esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sensitive Wavebands Selection
Finally, the characteristic variables with high correlation were selected according to the principle of the best prediction effect with the least number of variables. The final characteristic variables and the prediction performance are shown in Table 4.  
According to Table 4, the prediction effects of soil nitrogen by the method of CARS-BIPLS-PLS achieved good results when soils were detected (4.53 \(<RPD<\) 5.03, 0.946 \(<R_{p}^{2}<0.966\), 0.172 g/kg \(<RMSE_{p}<\) 0.128 g/kg) after 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C drying. Additionally, the corresponding characteristic bands focus on 928-960 nm and 1638-1680 nm. Compared with the full spectrum model by PLS, the prediction of the characteristic bands by CARS-BIPLS-PLS is obviously better than that in the condition when the soil was detected at a 25 \({}^{\circ}\)C placement. The reason might be that the vibration \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Int-Number** & **Interval-ID** & **RMSECV** & **Number of Variables** & **Int-Number** & **Interval-ID** & **RMSECV** & **Number of Variables** \\ \hline
20 & 1 & 0.174 & 400 & 10 & 4 & 0.168 & 200 \\
19 & 18 & 0.173 & 380 & 9 & 8 & 0.167 & 180 \\
18 & 17 & 0.172 & 360 & 8 & 11 & 0.167 & 160 \\
17 & 16 & 0.172 & 340 & 7 & 5 & 0.167 & 140 \\
16 & 14 & 0.171 & 320 & 6 & 6 & 0.167 & 120 \\
15 & 13 & 0.170 & 300 & 5 & 2 & 0.167 & 100 \\
14 & 15 & 0.170 & 280 & 4 & 7 & 0.166 & 80 \\
13 & 12 & 0.170 & 260 & 3 & 3 & 0.167 & 60 \\
12 & 9 & 0.169 & 240 & 2 & 20 & 0.169 & 40 \\
11 & 10 & 0.168 & 220 & 1 & 19 & 0.226 & 20 \\ \hline \end{tabular}
\end{table} 
Table 2: The variable selection results of BIPLS when the spectra with 400 points are divided into 20 segments. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**CARS Algorithm**} & \multicolumn{2}{c}{**BIPLS Algorithm**} \\ \cline{2-4}  & **Characteristic Variables (nm)** & **Number** & **Characteristic Intervals (nm)** &
\begin{tabular}{c} **Serial Number of** \\ **Characteristic Interval** \\ \end{tabular} \\ \hline
25 \({}^{\circ}\)C & 1614 1626 1647 1649 1651 1664 & 9 & 1036–1078, 1633–1666 & 4, 19 \\
40 \({}^{\circ}\)C & 1638 1640 1642 1644 1669 1671 & 10 & 991–1034, 1633–1666 & 3, 19 \\
60 \({}^{\circ}\)C & 928 93 1559 1605 1656 1661 & 1080–1022, 1598–1631, \\
1669 167 1617 1675 & 1633–1666 & 5, 18, 19 \\
80 \({}^{\circ}\)C & 940 942 1644 1669 1671 & 10 & 1450–1486, 1524–1559 & 14, 16 \\
105 \({}^{\circ}\)C & 958 1141 942 1644 & 5 & 901–944, 1633–1666 & 1, 19 \\ \hline \end{tabular}
\end{table} 
Table 3: The selected characteristic variables, characteristic intervals, and sensitive wavebands. of N-H was covered by little soil water content in the full spectrum [40]. However, the most optimal characteristic variables were selected with combined CARS and BIPLS and the noise information was also removed [30; 31; 32].
