esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Retrofit Design Estimations vs. Dynamic Energy Simulations
### Comparison and Assessment  
When considering the results of the dynamic energy simulations for the three retrofitted schools, an overall comparison is performed between design estimated, dynamic simulated. and actual reported heating and electricity energy savings for the three investigated schools, as shown in Figure 10. It could be noted that both static design estimations and dynamic energy simulations overestimate the electricity savings in the three public schools due to the retrofit projects. However, it is shown that the dynamic simulations more accurately predict the technical impact of the retrofit improvements in the three schools in terms of electricity consumption savings, where design estimations are largely off with a big margin. In terms of heating consumption, the dynamic energy simulations for the three schools underestimate the annual heating energy savings in the three schools with an acceptable margin of error. On the other hand, static design estimations are again overestimating the heating energy savings with a large deviation when compared to actual reported data in Schools A and C, where the estimations for School B are much more accurate with acceptable error margin.  
Figure 9: Sketchup 3D architectural model for four blocks in School B (**a**) block 1, (**b**) block 2, (**c**) block 3 and (**d**) block 4.  
Table 4 provides a more detailed assessment on the relative error in terms of annual heating and electricity savings, as estimated by static design numbers from one side and the dynamic energy simulations on the other side, when compared to the actual monitored numbers from the three schools. Based on the reported data, it is clear that the dynamic energy model simulations predict in a fairly higher accuracy, compared to the static estimations, the technical impact of the retrofit projects in the three schools. The respective performance gap between the actual real energy savings and simulated numbers in the three schools is in the range of 11-26% and 16-29% for heating and electricity consumption. On the other hand, the static design estimations are very far from being accurate in estimating the impact of the retrofit projects and the energy savings in the three schools, except for the heating energy savings in School B with a relative error of around 13%. Overall, it is found that dynamic energy simulations using a detailed and calibrated holistic energy model could provide a very good prediction on the impact of a retrofit project with an acceptable performance gap and much higher accuracy when compared to conventional approaches while using tabulated assumptions and static design estimations.
# Peptides That Block RAS-p21 Protein-Induced Cell Transformation
### Structure of RAS-p21
Additionally, the ring atoms of Pro would appear to be at least as bulky as the methyl side chain of Ala at this position, which is an oncogenic amino acid substitution. In addition, the substitution of Gly for Gln 61 is oncogenic but has high GTPase activity. Other mutated RAS-p21 proteins such as D38E RAS-p21 bind with high affinity to GAP and to GTP but are enzymatically inactive, resulting in a low GTP hydrolysis rate; however, they are non-transforming. The mutant A59T RAS-p21 protein has at least five times the GTPase activity of wild-type RAS-p21 but transforms NIH3T3 cells with as high a rate as GTPase-defective oncogenic RAS-p21 proteins such as G12R- and G12K RAS-p21. Even more significantly, two triply substituted RAS-p21 mutant proteins have been identified that do not bind to GDP or GTP at all: The first is [G10V,G12R,A59T]RAS-p21, which transforms cells with a high efficiency, and the other is [G12R,G15V,A59T]RAS-p21 which is non-transforming [14]. Since neither protein binds to a nucleotide, the difference in activity between these two proteins cannot be due to differences in GTPase activity, but must be due to structural differences. In line with this conclusion, conformational studies [15] have indicated that critical amino acid substitutions at positions 12 and 61 can cause significant local changes in domain conformation, such as in switch 1 and switch 2 segments, and have been used to design specific peptides from the RAS-p21 protein itself that inhibit the oncogenic form of RAS-p21 selectively, which therefore may be of use in the treatment of ras-induced tumors. Peptides and Small Molecules Are Promising Agents That Can Selectively Destroy Oncogenic RAS-p21-Induced Cancers  
In the next three sections, we present four ways in which specific peptides and small molecules block oncogenic RAS-p21, making them highly useful in treating RAS-p21-induced tumors. These include: 1. the identification of peptides corresponding to domains of RAS-p21 that selectively inhibit oncogenic RAS-p21 using molecular simulations and peptide library approaches in which overlapping peptide sequences from RAS-p21 are assayed for their abilities to block oncogenic RAS-p21 selectively; 2. a cyclic peptide library approach that generates peptides based on their abilities to bind to grooves in G12D-RAS-p21 that stabilize an inactive form of this protein bound to GTP, and a small molecule library screening approach for poly-aromatic compounds that can insert into a groove present specifically in the GDP-bound form of G12C-RAS-p21 that permanently locks this oncogenic form of RAS-p21 in its inactive GDP-bound state; 3. an informatic approach in which candidate peptides are computed for their abilities to dock to specific domains of wild-type and oncogenic forms of RAS-p21 and the concurrent use of a small molecule library to generate candidate analogues of GTP; and 4. a novel immunologic technique in which a peptide that targets cancer cell membranes is fused to the anti-RAS-p21 antibody Y13 259 that inactivates oncogenic RAS-p21.
