esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Digital twins of bridge structures are updated with real-time sensor data for predictive maintenance and structural evaluations with unmatched precision [178]. Large-scale bridges are essential for social and economic development. If they are not adequately monitored and maintained, they can be subject to precarious breakdowns and catastrophes. The causes of such failures can be complex, including safety issues, material degradation, corrosion, structural damage, cracks on the surface and in the sub-surface, performance ignoming, functional failure, and several other damages or diseases induced by load, erosion, and natural disasters. All these listed bottlenecks, damages, and faults affect bridges' regular and normal functioning and reduce the safety parameters that can pose paramount security risks to the bridge itself and the surrounding environment. This makes checking the bridge's structural health mandatory in real-time. Visual inspection has several restrictions, which are time- and skill-dependent. Therefore, conducting SHM with the help of technology is a promising option that can help assess civil infrastructures. SHM primarily depends on three basic steps. The initial step is the close monitoring of structures through the information collected by the sensors, which is followed by extracting the damaged features and finally analysing the damaged features to evaluate the actual condition of the structures. This is why many new bridges are installed with real-time SHM systems. A large amount of data related to the management and maintenance of bridges accumulate, which is otherwise difficult to manage. Techniques like data processing, computing algorithms, and Machine Learning tools used for data analysis have smoothed the data analysis path and incorporated processes like data pre-processing, data fusion, pattern recognition, and data visualisation.
## 4 Simulation Results
#### 4.1.2 Tracking Performance Analysis  
To measure the estimation accuracy of the proposed algorithm, we introduce the root mean squared error (_RMSE_ ) in the position and its mean _RMSE_ for different SNRs. Additionally, the _RMSE_ for \(M\) simulations with different frames at time \(k\) is defined as:  
\[RMSE=\left[\frac{1}{M}\sum_{m=1}^{M}\left(\left(\hat{x}_{k,m}-x_{k,m}\right)^{ 2}+\left(\hat{y}_{k,m}-y_{k,m}\right)^{2}\right)\right]^{\
icefrac{  
The results in terms of mean RMSE for the different SNR values of the three algorithms are compared in Table 2. Figure 4 shows the results in terms of RMSE in position with corresponding frames for different SNR values.  
As expected, the tracking accuracy of each algorithm degrades as the SNR reduces. Meanwhile, the proposed algorithm shows a better performance on the low SNR target than the other two algorithms in Figure 4. It is can be seen that the proposed algorithm has much lower RMSE when the target began to appear. It is demonstrated that the HDE PF-TBD algorithm can more accurately estimate the initial location of the target appearance. Moreover, from Table 2, it can be clearly seen that the estimation  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **9 dB** & **6 dB** & **3 dB** & **1 dB** \\ \hline MultinomialR PF-TBD & 2.1757 & 2.4715 & 2.7528 & 3.0814 \\ SystematicR PF-TBD & 1.9771 & 2.3427 & 2.6222 & 3.0622 \\ HDE PF-TBD & 1.3810 & 1.5971 & 1.8221 & 2.3595 \\ \hline \end{tabular}
\end{table}
Table 2: The average of the RMSE for different SNRs for the CV model.  
Figure 4: **(a)** RMSE in position at SNR = 9 dB for the CV model; **(b)** RMSE in position at SNR = 6 dB for the CV model; **(c)** RMSE in position at SNR = 3 dB for the CV model; **(d)** RMSE in the position of existence at SNR = 1 dB for the CV model.  
accuracy of the proposed algorithm is always superior to that of the other two algorithms. Although the RMSE of the proposed method approximates the other two algorithms at Frames 16 and 17 when the SNR is 1 dB, the tracking accuracy of the HDE PF-TBD algorithm is still higher than the other two algorithms in the whole frame range where the target exists. Therefore, the proposed algorithm is capable of tracking the target with low SNR, and it outperforms the SystematicR PF-TBD and the MultinomialR PF-TBD.