## 3 The Multiple Workflows Allocation Model
### Cloud Workflow Allocation Framework
#### 3.1.3 Cloud Resource Layer  
This layer aggregates the cloud resources used for workflow processing as per the user's demands. Virtual resources are created from the physical instances using hypervisor, as mentioned earlier to improve the overall performance of the cloud platform. It is explained with its two components as follows:  
* **Virtualization Layer:** The VM layer is an abstract layer between the network and physical hosts. One operating system per host is inflexible for the usage of virtualization, leaving unused resources. A host machine can create more VMs by using this VM layer on which additional operating systems can be installed. In this way, all the physical resources are shared as VMs with higher efficiencies and lower hardware costs. A VM can be viewed as an application environment that is installed on specific software to work as a dedicated physical resource. VM provides the feel of dedicated hardware to the end users. Software known as a hypervisor is responsible for all types of VM management. Each VM offers its own virtual hardware, such as CPUs, memory,network interfaces, etc. This virtual hardware is mapped to the physical machines, reducing the overall costs and overhead.
* Physical Host Layer: It consists of the fundamental physical computing resources, i.e., CPUs, memory, and network resources, that make up a cloud infrastructure.
## 2 Related Works
### Long Short-Term Memory  
The LSTM model possesses a unique architecture to control the update process of its memory state using a gating mechanism, which is capable of counteracting the vanishing gradient problems of traditional recurrent neural networks (RNNs) [8; 9]. As for LSTM-based models, the prediction always depends on the previous data samples, and so, missing or corrupted data samples significantly affect the performance of the models. Hence, dealing with this problem of missing or corrupted data is crucial for such models. Researchers use different approaches to address this problem. For example, the masking and imputation strategies proposed by [10] exhibited excellent outcomes. The proposed model obtained a mean absolute percentage error (MAPE) of only 2.10% in hourly traffic volume and average annual daily traffic (AADT) forecasting tasks. Although the results demonstrated a remarkable improvement, the model suffers from a lack of robustness due to the occurrence of non-recurrent events.  
Taking into account weather data and information about adjacent road segments is a very well-established solution to overcome the aforementioned problem. In [11], both rainfall and speed data were used to feed an LSTM model for traffic speed forecasting, which led to an enhancement both in terms of robustness and accuracy. The Bi-LSTM architecture proposed in [12], which considers the influence of air pollution, weather conditions, and temporal features of traffic states, also led to a more robust prediction model, which achieved a 1.02% MAPE improvement relative to a CNN-based baseline.  
Capturing both the spatial and temporal correlations of traffic states also plays a vital role in enhancing the robustness of a traffic prediction model. In [13], a capsule neural network (CapsNet) based on a CNN was used to obtain the spatial correlations, and a nested LSTM structure was employed to capture the temporal dependencies of the traffic states in order to effectively increase both the robustness and accuracy of the traffic estimations. The proposed solution achieved a MAPE of only 0.211 for traffic speed prediction over a 20 min time horizon on a dataset from Gaode Maps ([[https://ditu.amap.com/](https://ditu.amap.com/)]([https://ditu.amap.com/](https://ditu.amap.com/)) accessed on 13 April 2022). A recent study also suggested that the elimination of non-Gaussian disturbances can effectively boost the robustness of an LSTM model [14].
# Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
## 4 Experimental Results and Analysis  
### Experiment Setup  
The experimental platform for fault diagnosis of PMSM is shown in Figure 1. The current sensor and the vibration sensor collect signals from the motors, and data acquisition (DAQ) card uploads the signal data to the computer for the subsequent research on the algorithm.  
Figure 1: The experimental platform for fault diagnosis of permanent magnet synchronous motors (PMSM).  
The experimental platform includes two low speed PMSMs (90 TDY115-2B), current sensors (The experimental platform), piezoelectric accelerometer (CA-YD-186) and DAQ card (NI-PCI6225). One of them is a normal motor and the other one is artificially set to a 15% stator inter-turn fault. These current sensors acquire the stator current of the motors and transform the current signal into voltage signal that is convenience for the host computer to deal with. Piezoelectric accelerometer gets the vibration information and its output is amplified by signal conditioner (YE3832). In the host computer, A DAQ card, NI-PCI6225, was installed, to which a junction box, BNC-2110, was connected. They can upload the data from sensors to the host computer. After the data were saved, the algorithm in this paper was implemented in MATLAB (2015b, MathWorks, Natick, MA, USA., 1984).
