esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Calculation Method for Active Anti-Floating of Elliptical Basements by Relief Wells
## 4 IDPR Anti-Floating Design and Case Application
### Verification of Actual Engineering Case  
Taking the engineering case in literature [12] as an example, the project is located in the underground car park of a hospital in Huadu District, Guangzhou City, and the distribution of the relief wells is shown in Figure 12, where J1-J8 are the relief wells, S1-S9 are the pressure measuring tubes of the base plate, and W1-W5 are the peripheral water level observation holes.  
Figure 11: Comparison of results of elliptical pits with different aspect ratios calculated using different simplified methods in the pressure-bearing mode: (**a**) Elliptical pit with \(a=90\) m, \(b=70\) m; (**b**) elliptical pit with \(a=100\) m, b = 60 m; (**c**) elliptical pit with \(a=120\) m, \(b=50\) m; (**d**) elliptical pit with \(a=160\) m, \(b=40\) m.  
According to the statistics, the final parameters for each seepage calculation for this case are as follows: influence zone of seepage field \(R_{0}\) = 300 m; the head height at the edge of the impact area is assumed to be \(H_{01}\) = 9.5 m under normal working conditions; \(H_{02}\) = 9.5 m under extreme rainfall conditions; the diameter of the relief well is \(r_{\rm w}\) = 1 m; and the well head height is \(h_{\rm w}\) = 0.05 m; thickness of gravelly sand layer \(T_{0}\) = 11 m; thickness of powdery clay layer \(T_{1}\) = 18 m; depth of relief well and thickness of hydrophobic layer \(T\) = 1.7 m; thickness of cut-off wall \(d\) = 0.77 m; permeability coefficient of highly permeable and hydrophobic layer \(K\) = 2.2 \(\times\) 10\({}^{-1}\) cm/s; permeability coefficient of weakly permeable layer \(K_{1}\) = 2 \(\times\) 10\({}^{-4}\) cm/s; permeability coefficient of highly permeable layer \(K_{2}\) = 2.2 \(\times\) 10\({}^{-1}\) cm/s; permeability coefficient of pit cut-off wall \(K_{\rm w}\) = 5 \(\times\) 10\({}^{-1}\) cm/s; thickness of the weakly permeable layer at the bottom of the pit waterproof curtain \(D\) = 16 m; number of wells \(n\) = 8.  
The total flow rate of the site is 1606 m\({}^{3}\)/d in the final calculation, the total flow rate of the site is 1622 m\({}^{3}\)/d in the finite element calculation, and the average value of the measured total flow rate is 1563 m\({}^{3}\)/d. The distribution of the seepage field inside the pit curtain under the different calculation methods is shown in Figure 13, from which it can be seen that the simplified calculation is accurate and can be safely used in the project with high reliability and practicality.  
Figure 12: Schematic diagram of the location of the relief wells in the garage. “Reproduced with permission from [PERSON] et al. [12], Application of interception and drainage anti-floating system in treatment of uplift accidents; published by Chinese Journal of Geotechnical Engineering, 2018”.
## 3 Outcomes
### Assessing Learner Performance  
COMET's metrics allow regular monitoring of learner performance on assessments. These data help us understand the effectiveness of our instruction and can aid us in identifying any issues with the assessments we create. In the past few years, COMET has begun including pre-assessments with every lesson. The pre- and post-course assessments, offered upon launching the course and immediately following completion, are aligned to measurable learning objectives associated with each lesson, and the questions are weighted equally across both assessments (in terms of score, objective, level of cognition, content area, and skill application). Performance on assessments can therefore offer a metric to measure the impact of the lesson on learner comprehension and skills application.  
Comparisons of the pre- and post-test scores for three recent satellite training case exercises (Figure 15) show an increase in the mean post-lesson quiz scores over the pre-assessment scores. This increase suggests that upon completion of the lesson, learners have gained enough functional knowledge to better answer a challenging set of questions based on the lesson's learning objectives. The improvement in scores can help to demonstrate the effectiveness of the instruction. The materials remain available online as a resource for learners so they can return to refresh skills as needed, and if they also enroll in booster questions, there are increased opportunities for practice and retention. The final booster question asks the learner to draw the connection between what they have learned in the lesson and its applications to on-the-job duties, which gives us information about how they are actively implementing their learning. We are hopeful that the increased knowledge and skill imparted through these case exercises and our educational materials remain in use well beyond the lesson.  
