esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results  
### Data Preparation  
To identify structural components from 2D drawings, a large number of images with class labels are necessary to train and test the YOLO model. However, there is no public and general-purpose image dataset of structural drawings with multiple labeled classes in the field of object detection so far. Therefore, this study collected and established a dataset consisting of 500 images of 2D structural drawing with a fixed resolution of \(850\times 750\). Two types of structural drawing images were included in this dataset: the column layout plan image (CLPI) and the framing plan image (FPI). Each image was obtained by scanning a paper-based structural drawing. The components to be recognized in the image were divided into five classes: grid reference, column, horizontal beam, vertical beam, and sloped beam. Here grid reference refers to the corresponding numbers of grids, and the reason for regarding the grid reference as one of the structural components is that the successful recognition of the grid reference will be beneficial to the establishment of component coordinate systems in the future, which plays a significant role in matching the topological and semantic information of structural components. Moreover, the names of three types of beams are used to represent the different placement directions of them on 2D structural drawings. For example, if a beam spans horizontally, it will be regarded as a horizontal beam. Otherwise it will be considered as a vertical beam or sloped beam.  
Figure 4: Example of output for component detection result.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Data Preprocessing
#### 3.2.4 Image Augmentation  
In the data preprocessing step, the final step involves applying image augmentation techniques to the dataset. Image augmentation is a powerful approach that enhances the diversity and robustness of the training data, thereby improving the model's ability to generalize to unseen examples. For this purpose, the Tensorflow's ImageDataGenerator from the Keras API is employed. During the image segmentation step, various augmentation parameters are set to introduce controlled transformations to the images. These transformations include rotation, shifting, shearing, zooming, horizontal flipping, and vertical flipping. Specifically, the rotation range parameter is set to 90 degrees, the width and height shift ranges are both set to 0.3, the shear range is set to 0.5, the zoom range is set to 0.3, and the fill mode is set to'reflect'. These values are carefully chosen to introduce realistic variations in the data, mimicking potential variations in the real-world scenarios. By augmenting the data with such transformations, the model becomes more resilient to variations in the input images, improving its ability to accurately segment the lung and infection areas.
## 3 Analytical Methods and Applications
### Basic Analytical Operations  
In geographic information science, basic analytical operations include buffering, overlays, neighborhood-based analysis and map algebra. In the IoT scope, mostly buffering and overlaying have been recorded.  
Buffering is the process of creating zones around selected features, within a predefined distance from these features. An example of point buffering is shown in Figure 3, where red dots represent the features and blue circles the buffer zones. Overlay is a GIS operation that superimposes multiple data sets (representing different themes) together for the purpose of identifying relationships between them.  
Figure 2: The concept of UrbanRadar mobile application (Source: [13]).  
The type of intersection used in the overlay operation is one of the nine possible operations described by [PERSON] [30], i.e., union, touch, overlap, etc.  
Buffering has been observed in flooding models, for calculating the expected excess rainwater [32] and in assessing the risks of forest fire [33]. A combination of buffering and overlays has been used in assessing the devastation potential of earthquakes [34], in calculating the greenhouse emissions produced by livestock agriculture [35] and in tsunami emergency evacuation-planning scenarios [36].
# Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM
## 5 Fault Detection of Wind Turbine-Based Pitch Control System
### Performance Evaluation Index of Fault Detection Model  
The normal state and fault state of the pitch control system are labeled as Q = [0, 1], respectively, and the dataset containing two parts. The IHHO-LightGBM algorithm is used to detect WT fault. To reasonably evaluate the effectiveness of fault detection, the false negative rate (FNR) and false alarm rate (FAR), proposed by the confusion matrix as well as the F1-score, are used as the evaluation index of fault detection. The dichotomous mixing matrix is shown in Table 4 below:  
Under the second classification, the evaluation indexes of fault detection are as follows:  
\[\text{FNR}=\frac{\text{FN}}{\text{TP}+\text{FN}}\;, \tag{25}\]  
\[\text{FAR}=\frac{\text{FP}}{\text{TN}+\text{FP}}, \tag{26}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{27}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{28}\]  
\[\text{F1}-\text{score}=2*\frac{\text{Precision}*\text{Recall}}{\text{ Precision}+\text{Recall}}, \tag{29}\]  
TP means the quantity of negative samples defined as negative; FN means the quantity of negative samples defined as positive; FP means the quantity of positive samples defined as negative; TN means the quantity of positive samples defined as positive.
