esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Discussion of Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Architecture** & **F1-Score (\%)** & **Accuracy (\%)** & **CPU (s)** & **GPU (s)** \\ \hline ResNet50 & 92.16 & 94.12 & 0.27 (\(+/-0.25\)) & 0.20 (\(+/-0.25\)) \\ VGG16 & **93.73** & **95.59** & **0.21 (\(+/-0.07\))** & **0.14 (\(+/-0.10\))** \\ Xception & 80.98 & 88.24 & 0.23 (\(+/-0.08\)) & 0.15 (\(+/-0.12\)) \\ Inception-V3 & 82.01 & 88.24 & 0.28 (\(+/-0.18\)) & 0.18 (\(+/-0.17\)) \\ MobileNet-V1 & 86.51 & 91.18 & **0.21 (\(+/-0.14\))** & 0.18 (\(+/-0.19\)) \\ MobileNet-V2 & 48.78 & 76.47 & 0.25 (\(+/-0.19\)) & 0.18 (\(+/-0.15\)) \\ NasNetLarge & 81.20 & 83.82 & 0.68 (\(+/-0.44\)) & 0.32 (\(+/-0.43\)) \\ Inception-ResNet-V2 & 76.22 & 85.29 & 0.36 (\(+/-0.17\)) & 0.22 (\(+/-0.27\)) \\ ResNet152v2 & 71.22 & 83.82 & 0.34 (\(+/-0.15\)) & 0.19 (\(+/-0.22\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results using the fine-tuning strategy over the nine selected architectures, using the F1-score and accuracy. The time indicates the average computational cost per individual image. Bold highlights the best results. Figure 3: Accuracy (**a**) and F1-score (**b**) comparison of the results obtained in our proposed pipeline using transfer learning and fine-tuning on CRINF-300.
# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
## 3 Transformer Fault Diagnosis Method
### DGA Sample Dataset  
When a discharge or overheating fault occurs inside the power transformer, the transformer oil pyrolyzes, producing hydrogen (H\({}_{2}\)), methane (CH\({}_{4}\)) and other hydrocarbon gases [26]. According to IEC 60599 [11], transformer fault diagnosis results based on DGA are mainly classified into normal status (N), thermal faults and electrical faults. Distinguished by the severity of the fault, overheating faults can be divided into T1 (\(t\) < 300 \({}^{\circ}\)C), T2 (300 \({}^{\circ}\)C \(<t\) < 700 \({}^{\circ}\)C) and T3 (\(t\) > 700 \({}^{\circ}\)C), and electrical faults include PD (corona partial discharges), D1 (discharges of low energy) and D2 (discharges of high energy). Three hundred and seventy-nine transformer failure cases were collected from a DGA sample dataset, of which the sample distribution is shown in Figure 2.  
The abnormal values in the DGA samples have a great impact on the fault diagnosis results. The statistical analysis of the DGA sample data is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Value** & **H\({}_{2}\)** & **CH\({}_{4}\)** & **C\({}_{2}\)H\({}_{6}\)** & **C\({}_{2}\)H\({}_{4}\)** & **C\({}_{2}\)H\({}_{2}\)** \\ \hline mean & 222.38 & 122.25 & 73.14 & 164.26 & 29.00 \\ standard & 453.83 & 298.97 & 281.79 & 359.65 & 76.18 \\ minimum & 0 & 0 & 0 & 0 & 0 \\
1 st quartile & 18.50 & 10.90 & 2.48 & 2.60 & 0 \\
2 nd quartile & 72.20 & 43.00 & 17.80 & 30.00 & 0.30 \\
3 rd quartile & 191.46 & 136.60 & 54.00 & 147.05 & 13.75 \\ maximum & 3433.00 & 4992.00 & 4836.00 & 3671.00 & 765.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics Analysis of DGA Samples.  
Figure 2: Distribution of transformer fault samples.  
The differences in the mean, standard and even maximum values of the different gas contents in the samples are significant. In order to reduce the influence of numerical differences on the diagnosis results, the paper normalized the gas content by (4).  
\[x_{ij}^{*}=\frac{x_{ij}}{\sum\limits_{k=1}^{5}x_{ik}} \tag{4}\]  
where \(x_{ij}\) is the \(j\)-th gas content in the \(i\)-th DGA sample.  