Figure 15: The bar graph displays the mean pre-assessment and post-lesson quiz performance (average percent out of 100) of learners who took both assessments for each of three case exercise lessons.
# Tensioned Multi-View Ordered Kernel Subspace Clustering
## 3 The Proposed TMOKSC Algorithm
### The Optimization Algorithm
#### 3.2.2 Solve \(\mathbb{C}\)  
When the other parameters are fixed, we will solve \(\mathbb{C}\) by:  
\[\mathbb{C}=argmin\Big{(}\alpha\|\mathbb{C}\|_{\otimes}+\langle Y,\mathbb{Z}- \mathbb{C}\rangle+\frac{\mu}{2}\|\mathbb{Z}-\mathbb{C}\|_{F}^{2}\Big{)}. \tag{15}\]  
In this problem, it can be solved by the transformation of tensors \(\mathbb{Q}=\frac{Y}{\mu}+\mathbb{Z}\).  
\[\mathbb{C}=argmin\Big{(}\alpha\|\mathbb{C}\|_{\otimes}+\frac{\mu}{2}\| \mathbb{C}-\mathbb{Q}\|_{F}^{2}\Big{)}. \tag{16}\]  
Here, the t-SVD [26] transformation can be performed on the tensor \(\mathbb{Q}\). The spectral norm \(\|\cdot\|_{\otimes}\) corresponds to the sum of the singular values after the t-SVD decomposition of the tensor slice. As we know, the t-SVD models achieve the cyclic structure by t-product. So, the diagonal elements in the core tensor represent the \"energy intensity\" of the sample in a certain view. The larger singular values correspond to more significant subspace structures, which are manifested as the main components in the Fourier domain. The core tensor suppresses the weights of the noise perspective; at the same time, it also coordinates the contributions to clustering. However, since the tensor representation requires the same dimension for incomplete multi-views, the missing samples need to be restored.
## 3 Definitions and Assumptions  
### Building Voronoi Graph  
A graph \(G\) is defined as a set \(G=(V(G),E(G))\), where \(V(G)\) is the vertex set and \(E(G)\) is the edge set [50]. This article addresses the _building Voronoi graph_\(G\), as a topological map of a building.  
Consider a building with \(L\) floors. \(\pi:V(G)\rightarrow\{1,2,\ldots,L\}\) is an _index_, and the graph \(G^{\pi}=(V(G),E(G),\pi)\) implies that vertex \(i\) has index \(j\) if \(\pi(i)=j\). The indexing \(\pi\) induces a subgraph on \(G\) as follows: \(G_{i}^{\pi}=(V(G_{i}^{\pi}),E(G_{i}^{\pi}))\subset G\) where \(V(G_{i}^{\pi})=\{j\in V(G)|\pi(j)=i\}\) and \(E(G_{i}^{\pi})=\{(j,k)\in E(G)|\pi(j)=\pi(k)=i\}\).  
\(G_{i}^{\pi}\) is used as a graph of the \(i\)-th floor in the building. \(G_{i}^{\pi}\) is a 2D Voronoi graph, representing the topological map of the \(i\)-th floor.  
Using elevators in the building, a robot can move from one floor to another floor. Therefore, one introduces a link which is associated with an elevator. A link is defined as follows. An edge \((i,j)\in E(G)\) is a _link_ if \(\pi(i)\
eq\pi(j)\). Let \(G_{l}\subset G\) define the graph indicating all links. \(G_{l}=(V(G_{l}),E(G_{l}))\) where \(V(G_{l})=\{i\in V(G)|\exists j\in V(G)\) such that \((i,j)\in E(G)\) and \(\pi(i)\
eq\pi(j)\}\) and \(E(G_{l})=\{(i,j)\in E(G)|\pi(i)\
eq\pi(j)\}\).  
Figure 1 shows a building Voronoi graph associated with a building with three floors. In Figure 1, links are plotted with dotted curve segments. In Figure 1, there are two elevators. One elevator is represented with links to the left of the graph, and the other elevator is represented with links to the right of the graph.  