## 3 Brief Description of the CWRU Bearing Data Center Test Rig  
The test rig used to acquire the Case Western Research University (CWRU) dataset consists of a 2 hp Reliance Electric motor driving a shaft, as shown in Figure 2. A torque transducer and encoder were mounted between the motor and the load (i.e., a dynamometer).  
In order to study the response from damaged bearings, faults were seeded by electro-discharge machining (EDM) on the rolling elements and on the inner and outer races of the drive- and fan-end bearings (as described in Table 1). These artificial damages ranged from 0.007 to 0.028 inches (0.18 to 0.71 mm) in diameter size. Three monoaxial accelerometers oriented along the vertical direction were placed on the housing of the drive-end bearing (DE), on the fan-end bearing housing (FE), and on the motor supporting base plate (BA). The acceleration signals were sampled at 12 kHz for some tests and at 48 kHz for others. The operational conditions were stationary in terms of speed, while two loads were applied: at 0 Hp the resulting motor speed was of 1797 rpm, while at 3 Hp it was reduced to 1720 rpm. The complete exploration of the whole database is out of the scope of this work; hence, a small selection of particularly relevant acquisitions [26] was  
Figure 1: Scheme for the adopted methodology.  
Figure 2: The CWRU bearing test rig [32, 33].  
selected, as summarized in Table 2. Their corresponding raw time series are reported in Figure 3. For further details refer to [32; 33].
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 3 PL-MMSE Kalman Filter with Linear State Constraints
### Estimation Projection Method  
As a standard method to deal with constraints, the estimation projection method obtains the constrained estimate \(\hat{x}\) by projecting the unconstrained estimate \(\hat{x}\) onto the constrained surface. Define  
\[\tilde{x}=arg\min_{x}(x-\hat{x})^{T}W(x-\hat{x})\quad s.t.\quad Gx=g \tag{33}\]  
where \(W\) is a positive definite weighting matrix. The Lagrangian function used to solve this problem is  
\[J=(x-\hat{x})^{T}W(x-\hat{x})+2\lambda^{T}(Gx-g). \tag{34}\]The necessary conditions for the local minimum are given by  
\[\frac{\partial J}{\partial x} =0, \tag{35}\] \[\frac{\partial J}{\partial\lambda} =0. \tag{36}\]  
Solving (35) and (36) gives  
\[\tilde{x} =\tilde{x}-W^{-1}G^{T}(GW^{-1}G^{T})^{-1}(G\hat{x}-g), \tag{37}\] \[\lambda =(GW^{-1}G^{T})^{-1}(G\hat{x}-g). \tag{38}\]  
It is worthwhile to point out that the result given by the estimation projection is equal to the mean square method when \(W=I\). Hereby, Table 2 summarizes the steps of the PL-MMSE with linear constraints by the estimation projection method.
## 3 Experiments and Results  
### Mask Generator  
Based on the structure and process outlined in the previous section, the mask generator was constructed. Experiments were subsequently conducted to evaluate and validate the mask-generator method, as well as to assess the effectiveness of the mask-score selection mechanism.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation
### Experimental Analysis of Faulty Rolling Bearing
#### 4.2.4 EEMD  
In this method, the observation signal is decomposed with EEMD first, and then the Hilbert envelope spectra of each IMF can be obtained. The result is shown in Figure 14. In Figure 14a, the fault frequency of the inner ring, \(f_{ic}=106\) Hz, is extracted. The fault frequency of the outer ring, \(f_{oc}=70\) Hz, can be seen in Figure 14b. However, the features are not clear enough, the amplitudes of other frequencies are very high and the noise energy is still high.  
Figure 14: The Hilbert envelope spectra of each IMF after EEMD. (**a**) IMF1. (**b**) IMF2. (**c**) IMF3. (**d**) IMF4.  
Figure 13: The Hilbert envelope spectra of each component signal after VMD. (**a**) Component signal 1. (**b**) Component signal 2. (**c**) Component signal 3. (**d**) Component signal 4.
