esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Artificial Neural Network  
Artificial neural networks (ANNs) based on the full spectra were also studied, allowing for the development of a regression model of rice pasting properties. The noise present in the spectral data was previously eliminated using pre-processing methods (SNV, MSC, and smoothing derivative). Five models were developed separately to predict the pasting parameters (BD, ST, TR, PV, and FV) based on the NIR spectra. The best ANN models were characterized by a network model with 10 hidden nodes, presenting higher R values for the calibration step--BD (0.99; 38.7), FV (0.99; 161), PV (0.99; 107), ST (0.99; 5.1), and TR (0.99; 5.7)--than those attained by [PERSON] et al. (2021) in rice flour (0.96 for BD and ST) [10].  
The correlation coefficient (R = 0.99) showed a suitable fit between the observed and predicted data, showing that the MLP algorithm associated with the Broyden-Fletcher-Goldfarb-Shanno learning algorithm can be helpful in modeling the pasting properties, as compared with iPLS and siPLS (Table 2, Figure 4A-D). The ANN algorithm was also applied to develop models to predict the pasting profiles as part of a faster and more accurate method for rice quality analysis [31]. Based on the ANN model, we constructed an optimized regression model characterized by low prediction error and, consequently, a suitable accuracy. Neural networks may recognize complex relationships and generalize outcomes from a specific pattern of data and are therefore considered a suitable technique for modeling complex systems. Compared with the iPLS and siPLS models for the different pasting parameters, the models developed using ANNs can be considered appropriate tools for industrial agents for rice quality evaluation, allowing them to save time and reduce associated costs. This strategy, due to its feasibility and quickness, could be replicated in other products to examine industrial parameters.
## 2 Methodology
### Formal Definition
#### 2.5.4 Reward Function  
The DQN agent achieves a reward or penalty based on how good or bad the state it has reached is. The ability of the DQN agent to converge lies in choosing a proper reward function. The reward function that can reward or penalize gradually based on some criteria helps achieve DQN agent converge quickly and choose meaningful actions.  
The criteria of the reward function are chosen based on the state of the occupant's visual comfort. The reward gradually increases from the DGP value of 0.2 until 0.36 then starts penalizing once it crosses 0.36. Likewise, it also penalizes when the DGP value goes under 0.2. The same happens with daylight availability in the range of 300 to 3000 lux. Both daylight availability and Glare are equally important so they are given equal weightage in reward and penalty.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **ID** & **IGDB ID** & \(T_{vis}\) \\ \hline Clear & 0 & 8905 & 0.448 \\ Low tint & 1 & 8906 & 0.121 \\ Medium tint & 2 & 8908 & 0.040 \\ Dark & 3 & 8909 & 0.007 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of various states of electrochromic glass and their IDs.  
The mathematical representation of the reward function for glare is given by:  
\[r_{g}(d_{i})=\begin{cases}(\frac{d_{i}}{d_{min}})-1,&\text{if }d_{i}\leq d_{max}\\ -\frac{d_{i}}{d_{min}},&\text{otherwise}\end{cases}\]  
where \(d_{i}\) is the DGP value at each of the workspaces from 1 to 4 and \(d_{min}\) is the minimal value of DGP where the comfort range starts i.e., 0.2, similarly \(d_{max}\) is the maximum value of DGP where teh comfort range ends i.e., 0.36. The total reward based on DGP is  
\[R_{glare}=\sum_{i=1}^{4}r_{g}(d_{i})\]  
The reward function for daylight availability is given by:  
\[r_{ill}(l_{i})=\begin{cases}(\frac{l_{i}-l_{min}}{l_{max}-l_{min}}),&\text{if }l_{i} \leq l_{max}\\ -\frac{l_{max}-l_{min}}{l_{max}-l_{min}},&\text{otherwise}\end{cases}\]  
Figure 3 shows the graphical representation of the glare reward function. The total reward from glare can vary from \(-5\) to \(3.2\).  
The total reward based on useful daylight availability is  
\[R_{ill}=\sum_{i}r_{ill}(l_{i})\]  
where \(l_{i}\) is the average illuminance value at each of the workspaces near (1 and 4) and away (2 and 3) from the facade. \(l_{min}\) is the minimal illuminance value of daylight where the comfort range starts i.e., 300 lux, similarly \(l_{max}\) is the maximum illuminance value of daylight where comfort range ends i.e., 3000 lux.  
