esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The heavy weight of the sensor may cause a local distortion of the cable, which can affect the global measurement performance of the device. Lastly, a monitoring system which carries out sag estimation on the basis of angular measurements acquired through wireless sensors is proposed in [13]. This represents an interesting solution since measurements obtained with wireless devices are expected to be used in smart grids [14]. Other works reporting sag identification techniques based on tiltmeters or accelerometers used as inclinometers can be found in [15; 16; 17]. Analyzing the literature, methods relying on angular measurements appear to be more robust solutions to obtain reliable sag estimations. Therefore, the main idea of this work is to develop a simple and robust method for monitoring sag variations on an operative line. The method is based on the assumption that a taut cable can be modelled with a parabolic function, resulting in a mathematical procedure based on simple equations and easily measurable geometrical quantities. The measurements of these geometrical quantities must be carried out with smart, low cost, and light devices. Moreover, the need of a continuous monitoring of the sag parameter on the line implies the presence of a device permanently installed on the structure. The sensing devices can compute measurements directly on-board and communicate data wirelessly in real time. Authors of this paper have previously developed a smart system for monitoring wind-induced vibrations on HVTL conductors composed of wireless sensor nodes and a gateway [18]. These sensors are equipped with a triaxial MEMS accelerometer which can work as an inclinometer by measuring the acceleration of static components. For this reason, it seemed reasonable to take advantage of this system already validated and employed on the field to carry out angular measurements for the sag estimation. This would represent an integrated, robust, and low-cost solution. The correlation between the value of the conductor slope at the extremity of the span and the maximum sag is presented in Section 2. A simple formula for sag estimation using sensor node measurements is obtained from two different mathematical procedures. The application of the method and the measurement system is illustrated in Section 3, where the wireless sensors and the laboratory test set-up are described in detail. Results coming from the experiments are presented and discussed in Section 4. In the end, some conclusions are reported to highlight the simplicity and efficiency of the proposed method.
# Susceptibility Modeling of a Rubber Track for Lightweight Mobile Robots
## 3 Track Belt Model  
The elastomer track belt simulation model was made in the ADAMS MBD simulation program [51]. The track belt was considered a flat system composed of link-type elements. Furthermore, links were joined by general force vector constraints, enabling rotation in the transverse axis Z (Figure 1). The forces in the connection were characterized by the longitudinal, transverse, and angular stiffness and damping values (Figure 1). Forces \(F_{x}\), \(F_{y}\) and torque \(M_{z}\) occurring in the constraints were determined according to the equations:  
\[M_{z}(\zeta_{Z1})=k_{z}\cdot\zeta_{Z1}-c_{z}\cdot\dot{\zeta_{Z1}} \tag{1}\]  
\[F_{x}(x)=k_{x}\cdot(x_{2}-x_{1})-c_{x}\cdot(x_{2}-x_{1}) \tag{2}\]  
\[F_{y}(y)=k_{y}\cdot(y_{2}-y_{1})-c_{y}\cdot(y_{2}-y_{1}) \tag{3}\]  
where \(\zeta_{Z1}\) is the rotation angle of the link axes, \(x,~{}y\) are displacements of the connected elements, \(k_{x},~{}k_{y}\) are longitudinal and transverse stiffness, \(k_{z}\) is the angular stiffness, \(c_{x},c_{y}\) are the damping values on the \(X\) and \(Y\) directions, and \(c_{z}\) is rotational damping in the transverse \(Z\) axis.  
The values of the stiffness and damping coefficients depend on the length and number of links representing the track belt; therefore, three track model variants were developed:  
* Variant where links and grouser quantities are equal (38-element variant--E38);
* Variant with 1.5 times increased link quantity (56-element variant--E56);
* Variant with twice the link quantity (76 element variant--E76).  
Based on the rubber tracks modeling publication analysis, the coefficients of longitudinal and transverse stiffness and damping were determined as \(k_{x},~{}k_{y}\) = 100,000 N/mm and \(c_{x},~{}c_{y}\) = 1000 N/mm/s. High values of these coefficients significantly limited the possibility of longitudinal displacements of the track links. Therefore, the coefficients of angular stiffness and damping mainly affected the track's susceptibility and its deformation ability. In order to determine the exact values of these parameters, experimental tests were carried out.