Figure 1: A building Voronoi graph associated with a building with three floors. Links are plotted with dotted curve segments. One elevator is represented with links to the left of the graph, and the other elevator is represented with links to the right of the graph.
## 5 Results
It is worth noting that rigid body modes have also been predicted by this method in addition to natural frequencies corresponding to flexible modes. Figure 13 compares the receptance component H\({}_{Y_{1}Y_{2}}^{\text{ABC}}\), describing the translational displacement at node 1 due to an excitation force applied at point 2, across three methods, showing a strong alignment at low frequencies, where it represents the translational displacement at node 1 due to a force at point 2. Figures 14 and 15 similarly compare H\({}_{Y_{1}\theta_{2}}^{\text{ABC}}\) and H\({}_{Y_{2}\theta_{2}}^{\text{ABC}}\), representing translational displacements at nodes 1 and 2 due to couples at node 2. These figures demonstrate a strong correlation in undamped systems at low frequencies. However, deviations are expected at higher frequencies and in damped systems with complex geometries due to increased modeling challenges. Figures 13-15 demonstrate the effect of cross-coupling between substructures. However, it is important to note that  
Figure 14: Comparison of the receptance component H\({}_{14}\) (H\({}_{Y_{1}\theta_{2}}^{\text{ARC}}\)) using three different methods. Figure 15: Comparison of the receptance component H\({}_{34}\) (H\({}_{Y_{2}\theta_{2}}^{\text{ABC}}\)) using three different methods. for higher-frequency ranges and in damped systems with more complex geometries, deviations between the methods are inevitable. These deviations may be attributed to the increased complexity and the challenges associated with accurately modeling the dynamic interactions at higher frequencies. The close alignment of results across the three methods is expected due to the use of three rigidly coupled beam elements without damping, where the receptance of substructures is derived from the beam element's mass and stiffness matrices. Additionally, the analysis focuses on low-frequency ranges, where such methods typically yield consistent results due to the simplified dynamic behavior of the system. This comparison demonstrates the validity and reliability of the generalized coupling method for predicting the dynamic structure responses composed of multiple substructures.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 3 Results and Discussion
### Influence of Adsorption Conditions
#### 3.3.3 Effect of Concentration  
Figure 11 shows the effect of concentration on the removal of methylene blue. The removal capacity of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) increased with an increase in methylene blue concentration. This can be attributed to the increased effective collisions between the AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) and methylene blue, resulting in higher removal capacity for the methylene blue solution [43].  
Figure 11: The effect of concentration on the removal of methylene blue (contact time = 90 min, dosage = 1 g/L, temperature = 25 \(\pm\) 0.5 \({}^{\circ}\)C, initial pH = 7.00).  
Figure 10: The effect of (**a**) AC-CuO; (**b**) AC-ZnO; (**c**) AC-TiO\({}_{2}\) contact time on the removal of methylene blue (initial concentration = 5 mg/L, dosage = 1 g/L, temperature = 25 \(\pm\)0.5 \({}^{\circ}\)C, initial pH = 7.00).
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 4 Performance and Evaluation
### Ear Model Results  
The state-of-the-art networks are compared with a 3-layer simple Convolutional Network (SimpleNet) to verify the effectiveness of transfer learning, while selecting the best performing network to extract ear features from images. Table 1 presents the comparison of these networks in terms of parameters and depths.  
Figure 8 shows the Cumulative Match Characteristic (CMC) curves of the algorithms mentioned in Table 1 on the test set. The CMC curve [39] is a vital evaluation index for pattern recognition systems such as the face, fingerprint, etc. It can be seen from the figure that the Rank1 and Rank5 recognition rates of the VGG19 network are the highest, so the VGG-Ear model is established to extract ear features. Moreover, the recognition rates of the transfer learning models are higher than that of the direct convolutional network (SimpleNet).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Trainable Parameters** & **Non Trainable Parameters** & **No. of Layers** \\ \hline VGG19 & 51,813 & 20,024,384 & 19 \\ ResNet50 & 206,949 & 23,587,712 & 50 \\ InceptionV3 & 206,949 & 21,802,784 & 159 \\ Xception & 54,528 & 20,861,480 & 126 \\ MobileNet & 103,525 & 3,228,864 & 28 \\ SimpleNet & 19,392 & 0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of state-of-the-art models in terms of parameters and depths.
