esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Reading Comprehension Model Based on Fusion of Mixed Attention
## 5 Experimentation
### Ablation Experiment  
To study the contribution of different components of the hybrid attention mechanism model, ablation experiments were conducted. The experimental results are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **ROUGE-L** & **BLEU-4** \\ \hline match-LSTM & 33.6 & 34.5 \\ BiDAF & 36.3 & 39.5 \\ Baseline\_[PERSON] & 41.1 & 42.2 \\ Baseline\_[PERSON] & 49.8 & 51.9 \\ Baseline\_RoBERTa & 49.2 & 54.1 \\ Baseline\_RoBERTa\_wwm & 51.9 & 51.6 \\ CS—Reader & 57.6 & 58.9 \\ Hybrid Model & 61.1 & 60.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results on the TEST SET dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **ROUGE-L** & **BLEU-4** \\ \hline Hybrid Model & 61.1 & 60.9 & 61.0 \\ Hybrid Attention & 56.9 & 55.1 & 56.0 \\ Multiple Fusion & 54.9 & 54.5 & 54.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment results.  
As can be seen from Table 5, the removal of the hybrid attention component resulted in decreased ROUGE-L and BLEU-4 scores of 56.9 and 55.1 on the test set, respectively, that is, a reduction of 4.2% and 5.8%, respectively. However, when the Multiple Fusion component was omitted, the model's performance on the test set underwent a decrease, yielding ROUGE-L and BLEU-4 scores of 54.9 and 54.5, respectively. This represents a reduction of 2.0% and 0.6%, respectively. Consequently, the removal of the hybrid attention component led to an average decrease of 5.0%, while the exclusion of the Multiple Fusion component resulted in an average decrease of 1.3%. Based on the data, it can be concluded that the hybrid attention mechanism successfully fits the DuReader data, thereby preventing the model from forgetting the initial information and contributing to an improvement in prediction accuracy.
###### Abstract
[17] studied the air flow field around a building with horizontal or vertical ribs. They showed that the continuous horizontal ribs on the windward side would reduce the height at which the maximum wind pressure occurs, while the vertical ribs could reduce the turbulence intensity in the separated shear layer and near wake region. However, most previous studies focused on the effect of rib arrangement on the wind pressure while that of a rib extension depth was insufficiently looked into. [PERSON] et al. [18] found that an increase in the extension length of the vertical rib could notably reduce the vortex intensity and the vibration amplitude of a tall building, but the rib extension length used in their study was quite small, within the range of 0%-0.4% times the building width. [PERSON] et al. [19] showed that the installation of vertical ribs could significantly reduce the crosswind base bending moment by a maximum of 51.3%. However, only two large extensional depths of the vertical ribs equal to 7.5% and 12.5% of the building width were employed in their study, which may be uncommon in real projects. In design practice, rib extensional depths within the range of 0.3-1.0 m are generally considered. Therefore, it is necessary to investigate the effects of vertical ribs with various moderate (i.e., reasonable) extensional depths on the wind pressure distribution when aiming to provide useful guidance for aerodynamic optimizations of high-rise buildings. This paper aimed to show the effects of the rib extensional depth on the wind actions on a high-rise building with a square cross-section. A series of wind tunnel tests in association with the pressure measuring technique were conducted on a model with four moderate extensional depths of vertical ribs. The wind pressure distributions over each surface of the building at typical wind incidence angles were investigated. The variation of the layer wind force coefficient with the rib extensional depth was analyzed and the results were also compared with those determined by the current codes. Moreover, the influences of the rib extensional depth on the overall base shear and base bending moment of the building were explained under different wind incidence angles.