## 4 Exergoeconomic Analysis
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Component** & **Cost Balance Equation** & **Auxiliary Equation** \\ \hline Compressor & \(c_{1}\dot{E}x_{1}+c_{el,gt,1}\dot{W}_{comp}+\dot{Z}_{comp}=c_{2}\dot{E}x_{2}\) & \(c_{1}=0\) \\ Regenerator & \(c_{2}\dot{E}x_{2}+c_{5}\dot{E}x_{5}+\dot{Z}_{reg}=c_{3}\dot{E}x_{3}+c_{6} \dot{E}x_{6}\) & \(c_{5}=c_{6}\) \\ Combustion chamber & \(c_{3}\dot{E}x_{3}+c_{fuel}\dot{E}x_{fuel}+\dot{Z}_{cc}=c_{4}\dot{E}x_{4}\) & \(c_{fuel}=12\left(\frac{\dot{\delta}}{\dot{\zeta}_{\zeta}}\right)\) \\ First gas turbine & \(c_{4}\dot{E}x_{4}+\dot{Z}_{gt,1}=c_{4,gt,1}\dot{W}_{gt,1}+c_{5}\dot{E}x_{5}\) & \(c_{4}=c_{5}\) \\ Second gas turbine & \(c_{6}\dot{E}x_{6}+\dot{Z}_{gt,2}=c_{el,gt,2}\dot{W}_{gt,2}+c_{7}\dot{E}x_{7}\) & \(c_{6}=c_{7}\) \\ Steam generator & \(c_{12}\dot{E}x_{12}+c_{7}\dot{E}x_{7}+\dot{Z}_{sg}=c_{8}\dot{E}x_{8}+c_{9}\dot{ E}x_{9}\) & \(c_{7}=c_{8}\) \\ Steam turbine & \(c_{9}\dot{E}x_{9}+\dot{Z}_{st}=c_{el,gt}\dot{w}_{st}+c_{10}\dot{E}x_{10}\) & \(c_{9}=c_{10}\) \\ Condenser & \(c_{10}\dot{E}x_{10}+c_{13}\dot{E}x_{13}+\dot{Z}_{st,cond}=c_{11}\dot{E}x_{11}+c _{14}\dot{E}x_{14}\) & \(c_{10}=c_{11}\) \\ Pump & \(c_{11}\dot{E}x_{11}+c_{el,st}\dot{W}_{p}+\dot{Z}_{p}=c_{12}\dot{E}x_{12}\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cost balance and auxiliary equations of combined cycle system shown in Figure 3.  
where \(\dot{C}_{env}\) is obtained from the following relation [1]:  
\[\dot{C}_{env}=c_{CO2}\dot{m}_{CO2}+c_{NO}\dot{m}_{NO}+c_{CO}\dot{m}_{CO} \tag{26}\]  
Which is \(c_{CO2}\) = 0.024 \(\$\)/kg, \(c_{NO}\) = 8.175 \(\$\)/kg and \(c_{CO}\) = 6.242 \(\$\)/kg [28; 29]. In addition, the unit cost of electricity generated is calculated from the following relation:  
\[c_{el,tot}=\frac{\dot{C}_{el,gt,1,2}+\dot{C}_{el,st}}{\dot{W}_{net}} \tag{27}\]
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 2 Problem Statement
### Impacts of Subset Size on Accuracy and Integrity Performances
#### 2.2.3 Computation Load Increased by Subset Size Growth  
It is commonly known that the acquisition and tracking of the signal in space play an important role in the processing burden of the GNSS receiver. This burden grows proportionally as the satellites in use increase. Besides this, the computation load for RAIM process is discussed in this section.  
If the weighted least-squares estimation algorithm is adopted to calculate the receiver position and clock offset, the estimation of the states vector can be expressed as Equation (2) and the HPL of the weighted RAIM process can be obtained by Equation (8). The amount of calculation for positioning and integrity monitoring can be given in Table 2. The multiplication and addition times for the inverse operation of \(G^{T}WG\) only depend on the state dimension \(p\), thus the time complexity of the RAIM process is positively associated  
Figure 2: Minimal HPL value and HPL reduction to the former for each size subset. (**a**) In the ideal scene; (**b**) In the practical scene.  
with the square of the satellite number \(n\). When the satellite number increases, the amount of extra operations for RAIM process increases more quickly than that for acquisition and tracking processes. Consequently, choosing a relatively small subset that meets the accuracy and integrity requirements is an efficient way to improve the real-time processing capability of the navigation processor.  
Usually, the satellite selection method assigns a fixed subset size to maintain real-time processing capability. For example, the subset size is set to 8 for the dual-constellation navigation system and 10 for triple-constellation in [38], while 12 for the dual-constellation navigation system in [31; 39], and 18 for the quadruple-constellation navigation system in [23]. However, the optimal performance of the same size subset varies with the satellite geometry which depends on the receiver location and time. Even in a specified location, the minimal geometric dilution of precision (GDOP) value of the 6 satellites subset can vary from 1.7 to 3.1 during 24 h as illustrated in [24], and the optimal HPL value of the 12 satellites subset varies between 13 and 20 m during 24 h in [31]. While maintaining reliable accuracy and integrity performances under the requirement, the minimal size of the usable subset will also not be a fixed number but vary with the satellite geometry and the satellite selection algorithm. The minimal subset size could be an indicator to evaluate the performance of the satellite selection strategy when we focus on the real-time processing capability of the navigation system.
