esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Quality Assessment to Emulate Experts' Perception in Lumbar MRI Using Machine Learning
## 2 Methods and Materials
### Machine Learning
In order to reduce the dimensionality of the input data, the Principal Component Analysis (PCA) technique is applied, which decomposes the data set into a series of orthogonal components that explain a desired amount of variance. In the case of the proposed data set, the amount of features obtained in objective evaluation was reduced applying PCA, the variance was solved by 99%, and 12 principal components were reported. For LDA it is used as a singular value decomposition (SVD) solver which is not based on the calculation of the covariance matrix; instead, it performs a polar decomposition from a square matrix \(m*n\) to any other matrix. SVM was implemented using a radio basal kernel (RBF) which is described by \(\phi_{y}(x,l)=exp(-y||x-l||^{2})\); this causes many dimensions to be created in the dataset and makes it linearly separable. The hyperparameters were selected using a Grid Search Cross Validation. To avoid overfitting and validate the model, we carried out the simulation study with a 10-fold cross-validation scheme. The averages of the accuracy, precision, recall, F1-score and area under curve (AUC) for testing were estimated for each of the machine learning models. Moreover, the Kappa Index was estimated as the Agreement coefficient.
## 2 Materials and Methods
### Instruments and Chemical Features of NPs  
A UV-visible absorption spectrophotometer (Spectrophotometer UV2, Uni cam UV-VIS, Ventura, CA, USA, in the wavelength range of 190-1100 nm via a quartz cuvette of 10 mm path length) was used to investigate the optical properties of the prepared metallic/bimetallic nanoparticles. The FT-IR spectrum analysis was run at Thermo-Fisher Nicolet IS10, Waltham, MA, USA. A spectrophotometer at a resolution of 4 cm\({}^{-1}\) in 500-4000 cm\({}^{-1}\) was used in a transmission approach to define the contribution of the functional groups in the biosynthesis and stabilization of nanoparticles. Zeta potential analyses were run on HORIBA SZ-100 (Singapore), with a holder (24.9 \({}^{\circ}\)C), dispersion medium viscosity (0.897 mPa\(\cdot\)s), conductivity (0.264 mS/cm), and electrode voltage (3.3 V). The TEM analyses were run on a Thermoscientific Talos F200i (Waltham, MA, USA) using a carbon-coated grid (Type G 200, 3.05 \(\upmu\) diameter, TAAP, Seattle, DC, USA) and were implemented for the characterization of size, morphology, aggregation, and crystallization of metallic/bimetallic nanoparticles. For the antioxidant activity and phytochemical analyses, a spectrophotometric apparatus (Spekol 11 spectrophotometer, analytic Jena AG, Jena, Germany) and UV lamp (Vilber Lourmat-6.LC, VILBER Smart Imaging, Marne-la-Vallee, France) were used.
## 2 Related Work
### Localization Schemes
#### 2.2.2 Accuracy Priority Trilateration (APT)  
The trilateration method is a geometric technique that uses distances between three ANs and one node of the unknown location to determine a UNN's location. A UNN can be localized when at least three reference points associated with it in two-dimensional space are known. The location of the UNN can be estimated by calculating the intersections of three circles. This situation is shown in Figure 6. In this way, three dark-colored nodes represent the ANs whose locations are known, and the light-colored node represents the target node whose location information is trying to be obtained by using the positions of the ANs [33].  
Figure 6: Trilateration technique.  
With the APT technique, the UNN location is obtained by using the three closest location message information from the MA to the UNN. In this technique, it is possible to estimate the UNN with high accuracy since three AN values closest to the UNN are considered [9].
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution
### Strain Energy Components
#### 3.1.3 Sensitivity Analysis of the Local Solutions  
The main purpose of the sensitivity analysis performed in this section is detection of the situation that the assessed global problem solution differs to the solution of the problem potentially free of locking to such an extent that the difference may suggest the presence of the locking phenomena. Note that the theoretical ratio of the true bending-dominated solution and locked membrane-dominated (and/or shear-dominated) solution is \(C\cdot t^{2}\), with \(C\) being a constant. This ratio results from the proportionality of the membrane (and/or shear) part of the strain energy and the bending part of this energy to \(t\) and \(t^{3}\), respectively, as shown in (8).  