## 3 Research Method
### Data Collection
#### 3.1.3 Sentinel-1 Satellite Data  
The Sentinel-1 satellite data used in this study comes from the European Space Agency, is used to monitor elevation changes in SDAs, and serves as the basis for monitoring dynamic changes in earth and rock. The Sentinel-1 satellite features a C-band radar imaging system with capabilities spanning interferometric wide swath, strip map, extra-wide swath, and wave modes. Its advanced attributes, including dual-polarization, rapid revisit cycles, and quick product generation, significantly bolster its earth observation capabilities. This research utilizes quarterly-collected Sentinel-1 C-band synthetic aperture  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sensor** & **Band** & **Wavelength Range** & **Resolution (m)** \\ \hline \multirow{6}{*}{Panchromatic multispectral camera} & 1 & 0.45–0.90 & 2 \\ \cline{2-4}  & 2 & 0.45–0.52 & \\ \cline{2-4}  & 3 & 0.52–0.59 & \\ \cline{2-4}  & 4 & 0.63–0.69 & \\ \cline{2-4}  & 5 & 0.77–0.89 & \\ \hline \multirow{6}{*}{Multispectral camera} & 6 & 0.45–0.52 & \\ \cline{2-4}  & 7 & 0.52–0.59 & \\ \cline{1-1} \cline{2-4}  & 8 & 0.63–0.69 & \\ \cline{1-1} \cline{2-4}  & 9 & 0.77–0.89 & \\ \hline \end{tabular}
\end{table}
Table 1: GF-1 satellite payload specifications.  
radar (SAR) imagery to monitor vertical changes in SDAs to accurately analyze and monitor terrain alterations within SDAs.
## 2 Materials and Methods  
### Chemicals and Reagents  
Metformin, collagen (type I), luciferin-luciferase, AA, 9,11-dideoxy-11\(\alpha\),9\(\alpha\)-epoxymethanoprostaglandin (U46619), heparin, prostaglandin E\({}_{1}\) (PGE\({}_{1}\)), nitroglycerin (NTG), phenylmethylsulfonyl fluoride, sodium orthovanadate, sodium pyrophosphate, aprotinin, leupeptin, sodium fluoride, phorbol 12,13-dibutyrate (PDBu), bovine serum albumin (BSA), and thrombin were purchased from Sigma Aldrich (St. Louis, MO, USA), and anti-phospho-p38 MAPK (Thr\({}^{180}\)/Tyr\({}^{182}\)) polyclonal antibody (pAb) was purchased from Affinity (Cincinnati, OH, USA). Anti-phospho-Jun N-terminal kinase (JNK) (Thr\({}^{183}\)/Tyr\({}^{185}\)), anti-SAPK/JNK, anti-phospho-(Ser) protein kinase C (PKC) substrate, and anti-phospho-phosphoinositide 3-kinase (PI3K) p85 (Tyr\({}^{458}\))/p55 (Tyr\({}^{199}\)) pAbs and anti-Akt, anti-p38 mitogen-activated protein kinase (MAPK), anti-phospholipasey2 (PLCy2), and anti-PI3K p85 (19H8) monoclonal antibodies (mAbs) were purchased from Cell Signaling (Beverly, MA, USA). Anti-phospho PLCy2 mAb was obtained from Abcam (Cambridge, UK). Anti phospho glycogen synthase kinase-3\(\alpha\)/ (GSK3\(\alpha\)/\(\beta\)) and anti-GSK3\(\alpha\)/\(\beta\) mAbs were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Anti-phospho-Akt (Ser473) pAb was purchased from BioVision (Mountain View, CA, USA). Anti-vasodilator-stimulated phosphoprotein (VASP) (phospho Ser473), anti-VASP (phospho Ser473), anti-VASP, anti-pleckstrin, and extracellular signal-regulated kinase (ERK) 1 (phosphate Thr473) + ERK2 (phosphate Thr473) + PAbs were purchased from GeneTex (Irvine, CA, USA). CF47488A Dye and CF47405M Dye were obtained from Biotium (Hayward, CA, USA). Bio-Rad Laboratories (Hercules, CA, USA) and Molecular Probes (Eugene, OR, USA) supplied protein assay dye reagent concentrate and Fura 2-AM, respectively. The FITC-anti-human CD42P (P-selectin) mAb was obtained from BioLegend (San Diego, CA, USA). H89 and KT5823 were purchased from Cayman Chem (Ann Arbor, MI, USA). The stock solution of metformin (100 mM) was dissolved in phosphate-buffered saline (PBS) and stored at 4 \({}^{\circ}\)C until use.