## 2 Experimental Program
### Compressive Strength Test  
Six prisms of SCMUs wall assemblages (denoted by PC1-PC6) as illustrated in Table 2 were prepared according to the procedure specified by the Chinese Masonry Code for compressive strength test [17]. The compressive strength test of masonry prism was conducted on units of dimensions of 590 mm length, 240 mm width, and 790 mm height, as shown in Figure 3. The masonry prisms were constructed with four courses in running bond pattern. The prisms were fully grouted and wooden forms were used to ensure the proper finishing of the grout at the end of the face-shells. The prisms were cured in standard conditions (22 \(\pm\) 3 \({}^{\circ}\)C and 95% RH) for 28 days. Before testing, the prisms were capped with a 10 mm thick cement mortar to have a uniform bearing surface.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Density** & **Thermal Conductivity** & **Specific Heat** & **Young Modulus** & **Yield Strength** \\
**(kg/m\({}^{3}\))** & **(w/m\(\cdot\)K)** & **Capacity (J/kg\(\cdot\)C)** & **(kN/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** \\ \hline
30 & 0.04 & 1300 & 3.6 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mechanical and thermal properties of expanded polystyrene (EPS).  
Figure 2: SCMU under compression test machine.  
The compressive strength test was performed using a universal machine (Jinan Time Shijin Instruments Co., Ltd., Jinan, China) with capacity of 5000 kN and loading rate of \(0.05\pm 0.01\,\mathrm{N/mm^{2}/sec}\). The prism was placed in the lower plate and then both centroidal axes of the prism and the center of thrust of the machine were aligned. Test setup of compressive strength is shown in Figure 4.  
Table 2 shows the results of compressive strength test of masonry prisms. The results revealed that the average compressive strength and the standard deviation of tested prisms were \(9.21\,\mathrm{N/mm^{2}}\) and \(0.4\,\mathrm{N/mm^{2}}\), respectively. The typical failure pattern observed during the compressive strength test is shown in Figure 5. It was observed that the prisms failed due to the development of vertical cracks along their height. Apparently, the lateral expansion of the mortar induced high tensile stresses in the blocks, causing them to crack and eventually fail. Generally, the pattern of failure is influenced by the modulus of elasticity of the different materials that made up the masonry system [20].  
\begin{table}
\begin{tabular}{c c} \hline
**Specimen No.** & **Compressive Strength (N/mm\({}^{2}\))** \\ \hline PC1 & 9.79 \\ PC2 & 9.58 \\ PC3 & 9.27 \\ PC4 & 8.85 \\ PC5 & 9.08 \\ PC6 & 8.71 \\ Average Compressive strength of prisms & 9.21 \\ \hline \end{tabular}
\end{table}
Table 2: Compressive strength results of masonry prisms.  
Figure 4: Compressive strength test setup.  
Figure 3: Masonry prism for compressive strength test (units in mm).
## 3 Methodology
### The Motivation  
As mentioned in the previous section, a lot of research has been performed to deal with noisy labels on image classification, and the most representative are methods that learn noisy labels directly and the semi-supervised methods that use a small number of clean samples for training and then generalize the model trained on that subset to unlabeled or weakly labeled sample dataset. However, the main problem with methods that learn noisy labels directly is that they usually face the major challenge of identifying mislabeled samples from hard training samples; thus, fixing this issue is crucial for improving the model's capabilities. This class of methods performs well only in simple cases, such as those where learning is easy or the number of classes is small. In addition, modifications to the loss function increase the training time for the model to converge. The semi-supervised techniques can significantly improve the noise robustness. However, hyperparameters introduced by these techniques make DNNs more susceptible to changes in data and noise types, and an increase in computational cost is inevitable.  