In order to improve the accuracy of transformer diagnostic results, this paper performs feature derivation of DGA gases. The five gas contents of H\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{6}\), C\({}_{2}\)H\({}_{4}\) and C\({}_{2}\)H\({}_{2}\) are expanded to 16 features, as shown in Table 2. The features derived in this paper mainly include three categories: gas content, three basic gas ratios and other gas ratios [11]. Among them, TH represents total hydrocarbon, \(M(\cdot)\) represents gas content, C(\(\cdot\)) represents three gas ratios, and \(K(\cdot)\) represents other gas ratios adopted.
## 4 Results and Discussion
### Using Laboratory Results to Predict Parameters Found for Field Tests (at 250% Scale)
#### Assessing the Effect of Employing Half-Symmetry  
Additional numerical simulations were conducted to determine the effect of this half-symmetry assumption on loading at each pressure gauge and to understand the validity of comparing experimental results to the large-scale (250%) field tests in [11]. CFD models for the 100% scale setup (employing half symmetry) were undertaken to examine loading at each location in comparison to a larger obstacle with a 50% longer side length, akin to [11] (Figure 10).  
Figure 10: CFD models assessed the influence of employing half symmetry for experimental corner geometry in comparison to the larger geometry used in [11].  
Figure 9: Schematic of the experimental set-up (**a**) and field test photographs (**b,c**) used in [11].  
CFD models of the experimental geometry showed that blast waves diffracted around the rear of the corner structure (anti-clockwise in Figure 10), reaching gauge B3 towards the end of the positive phase duration of the primary blast wave. This resulted in a slightly elongated positive phase duration (by 0.17 ms) and increased specific impulse (by 0.9 kPa.ms) at gauge B3 in comparison to models of the larger geometry (Figure 11). However, no difference was observed for pressure measurements at gauges A, B1, and B2 (Figure 11). Employing half symmetry for the corner geometry in the experiments and comparing it to the geometrically equivalent results in [11] was therefore deemed acceptable (Figure 12).  
Figure 11: Numerically modelled pressure histories for the 100% scale experimental geometry (employing half symmetry) and “larger geometry” akin to Gajewski and Sielicki [11] (dashed curves).  
Figure 12: Plan view of pressure contours at height z = 0.54 m for a 100% scale CFD model showing blast wave diffraction around the experimental corner structure at two-time points.
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 5 Experimental Design and Results
### Results and Analysis of the DMCNet  
This study compares the results between the 8-pilot and 32-pilot scenarios. As shown in Figure 11, it can be observed that in the eight-pilot scenario, with fewer pilots, the traditional approach struggles to utilize pilot data for channel information recovery and signal detection. Therefore, the BER performance saturates and is the worst when the SNR exceeds 15. On the other hand, in the deep-learning-based approach, both fully connected and convolutional networks show continuous BER reductions as the SNR increases. The conclusion drawn from this is that the neural-network-based approach exhibits stronger robustness regardless of the number of pilots, while the traditional approach heavily relies on pilot data for accurate channel estimation. The superiority of neural networks in this aspect mainly stems from their ability to approximate any function, and the characteristics of wireless channels can be seen as complex functions that can be learned from training data and network models.  
In the case of 32 pilots, the traditional LMMSE-MMSE receiver can leverage more pilots to recover channel information, and the performance of the neural network algorithm is comparable to the traditional method.  
Comparing the fully connected and convolutional receiver schemes, it can be observed that the convolutional receiver outperforms the fully connected receiver. In the single-antenna OFDM scenario, where the input-output signal length is short, the fully connected network can approximate the characteristics of the channel. However, in the multi-antenna scenario, with longer input signal lengths and where the channel state is represented as a multidimensional matrix, the operation of the input signal through the channel becomes more complex and the limitations of the fully connected network operation are insufficient to fit the channel model. The introduction of convolutional modules and residual modules in DMCNet enriches the network's operation and allows for a better fit to the channel model, resulting in a lower BER compared to the fully connected approach.  
Furthermore, DMCNet does not require input data segmentation, and its output length is 1024 bits, allowing for the reception of the entire input signal through a single network. In terms of the parameter count, in the eight-pilot scenario, DMCNet has a parameter count of 4,202,496, while the fully connected network has a parameter count of 7,348,224 \(\times\) 64. The parameter count of DMCNet is only 9% of the fully connected network, making it more suitable for practical deployment and usage.  
Based on the experimental results, it can be concluded that the proposed DMCNet is suitable for receiving \(2\times 2\) MIMO-OFDM signals and outperforms fully connected networks in terms of performance with fewer parameters. It exhibits a stronger robustness in scenarios with fewer pilots, being able to fit results with limited data. Its performance in scenarios with more pilots is comparable to traditional approaches.