Figure 4 shows the graphical representation of the illuminance reward function. The total reward from illuminance can vary from \(-\infty\) to 4.  
Therefore the total reward achieved by the DQN agent at any time step is given by  
\[R=R_{glare}+R_{ill}\]  
Theoretically, the total reward can vary from \(-\infty\) to \(7.2\).  
Figure 3: Reward function based on glare (DGP values).
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Assessment of Drought Hazard and Vulnerability
#### 2.6.1 Calculation of SPEI Drought Index  
To represent the relative probability occurrences of drought events in the Lam Phaniang River Basin, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated, which describes the trend, frequency, and characteristic values of droughts like severity, duration, and intensity, due to the simplicity of the calculation procedure including the effects of temperature variability for drought assessment [27]. In principle, the SPEI is a comparison of the accumulated climatic water balance (rainfall minus potential evapotranspiration) over a specific month (i, i \(-\) 1,..., i \(-\) n) to the equivalent n month accumulations over the historical record (2000-2017). For computing the SPEI values, the historical rainfall data of each station were fitted to a gamma probability distribution function. To obtain the SPEI, the cumulative probability, which allows the distribution of a rainfall event at the station to be effectively represented for each month of the year and at different time scales, was then transformed to the standard normal distribution or standardized normal variable. With a mean SPEI value of 0 and a standard deviation of 1, the SPEI value can be compared with other values over time and space [27]. Referring to [PERSON] et al. [28], the classification of drought intensities based on the range of SPEI dimensionless values was made into four different drought categories, where negative and positive values indicate drier and wetter than average conditions, respectively (see Table 1).
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems
### Global Navigation Satellite System  
One of the most interconnected systems is the Global Navigation Satellite System (GNSS). As a result, autonomous vessels that rely on improved satellite communications to transmit operational commands and sensor data may be at risk of cyberattacks [34], such as denial-of-service attacks, package changes, and man-in-the-middle attacks. Furthermore, low-power satellite signals have a significant technical disadvantage due to simple congestion. As a result, spoofing and jamming are considerable flaws that may represent high-cost and low-effort attacks [35; 36]. Additionally, because many ship systems rely heavily on satellite position, GNSS failure can lead to the breakdown of other ship systems (e.g., AIS). Autonomous transportation systems must be capable of communicating with operational crews from ground crews, allowing cyberattacks to take full control of critical transport operations, allowing for a broader range of attacks and incentives for intruders [10; 15].
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 3 Propagation Modeling
### Cost231  
One of the frequently used propagation models for indoor environments is the COST231, which is formulated in Equation (2) [30]. Implementation of this model is presented in [31; 32]. Unlike the log-distance model, the COST231 model requires site-specific information, such as the layout of the indoor environment; as a result, it offers better accuracy [33].  
\[PL=L_{FS}(d)+L_{C}+\sum\alpha_{j}k_{j} \tag{2}\]  
where \(L_{C}\) is the constant loss (dB), \(L_{FS}(d)\) is the free space path loss at distance \(d\) between transceivers, and \(\alpha_{j}\) and \(k_{j}\) are the attenuation coefficient and the number of walls of type \(j\) between the LoS.
## 3 Numerical Experiments
### Sensitivity Analysis  
The cost of battery purchase is one of the most critical factors impacting the profitability of the BSS. Table 5 shows the effects of different unit capacity costs of batteries on the optimization results of BSS configuration and various economic indicators of the BSS.  
Table 5 shows that when the unit capacity cost of batteries is larger than 60 USD/kWh, the optimal number of purchased battery blocks and electricity cost do not vary with unit capacity cost of batteries. This is because when the unit capacity cost of batteries is higher, the cost of purchasing an additional battery block is higher than its reduced charging cost due to charging at valley electricity prices, which makes the total annual cost of the BSS an upward trend.  
In order to analyze the impact of the ToU price on the operation of BSSs, we keep the price during valley price periods constant and increase the price during peak periods in equal proportion to form different ToU price schemes.  