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
## 2 System Model and Problem Formulation  
The aim of this section is first to introduce the existing AmBC system that utilizes OFDM carriers, then to present those factors that affect the detector performance. In order to understand how to make use of both energy harvesting and backscattering, we investigate the AmBC system model where an energy harvesting tag uses the existing radio signals from ambient sources to operate itself (e.g., RF source). It produces a modulated reflection of those signals to a nearby receiver (e.g., reader). Legacy receivers employ OFDM structure, where data is transmitted in parallel on a certain number of sub-carriers of different frequencies. Thus, it leads to low power consumption on data transmissions which is very suitable for low-powered hardware or batteryless IoT devices. Moreover, in terms of efficient spectrum sensing, it has been shown that energy detection approach has low computational complexity and ability to identify the spectrum holes without a _priori_ knowledge of primary characteristic [8; 14; 15]. Once we develop an appropriate test statistic for energy detector, it can guarantee a desired detection performance.
## 3 Results and Discussion
### FTIR Analysis  
The sample at pH 10 was chosen for FTIR analysis, and the results are shown in Figure 7. The FTIR data confirmed the presence of capping agents on the surface of the AuNPs. Since the FTIR spectra of the AuNPs were recorded in aqueous dispersion, the water spectrum was subtracted as a background, but there were still some water peaks present in the spectra. In particular, the peaks at 3400 cm\({}^{-1}\), 2123 cm\({}^{-1}\), and 1630 cm\({}^{-1}\) correspond to water. The peak 2123 cm\({}^{-1}\) corresponded to the combined band of O-H scissors-bending and the broad liberation band shown previously [36]. The broad band between 3800 and3000 cm\({}^{-1}\) was caused by O-H stretching: the shoulder at approximately 3300-3400 cm\({}^{-1}\) was from the solvent water, while the narrower peak at 3100 cm\({}^{-1}\) corresponded to the O-H stretching of the carboxylic acids. Three narrow peaks at 3000 cm\({}^{-1}\) represent the C-H (CH\({}_{3}\)) stretching vibrations of the capping agents present. In the region of the amine bands, there was an indication of the presence of C=O vibrations (1740 cm\({}^{-1}\)) as well as an N-H bending peak (1550 cm\({}^{-1}\)), which leads us to believe that the capping agent may be proteins in origin.
# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 3 Methods
### Heatmap  
To ensure the robustness of face keypoint detection in extreme cases, we propose to add a heatmap head network in addition to the backbone. The heatmap's prediction output has the same dimensions as the backbone's output, eliminating the need for global pooling. Since \(\mathbf{M}_{L}\in\mathbb{R}^{B\times C_{L}\times H_{L}\times W_{L}}\) lacks detailed image features necessary for fine keypoint detection, we perform an upsampling operation on \(\mathbf{M}_{L}\) to recover and enrich these details. The new output \(\mathbf{M}_{s}\in\mathbb{R}^{B\times C_{L}\times H_{L}\times W_{t}}\) is obtained using bilinear interpolation.  
To align the channel \(C_{L}\) of the upsampled output \(\mathbf{M}_{s}\) with the number of keypoints \(N\) in the label set \(\mathbf{Y}\), we apply a nonlinear multi-layer convolutional network composed of Conv-BN-ReLU operations to \(\mathbf{M}_{s}\), resulting in \(\mathbf{M}_{s}^{\prime}\in\mathbb{R}^{B\times D\times H_{s}\times W_{s}}\), where \(D=N\). To ensure that the output values approximate the real keypoint positions as probabilities, we apply a sigmoid operation to \(\mathbf{M}_{s}^{\prime}\), limiting \(\mathbf{M}_{\text{mod}}\in\mathbb{R}^{B\times D\times H_{s}\times W_{s}}\) to the range \([0,1]\).  
A Gaussian distribution is adopted with the standard deviation of \(\sigma\) to transform the label set \(\mathbf{E}\) into the heatmap \(\mathbf{K}\). For each pixel value \(M(x,y)\) in the image, \(x\) and \(y\) are the pixel coordinates, \((x_{i},y_{i})\) are the coordinates of the points in the set, and \(\sigma\) controls the range of influence. The output is a matrix \(\mathbf{K}\in\mathbb{R}^{B\times D\times H_{s}\times W_{s}}\), i.e., \(B\times D\), representing \(B\times D\) heatmap images. Each pixel value is calculated as the sum of the Gaussian effects from all points on that location.  
\[M(x,y)=\sum_{i=1}^{L}\exp\biggl{(}-\frac{(x-x_{i})^{2}+(y-y_{i})^{2}}{2\sigma^ {2}}\biggr{)} \tag{2}\]  
Finally, we compute the mean squared error (MSE) between the heatmap label \(\mathbf{K}\) and the output \(\mathbf{M}_{\text{mod}}\) to obtain the scalar loss\({}_{2}\). This MSE measures the variance between the corresponding elements of the two tensors at each feature map location. A smaller loss\({}_{2}\) value indicates that the predicted keypoints are closer to their true positions.  
\[\textit{loss}_{2}=\frac{1}{B\times D\times H^{\prime}\times W^{\prime}}\sum_{b =1}^{B}\sum_{d=1}^{D}\sum_{h=1}^{H}\sum_{w=1}^{W}(F_{\text{mod}}-K)^{2} \tag{3}\]  
Our proposed face keypoint detection method combines regression and heatmap approaches by integrating loss\({}_{1}\) (for the regression) and loss\({}_{2}\) (for the heatmap) into a unified loss function, guiding model learning with \(\text{LOSS}=\text{loss}_{1}+\text{loss}_{2}\).