# Phtyromediation of Toxic Metals: A Sustainable Green Solution for Clean Environment
## 6 Molecular Adaptation Mechanisms of Toxic Metals in Higher Plants
### Toxic Metals Detoxification  
Hyper-accumulators can detoxify a large number of toxic metals without harming their leaves and stems. Cuticle, epidermis, and trichomes [178; 179; 180; 181; 182; 183] are the principal sites of metal detoxification in plants [178]. Metal detoxification is an enzyme-controlled process that begins with the removal of organic ligands from the metabolic region and ends with ROS detoxification [184]. Biomolecules with thiol-producing capabilities, such as PCs, MTs, and GSH, can effectively detoxify toxic metals in plants [165]. These complexes are crucial in the plant's metal tolerance mechanism. Phytochelatins, as well as heavy metals (PC-HM) Complexes, abound in plant tissue vacuoles. The HMT1 transporter, a member of the ABC family, first discovered in yeast, transports PC-HM complexes across vacuolar membranes [185] ([PERSON] et al., 1995). The plant has a mechanism that is comparable to that of yeast. GSH, as with HMT1, aggressively detoxifies toxic metals and works as a potent reducing agent, particularly for reactive oxygen species (ROS). When plants have a contact with a high concentration of toxic metals, ROS are produced in the plant's sensitive organelles [165]. GSHs have also been linked to the reduction of H\({}_{2}\)O\({}_{2}\) toxicity, the reduction of xenobiotics, a causative agent of a toxicant to flower growth, and the formation of salicylic acid [186; 187; 188]. Another GSH-regulated hazardous metal detoxification pathway is GSH-HM complexation and impoundment in vacuoles, with those complexes possibly being released to the apoplast [190]. Furthermore, metallothiones create MT-HM complexes (low molecular weight chelating protein molecule groups mainly found in cysteine). MTs are classified into four categories based on cysteine deposits' formation [99], with differences in tissue structural specificity and metal element selectivity. MT1 and MT2b are two of the four types of chelators involved in Cd detoxification [191]. The fourth type of MTs, on the other hand, primarily detoxify Zn and, in comparison to the other three types, store larger Zn amounts at a given period [192].
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Multi-Channel Attention-Based Analysis
#### 4.6.1 Binary Backbone
Finally, the perturbation on the SWIR channels ends up in a reversed scenario, in which the precision drastically decrease, but this happens in association with a significant improvement of the Recall. This trend can be explained if the network identifies as burnt almost every pixel of the perturbed images. As a last remark, Table 4 shows that the highest \(\delta\)D\({}_{\text{KL}}\) values appears for NIR and SWIR Multi-bands. Even though they are not the most relevant groups after the _all-occlusion_ perturbation, this is an interesting result, as both bands composing the NBR index (namely, 8 and 12) came from these spectral ranges. Then, regarding the wildfire delineation phase, the network attention is effectively watching where a domain expert would have looked at. The interpretations of the results as the perturbed Multi-band changes is recalled in Figure 8, in which the input images are the same of Figure 5. In both the examples we can observe that removing AER, and NIR results in a limited performance loss with respect to the normal prediction (the yellow line in the figures). This observation is in accordance to domain knowledge, since AER channel is the less significant when analyzing the spectral response. Instead, predictions from the perturbation on the RGB, VRE, and SWIR groups are close to an all-unburnt and all-burnt maps, respectively.
# Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
## 3 Improved Whale Optimization Algorithm  
### Whale Optimization Algorithm  
A new heuristic search algorithm--Whale optimization algorithm (WOA) was proposed by Australian scholar [PERSON] in 2016. The main idea of the algorithm is to solve the target problem by imitating the predatory behavior of whales. It mainly includes three stages: surround prey, bubble net [32] and search prey. As a new swarm intelligent optimization algorithm inspired by whale predation, the whale optimization algorithm has a simple structure and requires less parameters to be adjusted. The position of each whale represents one of the feasible solutions of the WOA. The optimization problem is constantly optimized by simulating whale predation behavior. Then, the optimal individual is obtained at last and can be used as the optimal solution of the optimization problem. Whale optimization algorithm has been successfully applied to solving many parameters' optimization problems due to its simple structure and fewer parameters to be adjusted [33]. However, as a new member of the group optimization algorithm, the traditional WOA also has some common problems of the group optimization algorithm, that is, it is easy to fall into the local optimal solution, the convergence speed is slow, the convergence accuracy is low, and there are still some limitations in solving some practical problems.