Figure 7 shows that raising the electricity price during peak periods, i.e., increasing the difference between peak and valley electricity prices, leads to an increase in the optimal number of backup battery blocks in the BSS. This result is due to purchasing an additional battery, bringing a higher return on charging during valley price periods than its annual investment cost. Moreover, an interesting result is that when the peak electricity price growth rate is greater than 1.5, the electricity cost starts to decrease with the increasing growth rate. This indicates that the additional battery blocks are purchased to act as an energy storage battery, which is charged during the valley price periods and is then used for \(BSD\) during the peak price periods. BSS hardly charges the battery blocks during peak price periods.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Unit Capacity Costs of** & **Number of Purchased** & **Annual Investment Cost** & **Electricity** \\
**Batteries (USD/kWh)** & **Battery Blocks** & **of Battery Blocks (USD)** & **Cost (USD)** \\ \hline
100 & 31 & 19,502.11 & 84,049.78 \\
80 & 31 & 15,601.69 & 84,049.78 \\
60 & 31 & 11,701.27 & 84,049.78 \\
50 & 32 & 10,065.61 & 83,653.52 \\
40 & 33 & 8304.13 & 83,332.93 \\
30 & 34 & 6416.82 & 83,080.15 \\
20 & 35 & 4403.70 & 82,882.90 \\ \hline \end{tabular}
\end{table}
Table 5: Configuration optimization results of BSS under different prices of battery.  
Figure 6: Battery charging schedule in two scenarios over one year.
# Interactive Airfoil Optimization Using Parsec Parametrization and Adjoint Method
## 6 Conclusions  
This article focused on the fast interactive airfoil shape optimization achieved by assuming an incompressible inviscid potential flow. The objective of the optimization was to improve the lift coefficient of the airfoil. The optimization was carried out on the well-known NREL S809 airfoil, which is used on wind turbines. From the results obtained, it can be concluded that coupling the adjoint method with parsec parametrization and the panel method can yield feasible results and can run at a speed that is more than fast enough for it to be considered interactive. The panel method and the adjoint optimization code were written in MATLAB R2023b, and the verification computations were carried out in Xfoil 6.99. From the results obtained, the following statements can be derived:  
1. The optimization showed a significant improvement to the objective function \(C_{y}\). For 0 deg AoA, the improvement was 94.7%, and for 6.2 deg AoA, it was 16.1%.
2. A big improvement was also observed in the L/D ratio, which matched or even exceeded the improvement to \(C_{y}\), as the optimization did not noticeably affect the \(C_{d}\).
3. For different AoAs (0 and 10 deg), the resulting shape was almost the same, meaning that the shape optimized for one operating point should work well in a wide range of AoAs, adding to the practical usability of the optimized airfoil.
4. The runtime of the adjoint optimization was, for the same airfoil with the same cost function, orders of magnitude shorter than the runtime of the GA coupled with Xfoil while yielding similar results.  
Overall, this coupling seems very promising, but it is not without its difficulties. For example, further work would be needed to incorporate additional constraints into the optimization, most likely via the penalty formulation. However, the approach presented in this paper appears to suit those engineering problems where the inviscid incompressible flow can be assumed to be better than the ML approach because it avoids most of the main hindrances of ML while yielding convincing results.  