## 3 Results
### Evaluation of Other Paramagnetic Beads and Binding Buffer  
As the bulk of our paramagnetic bead capture used the Mag-Bind RXN Pure Plus, which consists of paramagnetic beads supplied in a binding buffer, we repeated our NaOH extractions from bacterial culture samples using PureSil-Silica paramagnetic beads and a defined bead-binding buffer (Methods). The average Ct values for the PureSil-Silica were 25.1 \(\pm\) 0.3, while the Mag-Bind RXN Pure Plus Ct values were 30.1 \(\pm\) 0.3. Thus, either source can bind DNA, but there may be some differences in either capacity or efficiency of  
Figure 6: Microbiome composition by phylum (**A**) or genus (**B**) from six pig fecal samples extracted either with a kit or the NaOH method. Sample numbers (top of paired bars) are as in Table 4, and the extraction method is indicated at the bottom.  
capture under alkaline conditions, with the PureSil beads and defined bead-binding buffer apparently recovering more DNA from equivalent aliquots. Additionally, with the PureSil beads and a separate buffer, we could examine different ratios of binding buffer to NaOH. First, we determined that a 1:1 mixture of 100 mM NaOH and the defined bead-binding buffer is pH 12.9. Therefore, the DNA capture is clearly under high pH. We then examined DNA recovery using ratios of binding buffer to 100 mM NaOH of 0:1, 0.25:1, 0.5:1, 0.75:1, and 1:1, followed by ethanol rinses, elution in Te, and qPCR quantification. While some DNA was captured even at 0:1 (Ct = 33.5 \(\pm\) 0.1 with specific HRM), increasing binding buffer increased yield: 0.25:1 Ct = 23.5 \(\pm\) 0.5, 0.5:1 Ct = 21.2 \(\pm\) 0.2, 0.75:1 Ct = 20.9 \(\pm\) 2.0, and 1:1 Ct = 20.8 \(\pm\) 1.0. Size selection of double-stranded fragments was reported through the ratio of binding buffer under neutral pH [44,45]. Given that our quantification is based on qPCR for chromosomal markers, it appears that 0.5:1 binding buffer to 100 mM NaOH is sufficient to capture the larger DNAs in this extraction procedure. For smaller fragments, it may be better to use 1:1 or higher ratios.
## 4 Results and Discussion  
### Overpressure Distribution Characteristics  
#### 4.1.1 Pressure Characteristics  
Figure 2 illustrates the pressure characteristics of the Qiongzhusi Formation. The pressure distribution within the Qiongzhusi Formation ranges from 28.38 to 128.23 MPa, with an average of 84.99 MPa. The pressure coefficient varies between 1.01 and 2.2, with an average of 1.76. Furthermore, a strong positive correlation exists between the burial depth of the Qiongzhusi Formation and both the reservoir pressure and the pressure coefficient.