## 1 Introduction
### Deployment of Extended Reality in Construction Safety  
Various authors have deployed extended reality for construction safety management. Developments have focused on training workers through virtual reality for identifying risks, working at heights and in hazardous environments and identifying collective and individual protection elements in the workplace [16; 17; 18]. In addition, various research projects have focused on training and using and handling construction machinery and equipment [19; 20; 21]. Workers' behaviour in these virtual environments has also been studied, in training and facing workplace disturbances and environmental variables like noise, among others [22]. Research has also been carried out on design reviews and construction planning. In addition, augmented reality has been used to supervise safety environments in the workplace, to verify construction processes and the provision of protective elements [23; 24]. Mixed reality has also been used to train workers to use complex tools and perform tasks [25; 26; 27].  
Extended reality constructs immersive virtual scenarios, in which safety managers can efficiently recognise and study hazards [28]. Moreover, safety training that has been traditionally oriented towards passive teaching can now migrate towards active worker participation in XR environments [29; 30]. Extended reality offers the possibility to recreate real scenarios in safe and monitored virtual environments. These scenarios increase safety awareness, motivate interest in learning and energise the training activity. The correct installation of health and safety measures on site and monitoring and controlling them throughout the project are significant tasks. All the scenarios and tools offered by XR can be superimposed onto the real site (which changes over time, depending on the type of tasks and projects), allowing for more effective supervision and control [31].  
While XR experiences validate these varied uses, most of these experiences have been isolated or were only part of research efforts by specific working groups or companies developing 'XR pills' for specific training. At present, extended reality cannot be considered a mainstream tool in the industry [32]. Integrating a holistic view of XR into workflows, deploying the technology effectively, developing its functionalities and multivariate uses, testing it to scale and using it in real scenarios are necessary to spread its use and implementation [12].
## 2 Numerical Method
### Numerical Solution  
This study uses the commercial CFD software ANSYS Fluent 2022R1 for numerical simulations of the model. The numerical model employs the explicit volume of fluid (VOF) method coupled with a level set function (CLSVOF) to describe the two-phase interface between liquid and air [47]. Considering the low-speed characteristics and incompressibility of the droplets, a pressure-based solver is used to simulate the transient process of droplet impact on the surface. Pressure-velocity coupling is achieved using the PISO scheme, and pressure discretization employs the PRESTO! method. The volume fraction function is discretized using the Geo-Reconstruct scheme, while the momentum equation, energy conservation equation, and level set function are discretized using a second-order upwind scheme. To better approximate real conditions, the simulation includes gravity in the negative y-direction, with a magnitude of \(g=9.81\) m s\({}^{-2}\).
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 4 Results
### Experimental Results
#### 4.2.3 WHU Building Dataset  
As shown in Figure 10, with a large amount of sample data, various models achieve commendable segmentation results in groups 4, 5, and 8. However, MFFNet has a distinct advantage in terms of capturing the contours of smaller structures. In the annotations of group 7, none of the five networks accurately segmented the entire tiny house. However, MFFNet delineated a portion of the house's area, while the other models categorized it as background. Simultaneously, UNet++, UNet, PSPNet, and DeepLabV3+ mistakenly segmented the complex background below as a building. In groups 2, 3, and 6, UNet++, UNet, PSPNet, and DeepLabV3+ all exhibited varying degrees of misclassification. In group 1, at the corner of the building, the manual annotation was at a right angle. However, intuitively, it can be determined that the original image had a certain degree of curvature. The segmentation results of all five networks seem more in line with reality than the manual annotation.  