# Research on Temperature Compensation Method in Crankshaft Online Measurement System
###### Abstract  
The crankshaft online measurement system has realized the full inspection function with fast beats, at the same time it requires for high-precision measurement. Considering the effect of ambient temperature and temperature changes on measuring machine, the calibration part, the measured crankshaft and displacement sensor, a temperature compensation method is proposed. Firstly, relationship between calibration part and ambient temperature can be get through the zero calibration. Then use the material properties to obtain compensation values of the calibration part and the measured crankshaft part at different temperatures. Finally, the compensation parameters for displacement sensor can be obtained through the BP algorithm. The improved dragonfly algorithm (DA) is used to optimize the parameters of BP neural network algorithm. Experiments verify the effectiveness of IDA-BP for LVDT in temperature compensation. After temperature compensation, the error range of main journal radius is reduced from 0.0156 mm to 0.0028 mm, the residual error decreased from \(-\)0.0282 mm\(\sim\)\(+\)0.0018 mm to \(-\)0.0058 mm\(\sim\)\(-\)0.0008 mm. The influence of temperature changes on the measurement is reduced and measurement accuracy is improved through the temperature compensation method. The effectiveness of the method is proved.  
online measurement; displacement sensor; DA-BP neural network; temperature compensation 2021 14 August 2021 18 August 2021 1
## 2 Materials and Methods  
### Radio Frequency Setup  
Figure 1 provides a schematic overview of the in-house developed RF setup, while Figure 2 provides a picture of the setup. RF electromagnetic waves with frequencies in the range of 26.7-29.3 MHz were generated at an input power of 12-14 W. The RF generator was built according to the flyback topology design using an air-core transformer (i.e., Tesla coil). The hot end of the high voltage transformer was connected to the electrode terminal made of a copper coin with a diameter of 16.25 mm. The cold end was connected to the base terminal of an NPN bipolar junction transistor (type 2 SC2275A, SavantIC Semiconductor, Irvine, CA, USA). The primary and secondary coil consisted of enamelled copper wire winded around glass tubes, with no pinch between turns. A resistor with a resistance of 887 \(\Omega\) was used in addition to two power supplies (Aim-TTi EX354 RD, 280 W, RS Components, Brussels, Belgium), the first to provide a permanent positive bias at the base-emitter terminal of the transistor, and the second to provide adjustable power. A cooling fan was installed to prevent damage to the secondary coil from over-heating during energy transmission to the load.  
The setup was designed to treat samples in small glass vials (4 mL, 45 \(\times\) 14.7 mm, BGB Analytik Benelux B.V., Harderwijk, the Netherlands) with a polypropylene screw cap lid with a hole in the middle. To reduce heat dissipation through the glass vial during RF heating, vials were placed inside a polyethylene foam case (Figure 1). In order to record sample temperatures during RF treatments, an ethanol-filled thermometer was inserted into the samples through the hole in the lid, using PTFE tape for fixation. In future studies, researchers could also use optical fibre sensors to measure temperature, in order to minimise interactions with the electromagnetic field. Electromagnetic resonances were quantified by means of an external function generator (Aim-TTi TG5011A, 50 MHz, RS Components, Brussels, Belgium). The RF frequency and waveform were quantified by means of the voltage probe of an oscilloscope (RS PRO IDS1152 AU, Bandwidth 150 MHz, RS Components, Brussels, Belgium).
# Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process
## 1 Introduction
### Problem Statement  
The concept of ubiquitous cities (U-Cities) is based on sharing information and data by collaborating and utilized various ICTs between city's services [17]. The idea of ubiquitous computing first arose around 1988 although the first to label it was [PERSON] who was recognized to be the pioneer of Ubiquitous computing after the project presented at the Xerox Palo Alto Research Centre in the US [18; 19; 20]. However, the available technologies that allow utilizing computers practices were limited at the time. Until recently, South Korea has been the pioneer to adopt and apply ubiquitous computing in their cities. With the urbanization process in Seoul, Songdo is one of the successful examples that a new city district constructed with implementing the ubiquitous computing technology into city service systems though analysis the collected information and dispatches [21].  