## 3 Results and Discussion
### Durability Properties  
#### 3.2.1 Water Absorption (WA)  
WA is the measure of water accessible porosity of a material. WA is the indirect measurement durability of concrete, since water can carry harmful chemicals into concrete which may react with its constituents and result in changes in the properties of the material. WA tests were conducted on each mix after 28 and 180 days, and the results are given in Figure 8.  
A general trend indicates that the addition of 10%-15% MAB reduces the WA of concrete after 28 days, compared to CON. After 180 days, 10%-20% MAB reduces the WA of concrete compared to CON. This behavior can be ascribed to an improvement in particle size distribution which results in a reduction in effective pore volume of concrete. Reduction in pore volume results in reduction in WA at all ages, whereas chemical reaction between bentonite particles and free CH can decrease the length of interconnected pores. Studies [59; 60] have reported that filler effect of pozzol  
Figure 8: Water absorption test results after 7, 28, and 180 days.  
Figure 7: Correlation between experimental values of compressive strength and split tensile strength.  
metakaolin, etc.) decreases the permeability of concrete, despite reducing the early age mechanical strength. After 180 days, the mix with 25% MAB shows WA comparable to that of the CON.  
After 28 days, mixes with TMAB up to 25%, show higher resistance to WA compared to CON. After 180 days, a mix with any level of TMAB incorporated shows higher WA resistance than CON. This can be ascribed to improved reactivity of bentonite particles and better distribution of particles in the binder matrix. It was noticed that for a given age and replacement level of bentonite, TMAB-added mixes outperformed MAB by a notable margin. For example, at a 10% replacement of bentonite after 28 days, the MAB-added mix showed a 7% lower WA value compared to Con, whereas TMAB-added mix's value was 24% lower.  
The experimental relationship between WA and compressive strength values is presented in Figure 9. Generally, compressive strength value increases with decreasing WA. An inverse-linear relationship is formed is between two parameters, as given by Equation (2). The proposed relationship has good predictability, showing an R\({}^{2}\) of 0.80.  
\[\text{WA }(\%)=18.45\times\text{e}^{-0.017\times f_{c}}\text{ }\text{ }\text{(for }7,\ 28,\text{ and }180\text{ days, }\text{R}^{2}\ =\ 0.80\text{)}, \tag{2}\]  
where WA is water absorption in percentage, and f\({}_{c}\) is cylindrical compressive strength of concrete in MPa.
## 5 Results and Analysis
### Wind Speed and Power Profiles  
The average wind speed was measured and recorded on the test site, which ranged from 0 to 21 m/s to produce the power from 0 to 4 kW, as shown in Figure 9. Each season has strong wind  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Key Indicator** & **Spring Day** & **Summer Day** & **Autumn Day** & **Winter Day** \\ \hline Power range of the demand (kW) & 0–5.8 & 0–6.1 & 0–9.7 & 0–8.9 \\ Daily electrical consumption (kWh) & 12.0 & 11.1 & 16.1 & 12.5 \\ Period of demand lower than 1 kW (h) & 22.4 & 22.2 & 21.4 & 21.4 \\ Period of demand over 4 kW (h) & 0.12 & 0.12 & 0.23 & 0.23 \\ Percentage of low demand (\%) & 93.3 & 92.5 & 89.2 & 89.2 \\ Percentage of high demand (\%) & 0.5 & 0.5 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electricity demand on the four days in a UK house.  
Figure 8: Typical days for domestic electricity consumption over the four seasonal days in a UK house [31]. (**a**) spring day; (**b**) summer day; (**c**) autumn day; (**d**) winter day.  
speed, however, there is no fixed pattern available in any given season. The four diagrams in Figure 10 illustrate the wind power variations for each single day. The spring day in Figure 10a showed that the wind power gradually decreased from the peak of 1.4 kW at 1:00 to the lowest point at 11:00. It maintained this low value to 16:00. Afterwards, the wind power slowly increased to 0.5 kW around 21:00. The summer day in Figure 10b had a power output below 1 kW over most of the day except the peak power at 20:00. The wind power had relatively low variations in the autumn day with the maximum value 1.2 kW at 12:00, as seen from Figure 10c. The wind power in the winter day ranged from 0.2 to 0.9 kW with some variations, as shown in Figure 10d. The waveforms show the wind power. By comparison, more energy was generated in the autumn and the winter days than that in the spring and summer days. The summer day provided the least energy to the system among those chosen days.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 2 USV Thruster Fault Feature
### Analysis of Thruster Fault Features
#### 2.2.3 Input Voltage  
In the case of the USV used in this study, the thrust system was composed of a battery, a motor drive and a thruster. Therefore, the battery voltage level and the thrust were directly connected. The input voltage was measured between the motor drive and the thruster; thus, the input voltage was additionally selected as the fault feature for reflecting this relationship.