Table 3 presents the evaluation metrics for each model on the WHU dataset. The MIoU of MFFNet stands at 91.82%, with an accuracy of 98.73%, a precision of 95.69%, a recall rate of 95.50%, an F1-score of 96.22%, and a kappa value of 94.22%. Compared to other models, MFFNet's MIoU surpasses UNet++, UNet, PSPNet, and DeepLabV3+ by 0.94%, 1.18%, 2.15%, and 0.70%, respectively. Furthermore, MFFNet's accuracy exceeds that of UNet++, UNet, PSPNet, and DeepLabV3+ by margins of 0.19%, 0.23%, 0.51%, and 0.21%, respectively. It is evident that, while MFFNet's MIoU and accuracy are marginally superior to those of other models, the distinctions are not substantial for other evaluation metrics. When applied to the WHU building dataset, it is apparent that MFFNet's segmentation capability is superior to that of traditional remote sensing segmentation models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MIoU** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline UNet++ & 80.64\% & 93.60\% & 87.94\% & 89.76\% & 88.79\% & 86.54\% \\ UNet & 80.36\% & 93.48\% & 87.50\% & 89.94\% & 88.58\% & 86.32\% \\ PSPNet & 76.19\% & 92.05\% & 84.70\% & 87.18\% & 85.48\% & 82.74\% \\ DeepLabV3+ & 78.08\% & 92.74\% & 85.49\% & 88.92\% & 86.99\% & 84.47\% \\ MFFNet & 81.76\% & 94.00\% & 89.02\% & 90.11\% & 89.50\% & 87.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segmentation Results on the Massachusetts Dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MIoU** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline UNet++ & 90.88\% & 98.54\% & 95.40\% & 94.63\% & 95.82\% & 93.53\% \\ UNet & 90.64\% & 98.50\% & 95.23\% & 94.64\% & 95.61\% & 93.38\% \\ PSPNet & 89.67\% & 98.22\% & 93.66\% & 95.18\% & 95.05\% & 92.74\% \\ DeepLabV
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.4 Support Vector Machine (SVM)  
The Support Vector Machine (SVM) represents a supervised machine learning paradigm that endeavors to identify a hyperplane effectively demarcating two classes [33]. Given that there exists an infinite number of hyperplanes capable of precisely segregating the two classes, SVM aims to select the optimal hyperplane by identifying the one with the maximum margin. The maximum margin signifies the greatest separation between hyperplanes, thereby maximizing the distance between the two classes.
## 3 Experimental Setup
### HMM-DNN Hybrid Baseline  
We build a basic HMM-DNN hybrid system using the Kaldi toolkit [28] to compare with the E2E framework. It follows the training method and configuration in the Kaldi toolkit. After the training of mono phone and triphone, the Nnet3 script is used to train a time-delay neural network (TDNN), consisting of five layers, each with 650 nodes. The time delay is configured as \((-2,-1,0,1,2)\)\((-1,0,1)\)\((-1,0,1)\)\((-3,0,3)\)\((-6,-3,0)\). The decoding process uses the default beam search method with the beam size set to 10.
# A Survey of Low-Rank Updates of Preconditioners for Sequences of Symmetric Linear Systems
## 8 Conclusions  
We have presented a general framework of updating a given preconditioner \(P_{0}\) with a low-rank matrix with the aim of further clustering eigenvalues of the preconditioned matrix \(P_{0}A\). We have shown that this approach is particularly efficient when a sequence of linear systems has to be solved. In this case the cost of computation of the leftmost eigenvector of the preconditioned matrices is payed for by the number of system solutions. Alternatively, the vector forming the low-rank updates can be chosen as the previous solutions in the sequence. In summary we have reviewed three updating processes:  
* Deflation, aimed at shifting to zero a number of approximate eigenvalues of \(P_{0}A\).
* Tuning, aimed at shifting to one a number of approximate eigenvalues of \(P_{0}A\).
* Spectral preconditioners, aimed at adding one to a number of approximate eigenvalues of \(P_{0}A\).  
The most popular choices of the vectors (such as leftmost eigenvectors of either \(A\) or \(P_{0}A\)) have been considered together with some techniques to cheaply assess them.  
Finally we have considered a number of applications, such as discretization of evolutionary PDEs and solution of large eigenvalue problems. In all these applications the low-rank correction is much beneficial to reduce the number of iterations of the iterative solver of choice.  
As a general recommendation we can say that accelerating a given preconditioner by a low-rank matrix can be useful when both the following situations occur:  
1. A sequence of linear systems with constant or slightly varying matrices has to be solved.
2. Either the smallest or the largest eigenvalues do not form a cluster.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author is indebted to the reviewers whose suggestions helped improve the quality of the paper. This work was partially supported by the INdAM Research group GNCS (Year 2020 project: _Optimization and Advanced Linear Algebra for Problems Governed by PDEs)_ and by the project funded by CARIPARO: _Matrix-Free Preconditioners for Large-Scale Convex Constrained Optimization Problems (PRECOOP)_.  
**Conflicts of Interest:** The author declares no conflict of interest.