\"Ubiquitous\" means having access to any data or services at any time and in any place via any device through the network [22; 23]. The idea of U-cities is implemented by the process of ubiquitous computing, which is integrating computing technology into objects seamlessly whilst being invisible to the end-user. By picking up information via sensors and actuators in the built urban environment, all devices within the U-cities' network can interact with each other in the system continuously and transfer information simultaneously [24]. The heavy reliance of U-cities on modern information technology obtrudes an additional set of threats from natural and man-made hazards. These hazards may affect sensors, communications, or the process of computing.  
The issue needs urgent attention, and the presented paper identifies the above-mentioned vulnerabilities and performs a detailed risk assessment of U-cities using the Analytic Hierarchy Process (AHP) approach.
## 4 Discussion  
### Discrimination of Major Crops  
For crop yield estimation using vegetation indices, the index should represent a particular crop. Annual crop inventory map was not available before the year of 2011 in Canada, making it difficult to estimate yields in a historical year. Using the cloud computing platform of the Google Earth Engine, [PERSON] et al. (2015) produced high-resolution (30 m) circa-2010 cropland extent classification for North America; however, different crops were not identified. Using annual crop classification maps of multiple years, crop-specific masks were generated and used for yield forecast in Canada (2016); however, the masks represent spatio-temporal \"likelihood\" distribution of crops and do not reflect the annual variation of the crop area changes. Using the decision tree algorithm, we were able to discriminate the four major crops with minimal requirement of ground truth data for training the classifier, thus mapping major crop types back to 2000 using 250 m time-series MODIS data ([PERSON], 2011). Although a satisfactory result was achieved to extract \"pure\" crop pixels, the majority of pixels at 250 m resolution were mixed in the regions of study, leading to large omission/commission errors in classification and impacting the creation of crop-specific masks (Figure 2). County-level phenological patterns for the three annual crops, represented by EVI2 extracted using crop-specific masks, were contaminated by this pixel mixing effect (Figure 3). Many experimental studies have been conducted to merge high-spatial-low-temporal resolution data with low-spatial-high-temporal resolution data to generate time-series high resolution data ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). Although applying this approach to the regional scale can be a challenge due to huge data volume and the need for intensive computational resources, it could be applicable as cloud computing becomes mature, or if the approach could be implemented at field scale instead of pixel scale.
## 2 Literature Review
The findings of the random forest model were nearly identical to those of the ANFIS, while the classification and regression tree had the lowest level of correctness. To estimate compressive strengths, [PERSON]. et al. [33] used three models namely genetic programming (GEP), ANFIS, and MLPNN that is a form of ANN. The results of the study showed that GEP models for data predictions are more precise than machine learning (ML) and that a new mathematical formula might be created and utilized to estimate additional database properties. The strength of lightweight concrete was predicted by [PERSON] et al. [25] using six machine learning algorithms: GPR, EL, SVMR, enhanced SVMR and GPR, and ensemble learning (EL). The results of this study showed that the optimized GPR model had the greatest accuracy. Furthermore, the improved GPR and SVMR models showed excellent behavior. [PERSON] and [PERSON], 2016 [34], applied the fuzzy inference model to improve a new precise procedure and to evaluate the square concrete columns' strength and strain subjected to a vertical load strengthened by fiber polymer wraps. An experimental compressive strength of 261 and a crucial experimental strain of 112 were gathered from the previous studies. The outputs of the finally proposed (Takagi-Sugeno) fuzzy inference models were well agreed with the experimental data of both strain and strength [34]. In order to estimate the density and compressive strength of local concrete mixtures based on the specific properties of their constituents, this work aims to develop the binary particle swarm algorithm (BPSA) with a new procedure. Because the experimental data currently available may be regarded as discrete space, and since the binary particle swarm approach is quick to reach the best answer with fewer iterations than other algorithms, it was chosen for this investigation. Moreover, artificial swarm intelligence (ASI) may considerably improve prediction accuracy and collective insights.