To compare the above two solutions, the following two local problems for each element of the plate or shell structure, or such parts of complex structures, has to be solved:  
\[\overset{e}{B}(\overset{e}{u}^{HP_{1}Q},\overset{e}{v}^{HP_{1}Q})-\overset{e }{L}(\overset{e}{v}^{HP_{1}Q})-\int_{S_{e}\setminus(P\cup W)}\overset{e}{( \overset{e}{v}^{HP_{1}Q})^{T}}\langle\overset{e}{r}(\overset{h}{u}^{hpq}) \rangle\ dS_{e}\ \ =\ \ 0 \tag{30}\]  
where \(P_{1}=p\), \(H=h\) and \(Q=q\) correspond to the global problem under consideration, and:  
\[\overset{e}{B}(\overset{e}{u}^{HP_{2}Q},\overset{e}{v}^{HP_{2}Q})-\overset{e }{L}(\overset{e}{v}^{HP_{2}Q})-\int_{S_{e}\setminus(P\cup W)}\overset{e}{( \overset{e}{v}^{HP_{2}Q})^{T}}\langle\overset{e}{r}(\overset{h}{u}^{hpq}) \rangle\ dS_{e}\ \ =\ \ 0 \tag{31}\]  
with \(P_{2}=p_{max}=8\), \(H=h\), and \(Q=q\) corresponding to the problem potentially free of locking.  
The following criterion is set to assess sensitivity of the solution to the change of the longitudinal approximation order from \(P_{1}\) to \(P_{2}\):  
\[\sum_{e=1}^{E}\frac{1}{2}\frac{\varepsilon}{B}(\overset{e}{u}^{HR_{1}Q}, \overset{e}{u}^{HR_{1}Q})<a\sum_{e=1}^{E}\frac{1}{2}\overset{e}{B}(\overset{e}{u} ^{HR_{2}Q},\overset{e}{u}^{HP_{2}Q}) \tag{32}\]  
where \(a\) is the coefficient which determines the ability of the adaptive method to overcome the locking phenomena with the standard \(hp\)-adaptive procedure. In the case of the applied procedure based on Texas three-step strategy [40] and error estimation based on the equilibrated residual method [43; 44], the reasonable (verified numerically) value is suggested to be equal to \(a=0.1\). Fulfillment of the above criterion suggests the possibility of locking appearance. This possibility has to be confirmed by the criterion (29) of bending-dominance.
# Impact of Lowering Speed Limit on Urban Transportation Network
## 2 Literature Review
### Studies on the Relationship between Vehicle Speed and Emission  
Previous studies confirmed that the relationship between vehicle speed and pollutants generated on the road, such as vehicle exhaust gas, showed a U-shaped curve. Pollutants initially decrease as the vehicle speed increases from zero and then increase again when the vehicle speed surpasses a certain level [23; 24].  
[PERSON] investigated emission and traffic conditions using the United States emission table. Different level of traffic volumes and intersection signal plans were considered in the analysis. The study suggested that a different strategy is necessary for different traffic conditions [25].  
[PERSON] reviewed the method of calculating GHG emissions generated by vehicles on highways. GHG emission characteristics were investigated with different traffic environments. It was confirmed that most vehicles except for large trucks emit the lowest GHG emissions at 65-75 km/h [26].  
[PERSON] found that acceleration and deceleration have a strong relationship with vehicle emissions. It was demonstrated that the emission was estimated significantly different with different acceleration and deceleration data. It was concluded that the emissions significantly increase as the acceleration increases [27; 28; 29].  
In summary, previous studies investigated the effect of lowering the speed limit in the urban traffic environments, and it was reported that the effect of lowering speed limit varied depending on the traffic conditions. However, there are few studies that investigated the effects of lowering speed limit with respect to various traffic conditions and traffic environments from a perspective of vehicle emission. Therefore, in this study, various traffic conditions including traffic volume and signal offset were investigated using microscopic traffic simulation, and the effect on vehicle emissions from lowering speed limit was investigated.