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 3 Methodology and the System Design Procedure
### Security Analysis of Chaotic Carrier Data Transmission System with Parameters Nonlinear Modulation
#### 3.3.2 Fourier Series Analysis  
Fourier series analysis allowed us to demonstrate the non-obvious frequency of the signal. This is so because the Fourier series result is the signal frequency spectrum. For extra analysis, we used Savitzky-Golay filtering. The red lines in Figures 9 and 10 show the filtered signal.  
Here, according to Figure 9a,b, it is also impossible to find significant differences between the results for transmission with a useful signal and without a useful signal. However, the analysis of the non-deterministic random signal (Figure 10) revealed a significant difference in the shape of the signal from the deterministic chaotic one (Figure 9). The latter makes it possible to identify the fact of transmitting a useful signal or the fact of the presence of a useful signal generator in the data transmission medium (even in idle mode transmission). Thus a steganography property, in this case, could not be provided. However, in real systems, there always exist some deterministic signals (interference from other communication channels, etc.). This means it is virtually impossible to obtain an ideal non-deterministic noise in the communication channel in the absence of transmission of a useful signal.  
Figure 8: Correlogram of a random signal.  
Figure 7: Correlograms of \(Z(t)\): (**a**) idle transmission (without useful signal); and (**b**) transmission with useful signal (nonlinear, multiplicative modulation).  
Thus, the above analysis showed that the multiplicative modulation of a chaotic carrier does not allow us to separate the useful signal from the carrier and does not even allow us to establish the fact of transmission for an external listener. The above analysis using autocorrelation and Fourier series is the simplest analysis. The problem requires other solutions, however, the result above allows us to conclude that the used approach is viable for data protection.  
In the following sections, we considered the procedure for reconstructing the useful signal on the receiver side using the methods and approaches of synergetic control theory [38; 39; 40; 41].
## 3 Data Coverage and Method  
### Data Coverage and InSAR Processing  
Here we use the SAR data from the L-band JERS, ALOS, and the C-band ENVISAT satellites to map the line-of-sight (LOS) surface deformation. There are three kinds of SAR images, including 42 ALOS1 images acquired from two parallel ascending-orbit tracks from December 2006 to July 2010, 22 JERS images acquired from October1992 to September 1998, and 23 ENVISAT images acquired from October 2003 to May 2007 (See Figure 1 and Table S1-S3). Those InSAR datasets provide deformation of a 200 \(\times\) 100 km area covering the whole peninsula (see Figure 1). Detailed SAR data information is shown in Table S1-S3.  
These SAR data are processed by the conventional two-pass differential interferometry methods of GAMMA software package [17]. We applied a multilook operation of 6 \(\times\) 16 pixels for the ALOS1, JERS data and 4 \(\times\) 20 for the ENVISAT data in the range and azimuth directions to reduce the phase noise as well as calculation burdens before the phase unwrapping. Using the 90-m resolution SRTM v4.2 digital elevation model, we removed the phase relating to topography. The refined ESA VOR orbits were used to remove the phase ramps for the ENVISAT acquisitions. However, there was no precise orbit for the JERS or ALOS1 data. So we adopted a refined method based on fringe frequency to correct the phase ramp in differential interferograms [18]. Before phase unwrapping, a modified Goldstein filter was used to reduce noise phases [19] and a manual mask applied to eliminate its impact on phase unwrapping from some low coherence areas such as isolated islands and sea areas. In some studies, ionospheric disturbance error of SAR interferogram also needed to be considered [20; 21], but we did not find any SAR images contaminated by ionospheric disturbances in this study. Finally, we used minimum cost flow algorithm to unwrap pixels whose coherence value was over a threshold (0.7).  
Combination of ascending and descending modes can be used to separate vertical and horizontal ground motions. However, here only the ascending ALOS1 or the descending JERS and ENVISAT acquisitions can be used in every time span. Thus vertical and horizontal components of the deformation are impossible to be retrieved independently [22]. Our results show that most ground displacement rates are <30 mm/year and the vertical subsidence is the main deformation component [23]. Thus, we assumed that the horizontal ground displacement is negligible and projected the results in LOS direction to vertical direction by dividing by the cosine of the incidence angles of each platform.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 4 Steam Leak Detection
### IIoT Module  
#### 4.2.1 Wireless Protocol Comparison  
In this subsection, we present the technology approach for the wireless IIoT device.  