## 2 Methods
### Numerical Simulation
#### 2.3.1 Modeling the Flow Field  
The sprayed biocide aerosol has a micro-sized diameter. The micro-sized aerosol is dependent on the flow field [26]. In the indoor air flow, a complex three-dimensional turbulent flow is generated. Therefore, it is important to capture the turbulence flow in indoor spaces in order to investigate the biocide aerosol behavior. For numerical simulations of turbulent behavior, the 3D Reynolds average Navier-Stokes (RANS) equation was solved based on the finite volume method. The RANS equation is as follows:  
\[\frac{\partial\rho}{\partial t}\ +\ \frac{\rho\partial\overline{u}_{i}}{ \partial x_{j}}\ =\ 0 \tag{5}\]  
\[\frac{\partial\overline{u}_{i}}{\partial t}\ +\ \overline{u}_{j}\frac{ \partial\overline{u}_{i}}{\partial x_{j}}\ =\ -\frac{1}{\rho}\frac{\partial\overline{p}}{ \partial x_{i}}\ +\ \frac{\partial^{2}\overline{u}_{i}}{\partial x_{j}\partial x_{j}}\ -\ \frac{ \partial}{\partial x_{j}}\tau_{ij} \tag{6}\]  
where the Reynolds stress tensor \(\tau_{ij}\) can be defined as:  
\[\tau_{ij}=\ \overline{u_{i}^{\prime}u_{j}} \tag{7}\]  
Here, \(\overline{u}_{i}\) and \(u_{i}^{\prime}\) are the mean velocity and fluctuating velocity, respectively, and \(\overline{p}\) is the pressure of fluid. The Reynolds stress tensor due to turbulence behavior is solved by adopting turbulence models, such as \(k-\varepsilon,k-\varepsilon-\text{RNG}\), Reynold stress model (RSM), etc. Since the flow field is different depending on the turbulence models, an appropriate turbulence model should be used [25; 26; 27].  
It has been proven that the \(k-\varepsilon\), \(k-\varepsilon-\text{RNG}\) that assumes the Boussinesq eddy viscosity can capture the flow circulating in an indoor chamber relatively better than the RSM model. The \(k-\varepsilon-\text{RNG}\) showed slightly better results than the \(k-\varepsilon\) model [14]. The RSM assuming the Reynolds stress tensor with three-dimensional anisotropic property predicted strong circulating flow. Therefore, in this work, we use the \(k-\varepsilon-\text{RNG}\) model for evaluating the feasibility of the CFD approach for the inhalation exposure assessment  
Figure 3: Experiment setup, measurement positions and chamber dimensions. The experiment was performed in a ventilation chamber of 30 m\({}^{3}\).  
in this study. The equation of the \(k-\varepsilon-\text{RNG}\) model is well described in the following references [27; 28].
# An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software
## 3 System Implementation  
The CIM architecture integrates the processes involved in the automation of industrial production, from the manufacturing of the product to the level of business management. The CIM model consists of five levels: (1) the field level, (2) process level, (3) level of supervision, (4) management level and (5) the company level [12].  
The architecture proposed in this work involves the first three levels of the CIM pyramid model. Figure 2 shows the proposed automation system architecture for the multi-tank system. The field level consists of ultrasonic sensors, flow sensors, a hydraulic pump and three solenoid valves. The process level consists of two low-cost embedded PLCs: (i) PiXtend based on the Raspberry Pi platform, which implements the control and fault detection algorithms in the system, and (ii) M-Duino based on the Arduino platform, which preprocesses the signals from the sensors. This configuration allows the demonstration of the integration of different platforms (heterogeneity) within the same system. The supervisory level is made up of an Internet of Things (IoT) local server installed in a Raspberry Pi connected to the local Wi-Fi router via an Ethernet cable, such that only the devices connected to the network--either wired or wireless--can access the data hosted on the thinger.io local server. It is important to mention that both PLCs, _M-Duino_ and PiXtend, are CE certified and have compliance with the IEC61131 standard. These features offer robustness in industrial applications.  