# Atlas of Defects within a Global Building Inspection System
## 1 Introduction
### Expeditious Tools to Assess Building Pathology
Additionally, [PERSON] et al. [9] conclude that a catalogue of images of building elements with reference values of severity would be a valuable contribution to classify building pathology, increasing the accuracy of assigning levels of severity of degradation. The proposed atlas of defects is the realisation of such a catalogue. Table 2 summarises the mentioned scales used to measure the degradation of building elements. The scales' measure, levels and type of use are highlighted. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Measure** & **Levels** & **Use** \\ \hline
[49] & state of a building component & 1–6 (excellent to very bad) & direct assignment \\
[50] & deterioration state of elements & a, b, c and d & direct assignment \\  & obsolescence criteria ratings for & A, B and C & \\  & elements of office buildings & (from good to poor or not sufficient) & algorithm \\
[54] & condition assessment ratings of & 5, 4, 3, 2 and 1 & \\  & building systems & (from excellent to bad) & direct assignment \\  & level of severity of degradation & 3–10 & \\  & of building elements & (8 levels, from the most to the least severe) & algorithm \\
[56] & inspected severity of deficiencies & 0–100 & \\  & & (from the least to the most severe) & direct assignment \\  & degradation condition of & 0, 1, 2 and 3 & \\  & building elements & (from not visible to & direct assignment \\  & & generalised degradation) & \\  & severity of defects & A, B, C, D and E & \\  & & (from the least to the most severe) & direct assignment \\  & distress severity (detailed & \\  & distress survey) & low, medium or high & algorithm \\
[59] & direct rating of serviceability & from Green (+) to Red (-) & \\  & problems due to degradation & (9 levels, from the least to the most severe) & direct assignment \\  & severity rating (seriousness) & 1, 2, 3, 4 and 5 & \\  & of defects & (from extremely minor to \\  & severity rating of defects (effects & 1, 2 and 3 \\  & and urgency of repair) & (from low to severe impact) & direct assignment \\
[8,11,14,16,18, 19,21,25,27,29-31] & severity/repair urgency level & 0, 1 and 2 & \\  & of defects & (from the most to the least severe) & algorithm \\
[62] & defects’ condition & 0, 1, 2, 3 and 4 & \\  & severity of degradation of & A, B, C, D and E & \\  & building elements & (from the least to the most severe) & algorithm \\
[9] & severity of degradation of & 0, 1, 2, 3 and 4 & \\  & building elements & (from the least to the most severe) & direct assignment \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of scales used to measure the degradation of building elements.
## 1 Introduction  
Time of flight (TOF) positron emission tomography (PET) provides additional information in the acquired data respect to conventional non-TOF PET. On top of the non-TOF information, which is sufficient for image reconstruction, TOF provides a range for the probable position of the annihilation event. This makes the tomographic problem better conditioned resulting in reconstructed images with a higher signal-to-noise ratio (SNR).  
This makes the tomographic problem better conditioned and the signal-to-noise ratio (SNR) higher on reconstructed images. In a TOF system characterized by a coincidence timing resolution (CTR) \(\Delta t\), expressed in full width half maximum (FWHM), the TOF \"effective diameter\" is \(D_{eff}\equiv\sqrt{2\pi}\frac{\Delta t}{\sqrt{8\ln 2}}c/2\). In the central pixel of a uniform circular object of diameter \(D\), at the maximum likelihood solution, TOF is expected to provide a noise reduction of a factor \(\sqrt{D/D_{eff}}\) with respect to non-TOF [1].  
In clinical setups, PET images are conventionally reconstructed with Maximum Likelihood Expectation Maximization (MLEM) [2], often used in combination with the ordered subset acceleration, in the so-called OSEM algorithm [3]. In MLEM and OSEM, the signal converges with iterations, which are, however, also characterized by a noise augment. Good SNR images are, therefore, obtained by stopping MLEM and OSEM at early iterations (early stopping rules).  
It has already been observed that TOF increases the MLEM convergence speed; therefore, at matched numbers of iterations, both the signal and noise are higher with TOF than without [4; 5]. To improve the SNR respect to non-TOF, a lower number of iterations is, therefore, generally recommended with TOF [6]. Empirical stopping rules were derived on patient and phantom data by matching non-TOF noise, thus, obtaining a better contrast or by matching the non-TOF contrast and, thus, maintaining a lower noise.  