# Accurate Landmark Localization for Medical Images Using Perturbations
## 3 Materials and Methods
### Heatmap Regression  
Algorithm 1 illustrates that the input of the neural network is an image I, and the output is a label set L. The label set provides the information about the landmark positions denoted by L = ((x_1, y_1), (x_2, y_2),..., (x_k, y_k)), where k is the number of landmarks.  
Two approaches are available for representing L. The first approach uses the coordinate values x_i and y_i as they are. In this case, the network output layer has 2k output nodes. The second approach converts the coordinate values of l_i = (x_i, y_i) into a heatmap, where a Gaussian distribution is formed centered at l_i. The heatmap size is the same as the input image. The heatmap is normalized in the range 0-1. Since each of the landmarks has its own heatmap, k heatmaps are made. The network output layer should output m \(\times\) n \(\times\) k tensor, where m \(\times\) n is the size of the input image, and k is the number of landmarks. The second approach is termed heatmap regression; an approach that has been proven to be superior [10; 32; 33]. This paper adopts the heatmap regression method.  
The literature adopting the heatmap regression usually used the Gaussian distribution to mark a landmark in the heatmap. Figure 2(a) shows a hand X-ray image, and Figure 2(b) illustrates the heatmap using Gaussian distribution for the landmark at Figure 2(a). Another option is to use the Laplace distribution that is sharper at the center than the Gaussian. Since the goal of the neural network is to determine a landmark position accurately, it seems that the Laplace distribution is better. This paper adopts the Laplace distribution in forming heatmaps. Figure 2(c) shows a heatmap formed by our method.  
Figure 3: (**a**) is a visualization of landmarks of a resized sample image in the Digital Hand Atlas dataset. (**b–d**) are the heatmaps for a landmark of (**a**).  
For the test stage, we should devise a method to convert a predicted heatmap into coordinate values. Figure 3d shows the predicted heatmap for the landmark. To calculate landmark coordinates, we select pixels whose values are larger than 0.85 of the maximum heatmap pixel value. The averages of these pixel positions are regarded as landmark coordinates on the heatmap.
# Uncertainty of the 2D Resistivity Survey on the Subsurface Cavities
## 3 Results and Discussion  
### Array Detection Ability  
The data recovering ability of different arrays may vary when applied to a specific subsurface structure [47]. In this study, we used the statistical variance to examine the measuring ability of arrays with regard to cavity depth. As depicted in Figure 2, when modeling the target cavity T\({}_{1}\) (at 2.2 m depth), resistivity data exhibited the variances of 24,400, 9058, 6341, and 2401 \(\rm{\Omega\cdot m}\) for the DD, PD, WS, and PP arrays, respectively. In contrast, for the target cavity T\({}_{6}\) (at 12.2 m depth), resistivity data showed a statistical variance of 1100, 1044, 936, and 900 \(\rm{\Omega\cdot m}\) for the DD, PD, WS, and PP arrays, respectively. The DD array depicted the maximum variance in resistivity data, whereas the PD and WS arrays produced moderate variances. The resistivity data from the PP array indicated the least variance. The results also show a linear dependence between the variance of the resistivity data and the accuracy of the cavity model recovered. Figure 2 shows that the decrease in resistivity data variance with increasing cavity depth is likely due to decreasing measurement sensitivity with survey depth [22].  
Figure 2: The variance of the numerically measured resistivity data for air-filled cavity situated at different survey depths.  