Details regarding the implementation of each level are given below.  
Figure 1: P&ID diagram of the multi-tank system.
## 4 Results and Discussion  
The results of the study are provided by three approaches. Firstly, the individual performances of the stages are provided. Secondly, the end-to-end evaluation is presented. Lastly, the performance comparison between HieD and one of the state-of-art networks, YOLOv4, is provided. We separated the dataset for training, validation, and testing. While 80% of the VHRShips dataset are kept for the training and validation (64% training and 16% validation), 20% of the dataset is used for testing. In summary, 4016 images are reserved for the training, 1021 images are reserved for validation, and 1275 images are reserved for testing. The images for the training, validation, and testing sets are selected considering the equal class distribution among the sets. There are 200 non-ship images in the testing set, and 1075 images with 2176 marked ships, 142 of which are navy ships.
## 2 Background  
Object clustering aims at grouping similar objects into the same cluster. Previous clustering techniques fell into two types: _hard_ and _soft_ clustering. Hard clustering aims at grouping an object into a cluster, while soft clustering (also known as _fuzzy_ clustering) aims at assigning different degrees of membership of an object to all clusters (for a survey of fuzzy clustering techniques, the readers are referred to [4]). By setting thresholds on the degrees of membership, fuzzy clustering techniques can achieve _overlapping_ clustering (i.e., partitioning objects into non-exclusive clusters). Fuzzy C-means (FCM) is a representative fuzzy clustering technique [4]. It worked on a similarity measure based on the distance (e.g., Euclidean distance) between objects and cluster means in a vector space [5]. For those domains in which good inter-object similarity measures have been developed, fuzzy C-medoids (FCMdd) was developed to employ these domain-specific similarity measures to improve overlapping clustering. FCMdd works to find a fixed number of representative _data objects_ (i.e., _medoids_, rather than cluster means) for clusters, with each of the other data objects having a degree of membership to each cluster, so that the sum of within-cluster dissimilarities is minimized (i.e., within-cluster similarities is maximized [6; 7]).  
Figure 1: Main idea of _issue-based clustering_ of scholarly article.  
The clustering of scholarly articles is a domain in which many good inter-article similarity measures have been developed, and hence, many previous studies employed these similarity measures to cluster scholarly articles. State-of-the-art inter-article similarity measures worked on two types of contents: (1) _citations_ among articles, and (2) the _main contents_ of articles (e.g., titles, abstracts, and main texts). The former was based on the expectation that two articles that share certain citations may be similar to each other, while the latter was based on the expectation that two articles that have similar main contents may be similar. The technical contribution of ICRT can thus be highlighted by explaining how these previous similarity measures have weaknesses in issue-based article clustering.
## 2 Structure and Design
### Optimisation  
For optimisation, the following parameters were chosen: _Lpatch_ = _Wpatch_, _lshort_, and _Apatch_. Initially, _Lpatch_ equalled \(\lambda_{0}/2\) and _lshort_ equalled \(\lambda_{g}/4\), as explained before. _Apatch_ initially equalled \(drod/2\) (to keep the rod inside the patch) plus a small displacement; in this case, a displacement of 1 mm was chosen (\(Apatch=2.5\) mm). In [24], the initial position with respect to the edge for feeding the patch antenna is explained. The impedance (\(Z_{patch}\)) and bandwidth depend on the shape factor (which is 1 in this case, as \(L_{patch}=W_{patch}\)) and the feeding point, denoted as \(A_{patch}\). Variations in the cavity volume alter the total amount of energy that can be stored between the patch and the ground plane.  