Based on these merits and demerits of existing technologies, the motivation of the proposed method is to provide a new idea and method for solving noisy labels effectively. Unlike previous research methods, we do not propose noise cleansing or noise robustness or semi-supervised algorithms; instead, we propose a new network model training strategy that considers mislabeled samples directly in the network training process. This method can effectively alleviate the adverse effects of noisy labels on network model training and improve the accuracy of network classification without changing the architecture of standard deep networks. Moreover, a new learning curriculum is designed to measure the complexity of the data by using the distribution density of the data in the feature space, and the complexity of the data is ranked in an unsupervised manner. The data are then divided into three subsets and input into the network \"progressively\" according to their complexity. This method provides a new idea of determining the \"easy sample\" in traditional curriculum learning and improves the generalization ability of the network.
## 3 Results
### ANN Results and Discussions
#### 3.2.1 Unity Plots  
Earlier a time-marching throughflow method for the off-design performance analysis of axial compressors was analyzed on rotor-67. The method was based on the Euler equations, and an inviscid blade force model was proposed to achieve desired flow deflection. The flow discontinuity problems at the leading and trailing edges were tackled by automatic correction of blade mean surface using cubic spline interpolation. The flow discontinuity issues at the leading and trailing edges were addressed by using cubic splitting to automatically fix the blade mean surface [11; 12]. In this research, multilayers, Feedforward, and a supervised learning Neural Network with the ReLU activation function are selected for training, testing, and validating the data set. The unity plot elaborates on the network's performance when the trained model is passed through the test dataset. The plot shows the trained model's generalizability such that the predicted samples should lie closer to the unity line. Figure 7a-d shows the test data unity plots of mass flow rate, pressure ratio, temperature ratio, and isentropic efficiency. The x-axis corresponds to the test dataset's target values, while the y-axis shows the predicted values obtained when the trained model is tested with the test data set.  
Figure 6: Loss function of the Neural Network model during train and test phase.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset Division.** & **MSE Value** \\ \hline Validation & 0.024 \\ Testing & 0.029 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean Square Error value at validation and test phase.
# Enhancement of Feed Source through Three Dimensional Printing
## 2 Generation of Wide Band Prototype
(**b**) Perspective glimpse of WBP. The WBP is suitable to manufacture using 3D printing techniques that utilize Vero CMYK (\(\epsilon_{r}=2.8\) and tan\(\delta\) 0.124). The respective size of the cylindrical rods and cube dimension variation are detailed in [19]. The length of perpendicular cylinders is maintained at 7.5 mm (0.3\(\lambda\)), which holds the cubes of defined dimensions. The cubes' dimension is increased in a step size of 0.5 mm (0.02\(\lambda\)) from minimum dimensions of 0.5 mm\({}^{3}\) until the maximum dimension of 7.5 mm\({}^{3}\). The optimal value of the cube size is 7.5 mm\({}^{3}\) and analytical calculations are performed to obtain the respective values of transmission magnitude and phase variations. The generated transmission magnitude and phase are arranged for specific layers of cubes in a five-layer structure. The phase values that correspond to the transmission magnitude of unity are considered and arranged in tabular form. The generated transmission magnitude and phase values for the arrangement of five layers of cubes are detailed in Tables 1 and 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transmission Magnitude** & **Transmission Phase (Degree)** & **x1** & **x2** & **x3** \\ \hline
# Instrumental Uncertainty of Conductance Transducers for
## 3 Metrological Characterization Methods  
### Electrical Stability  
This section describes the proposed characterization method of the conductance transducer's electrical stability. The nature of this type of transducer, where a relation between the dimensional quantity (the water level) and the electrical quantity (output voltage) is defined, justifying the characterization of the transducer's electrical behavior, namely, of its stability. The proposed electrical testing can be performed, in a first stage, for production quality control and maintenance purposes and, in a second stage, as a preliminary test performed in laboratory or in field, characterized by different electrical environments.  
It should be noted that, the laboratorial testing of conductance transducers is performed in a controlled environment, which is significantly different from the application scenario of conductance transducers in maritime reduced-scale models, constructed in large experimental indoor infrastructures, where influence quantities, such as air temperature and relative humidity, water temperature, and electrical power supply, show a larger variation.  