To obtain reliable and high-resolution resistivity tomograms, the arrays used in the measurements should provide data with maximum anomaly information [47]. Thus, we calculated the anomaly effect of the numerically measured resistivity data, which shows the recovering ability of the arrays for different air-filled cavity depths. As shown in Figure 3, the highest anomaly effects of 1.46, 1.16, 0.74, and 0.6 for the corresponding arrays of DD, PD, WS, and PP were shown for the simulation of the shallowest cavity T\({}_{1}\). In contrast, the arrays DD, PD, WS, and PP exhibited the lowest anomaly effects of 0.245, 0.24, 0.19, and 0.18 respectively, for the deepest cavity T\({}_{6}\). The dielectric nature of the cavity causes the arrays to acquire resistivity data with higher anomaly effects, mainly for the shallow cavity depth [11]. The DD array provided maximum anomaly effect, while the PP array exhibited minimum anomaly effect, similar to other studies [55]. In comparison, the PD and WS arrays depicted moderate anomaly effects. The anomaly effect shows a linear relationship with the measurement sensitivity of the array [47]. This could be the reason behind the decline of anomaly effects with an increasing cavity depth (Figure 3). The higher sensitivity near the electrode location may enable the arrays to get a more pronounced anomaly effect. On the other hand, the low sensitive zone of the far electrode position might limit the arrays to recover the higher anomaly effect.
## 2 Materials and Methods  
### Database  
The database, comprising 787 datasets, was obtained through articles published on composites of UHMWPE reinforced with CF, graphene, MWCNT, and HAP [1; 14; 20; 31; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68]. Altogether, 18 variables were considered as inputs, which incorporate the weight proportion and molecule dimensions of the micro/nano particles, with five variables considered as outputs; i.e., three mechanical properties, namely, ultimate tensile strength (UTS), hardness (H), and Young's modulus (E), as well as two tribological properties, namely, the specific wear rate (SWR) and coefficient of friction (CoF). Five independent ANN models were created to understand the relation between the inputs and outputs. The created models were then utilized for the minimization of the tribological properties and maximization of the mechanical properties. Table 1 portrays the input and output variables. However, in ANN modelling, the connection utilized for the standardization of each variable dropping between \(-1\) and \(1\) is shown in Equation (1), to limit the error.  
\[\text{X}_{\text{N}}=\frac{2(\text{X}-\text{X}_{\text{min}})}{\text{X}_{\text{ max}}-\text{X}_{\text{min}}}-1 \tag{1}\]  
where \(\text{X}_{\text{N}}\) corresponds to the normalized value, and \(\text{X}_{\text{max}}\) and \(\text{X}_{\text{min}}\) correspond to the maximum and minimum values of the input variable X.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Low** & **High** & **Average** & **Normal** \\ \hline \multicolumn{5}{c}{**Input variables (for all properties):**} \\ Molecular weight of UHMWPE & 1.2 & 9.2 & 4.882 & 1.988 \\ (million g/mol) & 0 & 3 & 0.72 & 0.758 \\ MWCNT (wt\%) & 0 & 30 & 8.606 & 13.244 \\ Fiber length of MWCNT (\(\upmu\)m) & 0 & 80 & 29.983 & 25.966 \\ Graphene (wt\%) & 0 & 5 & 0.248 & 0.744 \\ Sheet thickness of graphene (\(\upmu\)m) & 0 & 20 & 1.35 & 3783 \\ Sheet length of graphene (\(\upmu\)m) & 0 & 40 & 6.058 & 13.190 \\ CF (wt\%) & 0 & 20 & 2.529 & 5.081 \\ Fiber length of CF (\(\upmu\)m) & 0 & 1000 & 106.621 & 308.92 \\ Fiber OD of CF (nm) & 0 & 7000 & 689.361 & 1959.33 \\ HAP (wt\%) & 0 & 70 & 7.393 & 15.401 \\ HAP particle size (\(\upmu\)m) & 0 & 7.5 & 1.017 & 2.320 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Higher and lower values of every input and output parameter, with its normal deviation and average.
# Investigation on the Measurement Method for Output Torque of a Spherical Motor
## 1 Introduction  
In recent years, with the development of industrial automation and the progress of science and technology, multi-degree-of-freedom (multi-DOF) motors are more demanded in aerospace, intelligent transportation, medical and industrial robots, and other industrial applications [1]. A traditional multi-DOF motion system, which consists of multiple single-DOF motors and transmission mechanical structure [2], has the problems of low reliability, low efficiency and high energy consumption [3; 4]. To solve the above-mentioned problems, a permanent magnet spherical motor (PMSM), which is a kind of multi-DOF motor, has been developed and has attracted attention from researchers and practitioners.  