The chosen algorithm was the Trust Region Algorithm, with the goals of maximising the resonance peak at the operating frequency (10 GHz) and achieving a 20% bandwidth. In order to manufacture the prototype, the value of _hair_ was discretised using substrate heights available in the laboratory. Therefore, \(h_{air}=\sum_{i=1}^{n}(n_{1}\cdot h_{1}+n_{2}\cdot h_{2}\ldots n_{n}\cdot h_{n})\), where \(n_{i}\) is the number of stacked substrates and \(h_{i}\) the heights of them. Initially, an arbitrary value of N was selected in order to compute \(h_{air}\) (see Figure 3). Then, the design parameters (\(A_{patch}\), \(L_{patch}\), and \(L_{short}\)) were optimised to minimise the reflection coefficient at the operating frequency while covering the required bandwidth. If the resulting bandwidth was insufficient, the value of N was increased (by adding a layer), and the design procedure was repeated--applying the same optimisation goal--until \(h_{air}\) was sufficient to cover the required bandwidth (variations in efficiency are negligible with different values of _hair_). Finally, the value _hair_ = 2.439 mm was chosen because it is an integer multiple of 0.813 mm (the height of the Rogers 4003C substrate), which facilitates the subsequent assembly of the patch, provides the required bandwidth, and acceptable values of bandwidth and gain. All the final parameters after the different optimisations mentioned above are summarised in Table 1. CST Studio Suite 2023 was the software used to optimise them. This device provides a 20% bandwidth, 8.2 dBi gain, and radiation efficiency over 90% in simulation.
# Spectrogram Classification Using Dissimilarity Space
## 1 Introduction
In theory, all the training samples can be selected as centroids of the dissimilarity space. Dimensionality reduction is obtained by selecting a smaller number (\(k\)) of prototypes via a clustering approach. The dissimilarity space is the space where each spectogram is represented by a its distance to each centroid/prototype: in this space, the SNN is used to compare the spectrogram to every centroid, obtaining the spectrogram's dissimilarity vector, which is the final descriptor. The classification task is performed by a support vector machine (SVM) trained using the dissimilarity descriptors generated from the training samples. The proposed system is evaluated on two different datasets for animal audio classification: domestic cat sounds ([PERSON] et al., 2016) and bird sounds ([PERSON] et al., 2016). Results for the different clustering methods and different values of the hyperparameter (\(k\)) are reported. In addition, an ensemble of SVMs trained on different dissimilarity spaces (by changing the value of \(k\)) are combined by sum rule, and its performance is compared with (i) some canonical CNN approaches and (ii) the fusion of the SVMs and the CNNs. Experiments demonstrate for the first time that the use of dissimilarity spaces based on SNN is a feasible representation for image data and can, when combined with a general purpose classifier, achieve high classification performance. Because the descriptors obtained in the dissimilarity space show high diversity with respect to the representations based on CNNs, their fusion can be exploited in an ensemble, as proven by the high classification accuracy obtained by the fusion of CNNs with our approach. The MATLAB code used in this study is freely available at [[https://github.com/[PERSON]](https://github.com/[PERSON])]([https://github.com/[PERSON]](https://github.com/[PERSON])).
# Defect Prediction for Capacitive Equipment in Power System
## 3 Method
### Random Forest  
Random forest (RF) is a robust machine learning algorithm that combines multiple decision trees through the idea of ensemble learning [30]. RF is a particular form of the bootstrap aggregating (Bagging) method. Compared with Bagging, it exhibits three key distinctions:  
* RF employs classification and regression tree (CART) algorithms as its constituent weak learners;
* RF randomly selects features every time;
* The number of samples selected by RF is identical to that of the training set. Due to its randomness, it can reduce the variance of the model. Therefore, RF exhibits superior generalization and antioverfitting capabilities compared to Bagging [31]. RF boasts rapid training speed, parallelizability, and high efficiency in processing large-scale data.