The characterization method of the electrical stability is divided in two parts: (i) power supply testing, in an empty condition (without connection to a conductance transducer) and a nominal input voltage set point of 15 V (DC); and (ii) the loaded condition test, where the same input voltage is applied to a conductance transducer, in a fixed half-immersion position in water, as schematically represented in Figure 2.  
Figure 1: Conductance transducer developed by LNEC.  
These tests were performed with two power supplies: (i) an off-the-shelf power supply (Agilent, model U8001A, Santa Clara, CA, USA), used for the testing of conductance transducers at laboratory; and (ii) a custom-made power supply (design and produced by LNEC), used for quality control and maintenance activities. The loaded condition test was performed for two conductance transducers (LNEC, internal reference id's 54.12 and 57.12) with different measurement intervals, respectively, \(\pm\)400 mm, and \(\pm\)140 mm.  
For each test, a three-hour duration was defined, and the voltage (input voltage in both the empty and the loaded condition tests, and the output voltage only in the loaded condition test) were measured considering an acquisition time period of two minutes, using a digital multimeter (HP, model 3457A). The tests were performed in the same laboratorial facility used to determine the conductance transducers linearity, reversibility, and repeatability (described in Section 3.2). Air temperature and relative humidity measurements were performed with a digital thermohygrometer recorder (Rotronic, model Hygrolog), while the water temperature in the loaded condition test was obtained from a resistance thermometer and a Wheatstone bridge (ASL, model F250). These measurements were performed simultaneously with the voltage measurements above mentioned.
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation  
The third taxonomy is focused on specifying the accomplished task of the found solutions. It is presented in Figure 4. For this analysis, we used the extended set of papers (including the survey extension). Eleven main tasks were found, with three of them requiring further separation into subtasks. We analyzed papers that led to 17 different end results of data processing. Fourteen of them were successfully applied to 2D image processing and are marked on the diagram with a green background of the box. We describe all the papers analyzed in the rest of this section, presenting them grouped by the completed task. To facilitate the understanding of the content, we decided to develop a single, universal example that the reader may follow and that we could reuse in each of the subsections. It was meant to serve two purposes: graphical and practical. We wanted something visually simple, schematically showing the idea, yet actually used in real applications. We decided to use a sample floor plan representing an apartment consisting of a living room, two bedrooms, a toilet, a kitchen, and a corridor. Such an example was selected for three primary reasons: (1) the leading input data type of this study is a 2D image, (2) it is a data structure that could be applied to all the main described tasks, and (3) it is subjectively easy in both preparation and later reading. To further simplify the navigation across this document, in Table 4 we present a matrix of referenced articles, this time organized by the low-level actual application and the processed input data type. It allows for direct navigation into the subsection describing the task of interest or applications available to held data structures.
# Towards a Real-Time Description of the Ionosphere:
## 4 Methodologies of Analysis
### Graphical Representation of the Statistical Results  
Several graphical representations, depending also on the dataset used for validation, were adopted to highlight at best the different climatological variations exhibited by the data.  
The following graphical representations were used for both the ionosonde and the COSMIC datasets:  
* Statistical distribution of residuals between measured and modeled (by IRI and IR-TAM) _fo_F2 and _hm_F2 values.
* Density plots between measured and modeled _fo_F2 and _hm_F2 values, with the corresponding best linear fit line. The following graphical representations were used only for the ionosondes dataset:
* Grids of _Res. Mean_, _RMSE_, and _NRMSE_ values between measured and modeled _fo_F2 and _hm_F2 values, as a function of the LT and the month of the year, for the three different levels of solar activity defined in Section 3.1.  
Differently, in the validation analysis based on the COSMIC observations, IRI and IRTAM _RMSPE_ binned values, and corresponding differences (IRTAM-IRI), have been plotted as a function of:  
* Geographic latitude vs. geographic longitude.
* Modip vs. LT hour.
* Modip vs. day.
* Modip vs. F10.781.  
Finally, statistical probability distributions of the residuals deviation ratio parameter \(R_{\text{cw}}\) were calculated for the entire ionosonde and COSMIC _fo_F2 and _hm_F2 datasets.