In [7], as an example, the standard deviation between neighbouring voxels in a liver region of interest (ROI) was considered as a noise measure for noise matching. In other works, the contrast recovery on phantom data was used for contrast matching. These empirical rules depend on the patient/phantom characteristics and on the ROI positioning. Contrast recovery is known to depend on many factors, including the background composition.  
The aim of this study is to theoretically derive and experimentally validate a contrast matching stopping rule for TOF data with different CTRs that is able to guarantee contrast matching for the most critical signals and noise reduction. A theoretical rule of this kind is especially important in the context of the release of new TOF-PET systems, with a CTR as low as 210 ps [8].  
This paper is structured as follows. After notations and conventions, in Section 2 we analyze the convergence speed of the signal and noise in TOF and non-TOF MLEM and propose an optimal contrast matching stopping rule for scanners with different CTRs. In Sections 3 and 4 computer simulations and phantom measurements for theory validation are presented.
## 5 Results and Discussion
### Hyperparameter Experiments
#### 5.4.1 Effect of Number of Subsequences  
The number of subsequences determines the extracted time-domain features, which are the input features of the model and affect its performance. Figure 12 shows the effect of different numbers of subsequences, and it can be observed that both too-small and too-large numbers of subsequences lead to poor performance. On the one hand, a small number of subsequences means the data contain less wear degradation information. On the other hand, a too-large number of subsequences makes the model difficult to optimize and leads to overfitting. Therefore, data processing with 60 subsequences is executed in this paper.
## 3 Probability of Chip Error in Coherent O-QPSK
### Without Pulse Shaping
#### 3.1.3 Case (c): \(\sqrt{C}\leq\sqrt{I}\)  
For increasing values of \(\sqrt{I}\), the circle will move to the other side of the origin (see Figure 2c). \(\alpha\) can still be written as in Equation (11), but now:  
\[S_{0} =\alpha\] \[S_{1} =\pi/2-\alpha\] \[S_{2} =2\alpha-\pi/2\]  
When the received signal is in \(S_{2}\), the receiver makes two chip errors per each transmitted symbol. On the other hand, when it is in \(S_{0}\) or in \(S_{1}\), the receiver makes one chip error per each transmitted symbol. Regarding the number of chip errors in the different quadrants, \(P_{s}\) and \(P_{c}\) can be written as:  
\[P_{s} =\frac{2(S_{0}+S_{1})+S_{2}}{2\pi}=\frac{\alpha}{\pi}+\frac{1}{4}\] \[P_{c} =\frac{2(S_{0}+S_{1})}{2\pi}\frac{1}{2}+\frac{S_{2}}{2\pi}=\frac {\alpha}{\pi}\]  
The computational complexity of \(P_{s}\) and \(P_{c}\) is \(O(1)\) in big O notation, since \(C\) and \(I\) are constant values.
## 4 Discussion
### Accuracy and Repeatability  
The measurements and calculations of the calibration lenses indicate that (a) the NIMO shows very good accuracy and repeatability, lower at the lens center, but with random errors under 0.02 D (or 0.13% when expressed as a percentage), (b) the systematic errors are also low and, with the exception of the +33.33 D calibration lens, are always lower than the manufacturing error of 1%, and, (c) the shape of the power profiles measured with the NIMO TR1504 is very close to the theoretical shape computed by a ray tracing software, as shown by the values of the systematic errors in Table 1. These errors computed using Equation (2) are lower than the ISO tolerances for both the 10 and 20 D lenses. Although for the 33 D lens the errors are higher, the shapes of the theoretical and measured power profiles shown in Figure 1C are quite similar. Indeed, the systematic error represents, in this case, a piston between the theoretical and the measured power profiles. Moreover, even at the lens center, a zone of 0.25 mm from the center, and for high-power lenses, the device exhibits high repeatability (equal to or less than 0.13%) and good precision (less than 2% if we consider the +33.33 D lens).  
In the case of measurements obtained with both models of intraocular lenses, monoclonal and multifocal, the results also align in indicating that the lowest precision of the measurements is confined to a central zone with a radius of 0.25 mm.  