###### Abstract  
In this paper, the application of machine learning and deep learning algorithms for fault and failure detection in maritime systems is examined, specifically focusing on the detection of pipe ruptures in a ship's saltwater firefighting (FiFi) system using pressure sensor data. Neural network models were developed to distinguish between normal operational states and anomalies, as well as to accurately locate pipe faults within the ship. Data were collected using real-world tests with FiFi system sensors, capturing both normal operations and simulated pipe ruptures, and were meticulously labeled to facilitate neural network training. Two neural network models were introduced: one for classifying system states (normal or anomalous) based on time-series pressure data, and another for identifying the location of anomalies related to pipe ruptures. Experimental results demonstrated the efficacy of these models in detecting and localizing pipe faults, with performance evaluated using mean squared error (MSE) across different network configurations. The successful implementation of these machine learning and deep learning algorithms highlights their potential for enhancing maritime safety and operational efficiency.  
Keywords:anomaly detection; failure detection; pressure monitoring; FiFi system; machine learning; deep learning; neural network +
Footnote †: journal: Applied sciences
# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## 2 System Model
### Resource Model
\[G^{UE}_{n,k}=\alpha\frac{G^{UE}_{n,k}}{G^{Sat}_{\max}}, \tag{11}\]  
\[C^{UE}_{n,k}=\alpha\frac{C^{UE}_{n,k}}{G^{Sat}_{\max}}, \tag{12}\]  
\[O^{UE}_{n,k}=\alpha\frac{O^{UE}_{n,k}}{G^{Sat}_{\max}}, \tag{13}\]  
\[F^{UE}_{n,k}=\alpha\frac{F^{UE}_{n,k}}{F^{Sat}_{\max}}. \tag{14}\]  
Therefore, the resource demand for a user can be represented as follows:  
\[\mathcal{D}=\Big{\{}B^{UE},G^{UE},C^{UE},O^{UE},F^{UE}\Big{\}}. \tag{15}\]  
The resource capacity of a satellite can be expressed as follows:  
\[B_{n,k}^{UE}=\alpha\frac{R_{n,k}^{UE}}{W_{\max}^{Sat}}, \tag{8}\]0  
Similar to communication resources, these four types of resources also need to satisfy the constraint that the resource requirements of a single satellite user are less than the remaining available resources on the satellite. Therefore, it is necessary to satisfy the overall constraint:  
\[B_{n,k}^{UE}=\alpha\frac{R_{n,k}^{UE}}{W_{\max}^{Sat}}, \tag{8}\]1
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 4 Biochar as an Efficient Adsorbent for Organic and Inorganic Pollutants
### Parameters Affecting the Adsorption of Pollutants on Biochar
#### 4.4.2 Solution pH  
The solution pH is an important factor that control the adsorption process by influencing the surface charge of the adsorbent and the speciation, the charge and the degree of ionization of the adsorbate [136]. When the solution pH is greater than the point of zero charge, negative charge exists on the surface of the adsorbent following the deprotonation of phenolic and carboxylic groups on its surface, then the competitiveness between the protons and the cation contaminants decreases. At lower pH, basic functional groups such as amine get protonated and possess a positive charge, which promoted the adsorption of anions [154; 155; 156]. This implies that the adsorption behavior of biochar is a function of the pH of the medium and the deprotonation of functional groups. The impact of pH on biochar adsorption capacity toward ammonium (NH\({}_{4}\)\({}^{+}\)-N) was demonstrated by [PERSON] et al. [157] and [PERSON] et al. [158]. The authors showed that the adsorption of NH\({}_{4}\)\({}^{+}\)-N over biochar was lower when the pH was lower (e.g., pH = 3 or 4). With the increase in initial solution pH between 4 and 8, the adsorption capacity of NH\({}_{4}\)\({}^{+}\)-N increased and then decreased again as the pH was higher than 9.
## 4 Three-Port Converters with Partial Power Regulation
### Three-Port Converters with Partial Power Regulation  
The concept of partial power regulation in two-port converters has been introduced in TPCs. Therefore, different circuit topologies combining PPR and TPC techniques have been proposed [18; 19].  
Figure 12a-c show the power flow scheme of full power TPC, step-down TPC with PPR, and step-up TPC with PPR, respectively. Compared to full-power TPCs (Figure 12a), two additional power flow paths are employed in TPCs with PPR (Figure 12b,c), resulting in high power conversion efficiency and lower voltage and current pressures on the key components.  