## 1 Introduction
On the other hand, the Ziegler-Nichols method and the PSO algorithm are more sophisticated approaches that allow fine-tuning of the controller parameters to achieve desired performance characteristics. The studies demonstrated the effectiveness of complex PID and PI-PD controllers in controlling unstable processes with dead time [2], stable and unstable processes with first order plus dead time [12], higher-order dead-time systems [31; 35], anti-lock braking systems [24], real-time pressure used in processes [14], oil chiller temperature control [26], and other applications [29; 36; 37]. PI-PD controllers showed better settling time, overshoot, load disturbance rejection, and response speed than PID controllers and conventional controllers. Although peer-reviewed studies have provided valuable insights, there is still room for further research in PI-PD controllers for additional performance measures that capture other important aspects of control system performance. Overall, the reviewed studies demonstrated the effectiveness and versatility of PI-PD controllers in controlling a wide range of systems and processes. Through exhaustive performance evaluation and the use of proper tuning methods, these controllers have demonstrated improved performance in terms of settling time, overshoot, noise rejection, and speed of response. The study aims to develop a complex-order controller that considers the complex dynamics, non-minimum phase behaviour, and long transport delays commonly observed in industrial processes. The results of this research will contribute to the advancement of control strategies and support the selection and implementation of effective control systems in industrial applications, thereby optimizing process plant operation and improving control performance. The research involves the following:  
1. Developing a mathematical model that captures the dynamic reaction of a process plant. 2. Designing an appropriate complex-order PI-PD controller. 3. Conducting simulations and experiments to compare its performance with conventional PID controllers. Furthermore, the study assesses the advantages and disadvantages of the proposed complex-order PI-PD controllers in dealing with disturbances and set-point changes to enhance control accuracy, response time, disturbance rejection, and overall stability. Findings from these studies contribute to further developing control system design and optimization, enabling more efficient and effective control of complex processes. Future research activities will explore additional performance metrics based on these findings, compare them with other advanced controllers, and develop new tuning methods to improve the performance of PI-PD controllers in various applications.
## 3 Proposed Solution
### Channel Modeling
\[\pi_{G}=\frac{P_{BG}}{P_{BG}+P_{GB}}\quad,\quad\pi_{B}=\frac{P_{GB}}{P_{GB}+P_ {BG}} \tag{7}\]  
The mean sojourn time (the expected amount of time for which the channel remains in one state before moving to the other state (either in \(G\) or \(B\))) of being in that state was also calculated. As in this work, the channel is assumed to be in one of the two states only, i.e., \(G\) or \(B\), so the mean sojourn time of state \(G\) and state \(B\) denoted by \(T_{G}\) and \(T_{B}\), respectively, can be estimated through (3) and (4) as:  
\[T_{G}=\frac{1}{1-P_{GG}}\quad,\quad T_{B}=\frac{1}{1-P_{BB}} \tag{8}\]  
After that, the steady-state probabilities (the probability of channel errors occurring in the steady state) were also calculated. The steady-state probability of being in state \(G\) or state \(B\) in terms of mean sojourn time is denoted by \(P_{GG}\) and \(P_{BB}\), respectively, and is calculated as [59]:  
\[P_{GG}=\frac{T_{G}}{T_{G}+T_{B}}\quad,\quad P_{BB}=\frac{T_{B}}{T_{B}+T_{G}} \tag{9}\]  