Output torque is one of the essential performance indicators to evaluate the performance of the PMSM. However, output torque measurement is difficult in the research of PMSM. Many researchers have proposed methods to measure the output torque of PMSM at different structures. In the work of [5; 6], a torque measurement system is designed with a pressure sensor mounted in a torsion cantilever bracket structure. In [7; 8], a torque measurement system with a torque sensor installed in the circular and curved-guide rail is designed for different PMSMs. Moreover, for an inductive spherical motor, an output torque measurement system consists of a brushless motor and a six-axis force sensor installed on the rotor ball, is developed in the papers [9; 10]. In [11], a tension meter is utilized to measure the cogging torque of a spherical motor, based on the principle of the torque balance method. So far, the above methods for torque measurement are specially developed for the existing structures of PMSMs, which lack of generality. Furthermore, these measurement systems havecomplex structures and low maintainability, and can only measure the static torque in the form of spin motion. These methods are not proper for the PMSM in this paper and cannot be directly applied for the torque measurement.  
In this paper, the output torque of a 3-DOF PMSM is simulated and experimentally studied by considering friction loss and other factors of the inclined mode of excitation current. A MEMS gyroscope sensor is utilized to measure the rotor angular acceleration, which is then used to calculate rotor output torque on the basis of rotor dynamics equation for PMSMs. Simultaneously, the finite element simulation is used to analyze the magnetic field distribution of the PMSM, and then the automatic dynamic analysis of mechanical systems (ADAMS) is conducted for the mechanical simulation. By comparing and analyzing the simulation results with the experimental data collected by the MEMS gyroscope sensor, it is found that the output torque of the simulation is larger than that measured in the experiment because the friction is neglected in the simulation. The analysis results verify the feasibility of the proposed torque measurement for PMSMs.
## 5 Experimentation Process
### Anomaly Detection
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1 Score**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**ROC-AUC**} \\  & **I** & **O** & **I** & **O** & **I** & **O** & \\ \hline LOF (\(K=2\)) & 0.91 & 0.22 & 0.95 & 0.13 & 0.93 & 0.17 & 0.87 & 0.495 \\ ISOF (\(N=1000\)) & 0.87 & 0.00 & 0.71 & 0.01 & 0.78 & 0.00 & 0.64 & 0.096 \\ SGD One-Class SVM & 0.93 & 0.13 & 0.49 & 0.68 & 0.65 & 0.21 & 0.51 & 0.567 \\ Elliptic Envelope & 0.87 & 0.01 & 0.72 & 0.03 & 0.79 & 0.02 & 0.66 & 0.173 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Outlier detection methods’ performance. I: inlier. O: outlier. Figure 14: Confusion matrix for Local Outlier Factor \((K=2)\). Although the table shows that most algorithms seemed to obtain poor performance, particularly when predicting illicit labels, it is worth noting that these predictions were based only on the features of the data and did not take into account their labels at any moment. Thus, they were expected not to reach the performance of traditional supervised learning models, but on the other hand, there was the advantage of not needing manually obtained labels, avoiding a tedious and complicated process. Moreover, these methods are noticeably more performant than others based on deep learning frameworks or graph-related solutions and therefore are more suitable to be applied on massive datasets or even the entire blockchain. Still, these results show that Hypothesis 1 barely holds true when using strictly feature-based anomalies. Thus, the experiments suggested that the anomaly concept might be extended by taking into account other aspects such as the relational information of the transactions, which indeed, has been proven to be crucial in the context of fraud detection, as shown in other investigations. This could be achieved by making use of more sophisticated graph anomaly detection frameworks such as Graph Autoencoders [44], which identify structural anomalies based on high link reconstruction errors, or hybrid models such as DOMINANT [40], which combine both feature and structural anomalies.