coupled antenna size.
## 3 Methodology and Data Preparation
### Probability of Detection and Sensor Distances
The spatial distribution of the probability of detection can thus be specified for the structure in relation to the sensor positions. Therefore, the discrimination among the signal classes is similar to an outlier detection. The more significant the difference between the damage and background noise signal class, the greater the sensor distances can be selected, and a reliable distinction can still be ensured. If this difference is small, only narrow sensor distances can be achieved. These relationships form the basis for the development of the generalized monitoring concept. The unique aspect of wind turbines is that, in contrast to bridge construction, where the signals propagate along a mostly steady and uninterrupted structure, transmissions from the tendon to the foundation or the transition piece are zones with high impedance, which must be considered. Figure 7: Model for determining sensor distances based on a threshold for evaluating the probability of detection. The model relies on statistical input values for the signal distributions of damage and background noise signals and signal attenuation coefficients.
## 6 Evaluation
### Plaintext Training
#### 6.1.2 Skin-Cancer Dataset  
For the Skin-Cancer dataset, we evaluated AlexNet using our proposed methods. We set the batchsize to 64, the optimizer to Adam, and the initial learning rate to 0.001. The learning rate decays to 0.5 times the original value every 10 steps, and the LotHps regularization parameter \(\lambda\) is set to 0.0005. The accuracy of the original ReLU-based model is 81.14%, while the LotHps-based model without VDT strategy reaches 78.08%, resulting in a 3.06% accuracy reduction. In contrast, the LotHps-based model trained with VDT strategy achieves an accuracy of 81.10%. We set the value of \(e_{1}\) in the smooth transition function \(\rho(x)\) to 20, meaning that the model discarded the \(loss\_act\) term after 20 epochs and only used the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Model** & **Replacement** & **Original** & **Accuracy** \\  & & & **Accuracy** & **Accuracy** & **Difference** \\ \hline \multirow{8}{*}{MNIST} & CryptoNets [9] & C-A-P-A-F & 98.95 & 99.28 & \(-\)0.33 \\  & PPCN [10] & [C-B-A-C-B-A-P]*3-F-F & 99.30 & 99.59 & \(-\)0.29 \\  & CryptoDL [12] & [[C-B-A]*2-P-C-B-A-P]*2-F-F & 99.52 & 99.56 & \(-\)0.04 \\  & Approx-ReLU [21] & [C-B-A-C-B-A-P]*2-F-A-F & 99.54 & 99.57 & \(-\)0.03 \\  & QuaiL [35] & LeNet-5 & 99.26 & 99.32 & \(-\)0.06 \\  & **LHDNN** & **[C-B-A-C-B-A-P]*2-F-A-F** & **99.62** & **99.57** & **0.05** \\ \hline \multirow{4}{*}{Cancer} & CryptoNets [9] & AlexNet & 67.26 & 81.14 & \(-\)13.88 \\  & Approx-ReLU [21] & AlexNet & 75.85 & 81.14 & \(-\)5.29 \\  & **LHDNN (no VDT)** & **AlexNet** & **78.08** & **81.14** & \(-\)**3.06** \\  & **LHDNN** & **AlexNet** & **81.10** & **81.14** & \(-\)**0.04** \\ \hline \multirow{4}{*}{CIFAR-10} & CryptoDL [12] & CNN-10 & 91.50 & 94.20 & \(-\)2.70 \\  & PACN [13] & VGG-16 & 91.87 & 91.99 & \(-\)0.12 \\  & QuaiL [35] & VGG-11 & 82.85 & 90.46 & \(-\)7.61 \\  & QuaiL [35] & ResNet-18 & 85.72 & 93.21 & \(-\)7.49 \\  & BDGM [28] & AlexNet & 87.20 & 90.10 & \(-\)2.90 \\  & **LHDNN (no VDT)** & **ResNet-20** & **88.97** & **91.58** & \(-\)**2.61** \\  & **LHDNN** & **ResNet-20** & **91.54** & **91.58** & \(-\)**0.04** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Contrast of plaintext accuracy on three datasets.  
Figure 4: Comparison of train loss and test accuracy on MNIST.  
reduction of \(loss\_out\) as the learning goal. We also tried making \(e_{1}\) fluctuate up and down by 5 steps and found that the accuracy fluctuates within 0.03%. Additionally, we evaluated AlexNet using two methods: the squared activation function [9] and Approx-ReLU [21], with a final accuracy of 67.26% and 75.85%, respectively. This is a significant difference in accuracy compared to the original ReLU-based model.