In this paper, when the communication channel is in one of two states (say \(G\) or \(B\)), the Bit Error Rate (BER) at each time is calculated. Hence, the probability of being in a \(G\) or \(B\) state is dependent on the number of bit errors per unit time known as Mean Bit Error Rate (\(M_{BER}\)), such as \(M_{BER}=\left(P_{GG}\times P_{g}\right)+\left(P_{BB}\times P_{b}\right)\), where \(P_{g}\) is the probability of error occurrence in the \(G\) state and \(P_{b}\) is the probability of error occurrence in state \(B\)[26]. In our implementation, the calculated \(M_{BER}\) was varied from 0.07 to 0.1%, and it was computed over the total number of bits transmitted per sequence. For the implementation of the fixed GE model, \(P_{g}\) was set to zero (0) and \(P_{b}\) was set to 0.8. The probabilities of being in a good and bad state, \(P_{GG}\) and \(P_{BB}\), are dependent on \(M_{BER}\) in the equation above. For further implementation details on channel error modeling upon selectively encrypted video, refer to the paper [24]. It should be noticed that the GE model is used to introduce bit errors in the selectively encrypted bitstream, assuming that the occurrence of bit errors is independent of each other. The implementation steps of the GE channel model are summarized below.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 4 Proposed Method: Adopting a Signal-Processing Technique
### Root Mean Square (RMS) Amplitude  
Signal data as the medium response are recorded as an amplitude with a positive and negative value of the time series. The negative and positive values tied to specific medium properties sometimes become difficult to decipher when related to certain phenomena or properties prediction. In such a case, rather than using original signal data, analysis is carried out by converting the signal into root mean square (\(RMS\)) amplitudes, where all the values are positive. The \(RMS\) amplitude is the squared amplitude within a specific interval, as calculated using the following mathematical expression:  
\[RMS\ Amp=\sqrt{\frac{1}{N}\sum_{i=1}^{N}Amp^{2}} \tag{2}\]  
where \(N\) is the number of samples, and \(Amp\) is the amplitude of each sample. The calculated \(RMS\) amplitudes can now be directly related to the properties of the medium.  
However, interpretations based on amplitude anomalies have their pitfalls due to the non-unique amplitude responses due to the complexity of the constituent of the medium. Sometimes the high amplitudes of the signal are not uniquely related to the certain condition of the medium.  
Medical imaging such as CT scanning is conducted to image the human body using X-ray energy. Different media/tissues will absorb X-ray energy differently, which will then be projected into an image as a grayscale or Hounsfield unit (HU). CT scans produce a three-dimensional image in which each voxel of the image indicates the properties of human tissue. A high grayscale value (brighter) is associated with hard tissues such as bones and teeth, while a dimmer (darker) is related to soft tissues such as muscles or lungs. The grayscale values of the pixel in the images resemble the RMS amplitude scale in the time series signal. Analysis based on this value can be used to characterize the properties of human tissues. Unfortunately, different machines of X-ray produce different levels of energy which impact the recorded grayscale values. High grayscale values are sometimes caused not only by hard tissue but also by the source of the X-ray. Hence, the direct interpretation of the grayscale value needs to consider the X-ray energy source used during scanning.
# ARIBC: Online Reporting Based on Identity-Based Cryptography
## 3 Structure of the ARIBC
### ARIBC Setup Phase
#### 3.3.4 Selection of Hash Functions  
Five cryptographic hash functions are to be used in the signing and encryption procedures, as follows:  
* \(H_{1}\)**:**: \(\{0,1\}^{*}\to\mathbb{Z}_{p}^{*}\), where \(p\) = \"prime order\" of \(G_{1}\), \(G_{2}\), \(G_{T}\) is a cryptographic hash function viewed as a random oracle for hashing the \(ID\) of the receiver [45]; according to [42], the SHA family [48] is to be used, with the specific SHA function determined according to the value of the security parameter. Note that \(ID\) needs to be converted from a bit-string to an octet string before being used. Further details may be found in sections 5.2.6 and 6.1.1 of [42]. \(H_{1}\) is used both in the authenticity and integrity service, and in the confidentiality service.
* \(H_{2}\)**:**: \(G_{T}\times\{0,1\}^{*}\to\mathbb{Z}_{p}^{*}\) is a cryptographic hash function viewed as a random oracle. \(H_{2}\) is used only in the authenticity and integrity service.
* \(H_{3}\)**:**: \(G_{T}\to\{0,1\}^{length}\) is a cryptographic hash function, typically of the SHA family, viewed as a random oracle for XOR-ing the transmitted data. Further details may be found in sections 5.6.4 and 6.2.1 of [42]. \(H_{3}\) is used only in the confidentiality service.