## 3 Results and Discussion
### Compressive Strength
#### 3.2.2 Wet Compressive Strength  
The influence of fiber content for different cement ratios on the wet compressive strength (WCS) is shown in Figure 16. It can be observed that the incorporation of fibers  
Figure 15: Result of dry compressive strength according to the fiber content.  
to the composite decreases its WCS. The lowest value corresponds to the sample with a fiber content of 0.2% and a cement content of 4%, while the highest one was obtained for the sample with 12% cement without fibers. It can be noticed that the reduction in WCS for all earth blocks ranged between 5% and 25% and the values of WCS in non-filled samples were 4.63 MPa, 6.00 MPa and 7.63 MPa for 4%, 8% and 12% of cement, respectively. Moreover, the average difference between dry strength and wet strength is about 58% for 4% cement, 54% for 8% cement and 50% for 12% cement, which indicates that the increase in cement content leads to a reduction in the fractional difference between DCS and WCS. The obtained results confirm the highlights of other studies which noted that the most economical way to increase the WCS of blocks is to increase the stabilizer content [12; 55]. This finding is in agreement with a previous study from the literature on the influence of cement on the WCS of CEBs reinforced with date palm fibers. The same trend was reported by [PERSON] et al. [12]. They reported in their research work that WCS reached average values of 4.5 MPa, 6.31 MPa, and 8.5 MPa for 5%, 6,5%, and 8% cement contents, respectively. This result is also in agreement with the result of [PERSON] et al. They highlighted that the increase in cement content from 4% to 12% led to an increase in WCS from 1.30 MPa to 2.25 MPa, and that the highest value of 2.85 MPa corresponded to 20% cement stabilization for the unfilled CEBs [25]. Figure 17 shows that the highest (DCS/WCS) ratio, equal to 1.62, corresponds to the 4% cement and 0.15% fibers. In addition, it has been recommended by [PERSON] et al. that the DCS/WCS ratio should be less than 2 [54]. The findings of this work are well within this limit.
## 6 Systematic Differences Between the LT-1 and Sentinel-1 Satellites  
### Comparison of Orbit State Vector  
For the LT-1 satellite, without the precision orbit file, we can estimate the perpendicular baseline using the frequency information of the interferogram. Fast Fourier Transformation (FFT) techniques were applied to shift it to zero frequency without assistance from the Ground Control Points (GCPs) and accuracy DEM. In this experiment, we obtained the frequency information of the LT-1 and Sentinel-1 satellites, as well as the precision orbits of both satellites. Figure 11 shows the track of SAR satellites based on the orbit state vector in the images acquired over the study area.  
After importing the precision orbits, the orbital trajectories of both Sentinel-1 and LT-1 satellites are relatively stable when observing the study area at different image acquisition dates. By comparing their orbital precision, it can be observed that the orbiting precision of Sentinel-1A data, with auxiliary orbital details, is better than 5 cm, and the SD is 0.38. In contrast, the LT-1 data have an orbiting precision better than 5 cm, with an SD of 0.23. Both satellites demonstrate commendable orbit precision; however, the superior D in LT-1's orbital data suggests a more stable and reliable orbit. The comparative analysis of LT-1 and Sentinel-1A data serves to evaluate the efficacy and reliability of LT-1 in the context of mining-induced subsidence monitoring, providing valuable insights that can inform future applications and enhance our understanding of this important field of study.
## 3 System Design
### Control Program Design of Variable Spray System  
The program was utilized to control the variable spray system and process various signals. It was written in Keil Software (ARM Germany GmbH, USA), which was mainly composed of the PWM duty cycle adjustment module, serial communication module, LCD module and PID control module. The program control flow chart is shown in Figure 10. When the system works, the GPS communication protocol (NMEA0183) is used to analyze the information acquired by the GPS in real time [21], so the position information of the current plant protection drone is obtained. Then, the prescription value is extracted through the prescription graphic translation system which processes the information. The prescription values are sent to the variable spray system. When the variable spray system receives the prescription value information, the timing and counter are turned on. The number of pulses of the flow sensor is fed back every 50 ms, and the current system instantaneous flow is obtained through calculation. The deviation between the instantaneous flow rate and the target flow of the system is used as the input of the PID controller. After the PID control algorithm is operated, a duty value is output, and the variable spray controller generates a PWM square wave with the corresponding duty ratio.