One of the most important things to consider in the wireless sensor networks is the wireless protocol selected. This selection will determine, mainly:  
* Node energy consumption.
* Maximum range and the maximum number of nodes connected to the same gateway/repeater.
* Data rate and latency.
* Network costs (licensed or unlicensed bands) and infrastructure costs.
* Node data security encryption.  
According to 3 GPP and GSMA ([PERSON] et al. [16]), the LoRaWAN protocol is one of the most likely to be adopted in different Low-Power Wide-Area Networks (LPWANs) (Table 1) due to its robustness, range, and ease of scaling.  
For our project purposes, the LoRaWAN chipset from the microchip manufacturer RN2483 has been selected [17].
# The Cytotoxic Activity of Diiron Bis-Cyclopentadienyl Complexes with Bridging C\({}_{3}\)-Ligands
###### Abstract  
Diiron bis-cyclopentadienyl bis-carbonyl calic complexes with a bridging vinylinium ligand, [Fe\({}_{2}\)C(O)(\(\mu\)+CO)[\(\mu\)+n\({}^{1}\),\({}^{3}\)-C\({}^{3}\)(R\({}^{\prime}\))C\({}^{2}\)H\({}^{\rm C}\)\({}^{1}\)NMe(R\({}^{\prime\prime}\))][Cr\({}_{3}\)SO\({}_{3}\) (R = Xyl = 2,6\(\cdot\)C\({}_{6}\)H\({}_{3}\)Me\({}_{2}\), R\({}^{\prime}\) = Ph, R\({}^{\prime\prime}\) = H, **2a**; R = Xyl, R\({}^{\prime}\) = R\({}^{\prime\prime}\) = Me, **2b**; R = R\({}^{\prime}\) = H, **2c**; R = Me, R\({}^{\prime}\) = 2-naphthyl, R\({}^{\prime\prime}\) = H, **2d**; R = Me, R\({}^{\prime}\) = R\({}^{\prime\prime}\) = Ph, **2e**), are easily available from commercial chemicals, robust in aqueous media and exert a variable in vitro cytotoxicity against cancer cell lines depending on the nature of the substituents on the vinyliminium ligand. The anticancer activity is, at least in part, associated to fragmentation reactions, leading to iron oxidation and active neutral and well-defined mononiron species. We report an innovative synthetic procedure for the preparation of **2a,c,d**, and a facile method to access the mononior derivative of **2a,** i.e., [FeCp(CO)][C\({}^{1}\)(NMeXyl)C\({}^{2}\)HC\({}^{3}\)(Ph)C(O)] [**3a**]. According to IC\({}_{50}\) analyses at different times of incubation of the complexes, **3a** is significantly faster in inhibiting cell viability compared to its diron precursor **2a**. The neutral complexes [Fe\({}_{2}\)C\({}_{2}\)C(O)(\(\mu\)+CO)[\(\mu\)+N\({}^{1}\)N:k\({}^{1}\)C:k\({}^{1}\)C:k\({}^{1}\)C-C\({}^{3}\)(R\({}^{\prime}\))C\({}^{2}\)(Se)C\({}^{1}\)(NMe\({}_{2}\))C\({}^{4}\)(CO\({}_{2}\)Y)C\({}^{5}\)(CO\({}_{2}\)Y)] [R\({}^{\prime}\) = Y = Me, **4a**; R\({}^{\prime}\) = Pr, Y = **1**; **4b**; R\({}^{\prime}\) = Y = Et, **4c**) are obtained via the two-step modification of the vinyliminium moiety and comprise a bridging selenophene-decorated alkylidene ligand. The antiproliferative activity exhibited by **4a-c** is moderate but comparable on the ovarian cancer cell line A2780 and the corresponding cisplatin resistant cell line, A2780 cisR. Complexes **4a-c** in aqueous solutions undergo progressive release of the alkylidene ligand as a functionalized selenophene, this process being slower in cell culture medium. Since the released selenophenes ScC\({}^{1}\)C(O)R\({}^{\prime}\)C\({}^{2}\)(NMe\({}_{2}\))C\({}^{3}\)(CO\({}_{2}\)Y)C\({}^{4}\)(CO\({}_{2}\)Y) (R\({}^{\prime}\) = Y = Me, **5a**; R\({}^{\prime}\) = Pr, Y = **1**; **5b**) are substantially not cytotoxic, it is presumable that the activity of **4a-c** is largely ascribable to the [Fe\({}_{2}\)C\({}_{2}\)C\({}_{2}\)] scaffold.  
metal-based drugs; diron complexes; cytotoxicity; alkylidene ligand; selenophenes