The results obtained with both calibration and intraocular lenses suggest that central measurements obtained through deflectometry are less repeatable than non-central measurements within a radius of 0.25 mm from the center.
## 2 Network Model  
This section describes the UWASN model for applying the API protocol. We consider a centralized topology that is efficient to gather data from multiple underwater nodes for observation-basedapplications. In addition, the centralized topology is capable of managing network resources easily and supporting reliable network operations compared to a decentralized topology (e.g., ad hoc).  
The UWASN can be either two-dimensional or three-dimensional. In the two-dimensional UWASN, all nodes are located at the seabed or at an equal depth of water. Thus, horizontal communication is dominant. An example of the two-dimensional UWASN is a cluster-based sensor network composed of multiple sensor nodes centering on a cluster head [19; 20]. The three-dimensional UWASN consisting of a buoy on the water surface and several underwater nodes located beneath the buoy is based on vertical communication as explained in [21].  
As shown in Figure 1, the UWASN consists of a master node and a number of slave nodes. The master node located at the center is responsible for controlling all the slave nodes, gathering data from them, and forwarding the data to a land base station. The master node also sends data from the land base station to the slave nodes. A slave node located within the master node's coverage enters a network and exchanges data with other nodes under the control of the master node. A master node is exemplified as a cluster head, a buoy, or an underwater base station. Slave nodes can be either mobile (e.g., AUVs or underwater drones) or fixed (e.g., fixed sensors).  
The UWASN is operated through two stages; one is the initialization stage, and the other is the operational stage. The API protocol is employed in the initialization stage. A master node manages the start and the end of each stage. When frequent network failures occur, and thus an ongoing application is no longer executable, the master node terminates the operational stage and returns to the initialization stage.  
All the nodes of the UWASN are equipped with a digital acoustic communication module that provides the following common characteristics:  
* The communication between a master node and slave nodes is based on a half-duplex scheme. That is, one acoustic channel in the frequency domain is used.
* A node can communicate with any nodes within its communication range by using an omni-directional antenna.
* Depending on the transmission power of acoustic signals, nodes can be connected with each other via either one-hop or multi-hop. In the initialization stage, it is assumed that any slave node can connect to a master node directly by sending signals with the maximum power.
* All nodes obtain sensing capability by having a sensor attached according to applications.  
Figure 1: The architecture of the UWASN to apply the API protocol.
## 3 Results and Discussions
### Effects of Changes in the Types and Amounts of Binder and Dispersants
However, when 1 wt.% of DARVAN C-N was added, the viscosity decreased to 52,740 m\(\cdot\)Pas at a shear rate of 0.003, while when 1 wt.% of Dolapix CE 64 was added, the viscosity decreased to 16,230 m\(\cdot\)Pas at a shear rate of 0.003. It can be concluded that this is relatively more effective for the zirconia slurry because the viscosity was lowered by more six times at the same shear rate when adding 1 wt.% of Dolapix CE 64. Nonetheless, when 1.5 wt.% of Dolapix CE 64 is added, the viscosity increased again to 24,340 m\(\cdot\)Pas at a shear rate of 0.003. According to these results, when a dispersant is added to zirconia slurry, the viscosity is lowered, but it increases at a certain point. Thus, we find that when Dolapix CE 64 was added as a dispersant at 1 wt.%, the best condition arises to create reticulated porous zirconia with excellent compressive strength. However, we cannot be sure that this can be applied to all types of ceramics. For now, we only can be certain that optimization of the dispersant is necessary.
## 3 Results
### DL Methods
The researchers used ROC-AUC as a metric to evaluate the model. The results demonstrated: 100% sensitivity [95% CI: 100%, 100%], 89% specificity [95% CI: 78%, 97%]. Four false positives and zero false negatives. A 0.96 ROC-AUC [95% CI: 0.89, 0.99]. Figure 3 demonstrates that deep learning approaches (particularly DCNNs) generally achieved higher accuracy (90-99%) compared to traditional ML methods (72-96%). However, traditional methods like Random Forest and SVM still demonstrated robust performance, especially when using optimally selected features such as frontal horn length and callosal angle. Hybrid approaches combining ML and DL techniques showed promising results, leveraging advantages from both methodologies.