Figure 11: Partial power boost DC-DC converter.  
Figure 10: Power flow of two-port partial power converters (**a**) with step-up (\(v_{c}>0\)) or step-down (\(v_{c}<0\)) operation modes. (**b,c**) Two examples of circuit structure of step-up operation. (**d,e**) Two examples of circuit structure of step-down operation.  
Figure 13 illustrates the power flow scheme of a BESS-integrated PV system connected to a DC-DC converter or a DC-AC inverter with PPR. As can be seen in this figure, the PV panel, the BESS, and the output of the PV system are connected in a series; thus, a direct power flow path (red line) is created to reduce the power rating. The power between any two of the three ports (the PV panel, the BESS, and the output) is processed through a DC-DC converter, which means that just one stage conversion is employed. When the BESS voltage is positive (\(v_{b}>0\)), the circuit is operated under the step-down mode [18], while if \(v_{b}<0\), the circuit is operated under the step-up mode [19].
## 3 Methodology
### Mathematical Description of the Different Methods  
#### 3.2.1 GPR  
Gaussian Process (GP)  
GP is a nonparametric Bayesian technique that is used in regression modelling [75]. This GP process can be described as a finite assemblage of a set of arbitrary parameters that follow a multivariate Gaussian (normal) distribution [76]. That is, for every given input point from a set of input vectors \(r=(r_{1},r_{2},r_{3},\ldots,r_{m})\), the probability distribution over its function \(h(r)\) follows a Gaussian distribution. Thus, a GP \(h(r)\) is precisely shown in Equation (1) as:  
\[h(r)\sim\text{GP}\big{(}b(r),g\big{(}r,r^{\prime}\big{)}\big{)} \tag{1}\]  
From Equation (1) it can be deduced that a GP is fully characterized by a covariance function \(g(r,r^{\prime})\) and a mean function (MF) \(b(r)\) as expressed in Equation (2).  
\[\left\{\begin{array}{l}b(r)=E[h(r)]\\ g(r,r^{\prime})=E[(b(r)-h(r))(b(r^{\prime})-h(r^{\prime}))]\end{array}\right. \tag{2}\]  
For the basic GPR, the MF is normally set as 0, however, there many other MFs which can be applied in building the GPR model [77]. The noted MFs in literature have been categorized into two kinds, namely: simple and composite. The simple MFs include zero, one, constant, linear, polynomial, nearest neighbor MFs etc. whereas the composite ones include: the scaled version, sum, product, power and warped MFs [77]. It is worth noting that this study adopted an MF with a constant, \(b\).  
The covariance function on the other hand is the main component in the development of the GPR model. The best covariance function is dependent on the data being modelled. Literature is replete with a number of these covariance functions [70]. However, the notable ones include: the rational quadratic, matem class, squared exponential and the exponential  
Figure 5: A Systematic Flowchart for Prediction of Blast-Induced Ground Vibration.  
covariance functions. The most often used covariance function is the squared exponential covariance function [77; 78].
## 2 Materials and Methods
The excitation coil has a shape of shallow perpendicular, the direction of the air core is parallel to the weld line, the vertical direction of the GMR sensor array is parallel to the weld line, i.e., parallel to the coil length direction, the GMR sensor arrays are located beside the coil and the sample, each GMR sensor is gradient with the array line, and the scan direction follows the weld line. The geometrical and physical characteristics of the test specimen are listed in Table 1. In the heat-affected zone, the electrical conductivity is gradiently decreasing from the weld to the base material \(\sigma_{1}=28\sim 18\) u\(\Omega\)/cm and the relative permeability is gradiently increasing also from the weld to the base material \(\mu_{1}=201\sim 260\). For the measurement of the electrical conductivity and the relative permeability of the welded carbon steel pipe, a micro-ohmmeter and a DC B-H loop tracer were used, as illustrated in Figure 5. The measurement condition was 23 \({}^{\circ}\)C (room temperature) and the measurement was performed by the 4-point method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Geometric Parameters** & \multicolumn{3}{c}{**Physical Parameters**} \\ \hline Inner diameter & 311 mm & Specim conductivity & 18 u\(\Omega\)/cm \\ Outer diameter & 320 mm & Weld conductivity & 28 u\(\Omega\)/cm \\ Thickness & 9 mm & Specim permeability & 260 \\ Weld width & 12 mm & Weld permeability & 201 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometric and physical parameters of the test pipe. Figure 5: Conductivity and permeability measuring equipment: (**a**) Conductivity meter; (**b**) DC B-H loop tracer.