## 2 Modeling Methodology
### Local References Assignment in Mesh Elements  
The purpose is to reproduce the printing trajectories inside a finite element model. The finite element code ABAQUS is used in this study. From the CAD file representing the part, the geometry is generated. Then an adaptive mesh is generated using an eight-node brick element with reduced integration C3D8R (i.e., one integration point per element). The use of C3D8R elements is motivated by the need to reproduce the layer by layer built in the model. This choice allows a simplification of the problem. The elements heights are fixed and must be equal to the layer thickness in order to ensure the reproduction of the  
Figure 1: Workflow of the modeling methodology.  
built trajectories in a layer by layer process and facilitate the localization of elements in a certain layer (see Figure 2).  
The procedure starts by treating the G-code and defining the trajectories that only correspond to the real material deposition. This means that the trajectories where the extrusion nozzle is off are eliminated. Assigning the material's references goes through associating the material deposition trajectory with the elements of the numerical model. In each element, the vector tangent to a representative trajectory that intercepts the element faces is identified. This step requires distinguishing several cases according to the configuration of interception between the deposition trajectories and the element faces. Figure 3 demonstrates the possible cases. (i) If numerous trajectories belong to the element, the nearest to its Gauss's point is chosen. (ii) If a discontinuity between the two trajectories is the closest to its Gauss's point, the first trajectory analyzed is chosen with respect to the printing history. (iii) In the case where no trajectory intercepts the elements, the closest deposition trajectories are again selected.  
The representative trajectory selected previously is considered as the first vector of the local reference. The third vector is always the same since it is the built direction (Z) perpendicular to the building plate (XY). A complete coordinate system is defined in each element by computing the cross-product of the first and the third vectors already known. This method was successfully applied on a compact tension CT specimen where complex trajectories were reproduced inside a finite element model after assigning the adequate materials' references [24].  
Figure 3: Possible interception cases between one mesh element C3D8R and the deposition trajectories.  
Figure 2: Correspondence between the layers heights (**a**) and the elements heights in ABAQUS mesh (**b**). Every layer from the G code contains different deposition trajectories.
###### Abstract  
Entity-based information extraction is one of the main applications of Natural Language Processing (NLP). Recently, deep transfer-learning utilizing contextualized word embedding from pre-trained language models has shown remarkable results for many NLP tasks, including Named-entity recognition (NER). BERT (Bidirectional Encoder Representations from Transformers) is gaining prominent attention among various contextualized word embedding models as a state-of-the-art pre-trained language model. It is quite expensive to train a BERT model from scratch for a new application domain since it needs a huge dataset and enormous computing time. In this paper, we focus on menu entity extraction from online user reviews for the restaurant and propose a simple but effective approach for NER task on a new domain where a large dataset is rarely available or difficult to prepare, such as food menu domain, based on domain adaptation technique for word embedding and fine-tuning the popular NER task network model 'Bi-LSTM+CRF' with extended feature vectors. The proposed NER approach (named as 'MenuNER') consists of two step-processes: (1) Domain adaptation for target domain; further pre-training of the off-the-shelf BERT language model (BERT-base) in semi-supervised fashion on a domain-specific dataset, and (2) Supervised fine-tuning the popular Bi-LSTM+CRF network for downstream task with extended feature vectors obtained by concatenating word embedding from the domain-adapted pre-trained BERT model from the first step, character embedding and POS tag feature information. Experimental results on handcrafted food menu corpus from customers' review dataset show that our proposed approach for domain-specific NER task, that is: food menu named-entity recognition, performs significantly better than the one based on the baseline off-the-shelf BERT-base model. The proposed approach achieves 92.5% F1 score on the YELP dataset for the MenuNER task.  
named-entity recognition; domain adaptation; BERT pre-training; food menu +
Footnote †: 2021: 11, 6007. [[https://doi.org/10.3390/app11136007](https://doi.org/10.3390/app11136007)]([https://doi.org/10.3390/app11136007](https://doi.org/10.3390/app11136007))
## 2 Basics of QDI Circuits
### Timing Parameters of QDI Circuits  
Forward latency, reverse latency, and cycle time are the important timing parameters of interest in a QDI circuit. Forward latency refers to the worst-case propagation delay encountered in the data path (shown in Figure 2) for processing the data, and reverse latency refers to the worst-case propagation delay encountered in the data path for processing the spacer. From the above discussion, it may be noted that one complete data transaction in a QDI circuit would involve the forward latency as well as the reverse latency. The summation of forward and reverse latencies gives the cycle time. The speed of a QDI circuit is governed by the cycle time, which is equivalent to the clock period of a synchronous digital circuit.