* \(H_{4}\)**:**: \(\{0,1\}^{length}\times\{0,1\}^{length}\to\mathbb{Z}_{p}^{*}\) is used to derive a blinding coefficient. \(H_{4}\) is used only in the confidentiality service.
* \(H_{5}\)**:**: \(\{0,1\}^{length}\to\{0,1\}^{length}\) is used for XoR-ing with the plaintext. \(H_{5}\) is used only in the confidentiality service.  
Note that the transmitted data may be either communication messages or the key to be used with a symmetric algorithm, such as e.g., the Advanced Encryption Standard (AES), to encrypt subsequent communication.
# Applying Deep Learning to Medical Imaging: A Review
## 2 Deep Learning Techniques in Medical Imaging
### Convolutional Neural Networks (CNNs)
#### 2.1.3 Transfer Learning  
Transfer learning is a popular approach in DL, where a pretrained model is fine-tuned for a new task or domain, leveraging the knowledge acquired during the initial training [71]. This technique is particularly useful in medical imaging [72; 73; 74], where annotated datasets are often limited in size [75]. By using pretrained models, researchers can take advantage of the general features learned by the model on a large dataset, such as ImageNet, and fine-tune it to perform well on a specific medical imaging task [76]. Transfer learning has been successfully applied in various medical imaging applications, including diagnosing diabetic retinopathy from retinal images, classifying skin cancer from dermoscopy images, and segmenting brain tumors from MRI scans [60; 77; 78; 79].
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 2 Materials and Methods
### Description of ET Models
#### 2.1.2 Priestley-Taylor Two-Source Energy Balance Model, TSEB-PT  
Two-source energy balance models treat the land surface as two layers, soil and canopy, contributing to the energy and water fluxes (Equation (4))  
\[R_{n,C} =H_{C}+\lambda E_{C} \tag{4a}\] \[R_{n,S} =H_{S}+\lambda E_{S}+G \tag{4b}\]  
where soil (canopy) sensible heat flux is computed from the gradient between the soil (canopy) temperature (\(T_{S}\) and \(T_{C}\) respectively) and the air temperature at the sink-source height (equivalent to \(T_{0}\)). In the TSEB-PT model [28; 36; 53], an electrical circuit analogy is used in which \(H\) from soil and canopy are estimated based on three aerodynamic resistances to heat transport arranged in a series network. Since \(T_{C}\) and \(T_{S}\) are unknown _a priori_, they are estimated in an iterative process in which it is first assumed that green canopy (expressed as the fraction of LAI that is green, \(f_{g}\)) transpires a potential rate based on Priestley-Taylor formulation [36]:  
\[\lambda E_{C}=\alpha_{PT}\,f_{g}\frac{\Delta}{\Delta+\gamma}R_{n,C},\quad \alpha_{PT}=1.26 \tag{5}\]  
where \(\alpha_{PT}\) is the Priestley and Taylor [57] coefficient, \(\Delta\) is the slope of the vapour pressure to air temperature curve and \(\gamma\) is the psychometric constant. Then the canopy transpiration is sequentially reduced (i.e., \(\alpha_{PT}<1.26\)) until realistic fluxes are obtained (\(\lambda E_{C}\geq 0\) and \(\lambda E_{S}\geq 0\)).  
TSEB-PT probably is the model that requires most accurate retrievals of physical inputs (\(LAI\) and \(T_{rad}\)), and studies already reported larger uncertainty in senescent vegetation (i.e., \(f_{g}<1\)) and very dense (high \(LAI\)) or tall vegetation [43; 58]. It is more complex than METRIC and therefore has a large number of parameters and modelling options. Finally, the Priestley-Taylor formulation was shown to produce larger uncertainty in high advection conditions, cases in which initializing \(\lambda E_{C}\) with a Penman-Monteith formulation showed better results [37]. Combining TSEB-PT model with the disaggregation approach (described in Section 1) results in a disTSEB model [23].