Conceptualization, M.B. and T.H.; methodology, M.B.; software, M.B. and T.H.; supervision, T.H.; validation, M.B.; writing--original draft, M.B.; writing--review and editing, M.B. and T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Grant Agency of the Czech Technical University in Prague, grant No. SGS23/105/OHK2/27/12.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 2 Related Work
### Attention Mechanism
As shown in Figure 4, it combines a CNN for extracting image features with an attention mechanism to focus on important object regions and allocate attention to spatial and channel dimensions. CBAM consists of two main components: channel and spatial attention. Figure 5 illustrates the channel attention module, which helps focus on important channel features by modeling the relationships between channels in the input feature map. Spatial attention models the relationships between spatial positions in the input feature map to concentrate on important parts of the objects. CBAM models the interaction between the channel and spatial positions in the input feature map through these two attention mechanisms, enabling stronger attention and meaning towards specific objects. Channel attention generates an attention map by leveraging the relationships between channels in the features. First, channel attention calculates the average value for each channel in the input feature map. Spatial compression was performed using max pooling and avg pooling to efficiently compute the operations and transform them into a one-dimensional vector to achieve this. A multi-layer perceptron (MLP) learns the important weights and calculates the relevance of channel features among different feature maps using the channel-wise average values. The MLP also models interactions between channels. The obtained weights are then transformed into an appropriate form using an activation function. Typically, the sum of two outputs and a Sigmoid function are used to generate an attention map within the range of 0 to 1. \[M_{C}=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F))). \tag{3}\]  
Equation (3) represents the channel attention module of CBAM. Here, \(\sigma\) denotes the Sigmoid activation function, MLP refers to the multi-layer perceptron, avg pooling represents average pooling, max pooling denotes maximum pooling, and F represents the input feature map. As shown in Figure 6, spatial attention was used to model the spatial information across channels. This is typically achieved by multiplying the computed weights through convolutional layers and activation functions with the input feature map. The most common approach is to generate a one-channel feature map using a 1 \(\times\) 1 convolution, which is then incorporated into the input feature map. Unlike channel attention, spatial attention calculates the relevance between pixels. Subsequently, a Sigmoid function is used to generate an attention map within the range of 0 to 1. \[M_{S}=\sigma\Big{(}f^{7\times 7}([AvgPool(F);MaxPool(F)])\Big{)}. \tag{4}\]  
Equation (4) represents the spatial attention module of CBAM. Here, \(f^{7\times 7}\) denotes a \(7\times 7\) convolution. Figure 4: Convolutional block attention module. Figure 5: Channel attention module of the convolutional block attention module.
# A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain
## 4 Experiments
### Knowledge Graph Visualization  
After the experiments on the extraction layer, a set of triples containing multiple entities and relationships was obtained. The triples were then cleaned and underwent anaphora resolution. The results of the entities after cleaning and anaphora resolution are shown in Table 9.  
Figure 6: Performance of the model under different training data scales.  
In order to clearly present the relationships of each triplet, this study uses Neo4j for visualization. Compared to traditional databases, Neo4j allows the representation of nodes and the relationships between them in a graph format. Additionally, it supports both graphical user interface tools and the Cypher command-line tool, which facilitate database operations. Figure 7 below shows a partial visualization of the results.  
By performing information mining on the constructed appliance failure knowledge graph, it can be observed that the top five most frequently malfunctioning appliance types are washing machines, air conditioners, refrigerators, gas water heaters, and televisions. The most common failure symptoms associated with these appliance types are the washing machine not spinning during the dehydration cycle, the weak cooling effect of air conditioners, poor cooling performance of refrigerators, intermittent hot and cold water in gas water heaters, and no sound in televisions. Based on these commonly occurring appliance types and failure symptoms, the respective appliance manufacturers can take targeted measures. For instance, manufacturers of gas water heaters could innovate in the gas valve and thermostat components to enhance product quality and improve customer satisfaction.
## 3 Stray Current Corrosion Morphology, Measurement Method, and Influencing Factors
### Factors Affecting Stray Current Corrosion
#### 3.4.3 Other Factors
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor Categories** & **Specific Influencing Factors** & **Impact Descriptions** & **References** \\ \hline \multirow{8}{*}{Environment factors} & \multirow{4}{*}{Soil salt concentration} & Lower resistivity soil allows stray current to pass through more easily, leading to increased corrosion. & \multirow{4}{*}{[62]} \\  & & High-resistivity soil effectively inhibits stray current corrosion. & & \\ \cline{1-1}  & & Higher salt concentration increases conductivity, & & \\ \cline{1-1}  & & promoting the spread of stray current, which leads to & & [64] \\ \cline{1-1}  & & more severe corrosion. & & \\ \cline{1-1}  & & Soil oxygen concentration & Higher oxygen concentration in the soil promotes & \multirow{4}{*}{[16]} \\ \cline{1-1}  & & localized chemical reactions, accelerating corrosion. & & \\ \cline{1-1}  & & localized chemical reactions, accelerating corrosion. & & \\ \cline{1-1} \cline{2-5}  & & Soil porosity & Higher porosity allows stray currents to penetrate the & \multirow{4}{*}{[64]} \\  & & pipeline surface more easily, exacerbating corrosion. & & \\ \cline{1-1} \cline{2-5}  & & Buried depth & The burial depth of the pipeline can reduce the impact & \multirow{4}{*}{[63]} \\  & & of stray currents, but soil properties still & & \\ \cline{1-1}  & & influence corrosion. & & \\ \hline \multirow{4}{*}{Temperature, humidity, pH value} & High temperature, high humidity, and low pH soil will & \multirow{4}{*}{[61,64]} \\  & & accelerate the transmission of stray current to the & \\ \cline{1-1}  & & buried pipeline surface and make metal ions more & \\ \cline{1-1}  & & easily dissolved, aggravating corrosion. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Factors affecting stray current corrosion.
# Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance
## 3 Quadrotor Attitude Control System Design
### First-Order ESO and SEF for Quadrotor Attitude Control  
In a quadrotor attitude control system, taking the x axis as an example, the system can be written as:  
\[\begin{cases}\phi=\omega_{x}\\ \omega_{x}=b_{0x}*a_{x}+\xi_{x}\end{cases} \tag{7}\]  
\[\begin{cases}b_{0x}=\frac{\sqrt{2}}{2}*\frac{1}{j_{xx}}*C_{T}*d*scale_{x}\\ \xi_{x}=\frac{\omega_{y}\omega_{z}}{j_{xx}}\left(J_{yy}-J_{zz}\right)+\frac{1} {j_{xx}}D_{x}\end{cases} \tag{8}\]  
where \(b_{0x}\) is the estimation of the CGP and \(\xi_{x}\) is the estimation of the total disturbance. The ADRC algorithm takes the nonlinearities, uncertainties, and external disturbances as the total disturbance. The total disturbance can be observed by the ESO in real time with the quadrotor state input, the control output, and an estimation of CGP. The quadrotor control system receives angular velocity data directly from the gyroscope sensors, so the form of the ESO for the quadrotor attitude control system in a single axis is a first-order ESO (Algorithm 1):  
Figure 2: The quadrotor attitude control system diagram.  
```
\(y\): angular velocity data from sensors

\(z_{1}\): angular velocity output

\(z_{2}\): disturbance output

\(h\): time step

\(b_{0}\): critical gain parameter

\(u\): control output

\(e\), \(fe\): intermediate variables

\(\delta\), \(\beta_{01}\), \(\beta_{02}\): adjustable parameters

\(fal\): a non-linear function [14]
```
1:\(e=z_{1}-y\)
2:\(fe=fal(e,0.5,\delta)\)
3:\(z_{1}=z_{1}+h*(z_{2}-\beta_{01}e+b_{0}u)\)
4:\(z_{2}=z_{2}+h*(-\beta_{02}*fe)\) ```
**Algorithm 1** Extended state observer for quadrotor attitude control.  
Using the ESOs, the disturbance of three axes are estimated, and the disturbance feedback should be added to the controller output:  
\[\left\{a=a_{0}-\frac{z_{0}}{b_{0}}\right. \tag{9}\]  
where \(a\) is the final control value and \(a_{0}\) is the output of the controller.  
In this paper, only roll and pitch ESOs are designed, because the yaw axis is hardly affected by the external disturbances in our outdoor flight experience. In severe flight situations, such as propeller breakage and load swinging, the control system should focus on maintaining the balance of the roll and pitch axes, and the performance of the yaw axis may not be so important.
# Surface Texture Measurement on Complex Geometry Using Dual-Scan Positioning Strategy
## 5 Experimental Verification
### Long-Travel Line Scan  
A free-form sample was fabricated to verify the long-travel measurement capability of the proposed dual-scan surface tracing system. The system setup is shown in Figure 5a. Areas with different roughness were intentionally made on the sample surface. The surface roughness measurement results are shown in Table 4, with a standard deviation of five repeats for each measurement. A stylus profilometer (Talysurf PGI 800, AMETEK Inc., Berwyn, PA, USA) was used as the reference system.