esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion of Explainable AI Approaches  
In the development of high stakes decision-making systems, such as computer-aided diagnostic systems, it is of fundamental importance that we understand how those systems work and how they make decisions; without this knowledge, these systems cannot be trusted. XAI is rapidly becoming one of the mainstream subjects in AI as it provides the foundations for understanding complex AI models, a necessary requisite for deploying these systems in critical applications. XAI is still in its early stages. New and better explanation techniques are being developed, and we expect that they will revolutionize the healthcare field.  
Automated medical report generation can be subdivided into two problems, which are related but come from two separate domains of study. Analysis of medical images is one aspect of the problem that is related to computer vision. The other half of the problem belongs to natural language processing (NLP). There is no standard single XAI technique available that can be applied simultaneously to computer vision and NLP models. When analysis of medical images needs to be conducted on parts of an image, class activation maps (CAM) and its variants have proven to be important techniques for explaining the decisions of ConvNet classifiers. In situations where we do not have understanding of the AI model which needs to be trained, model-agnostic techniques, such as LIME, are helpful.  
Attention mechanisms are part of neural architectures and are capable of dynamically highlighting relevant features of the input data, which in the case of image analysis focuses on specific parts of an image and in the case of NLP on a particular sequence of textual elements. If we are interested in finding the role of each pixel to the training of an AI model, then LRP is the best-suited technique as it propagates the output back through the network until reaching the input layer using the network weights and neural activation created by the forward-pass.  
A summary of XAI techniques used in medical image analysis and report generation is depicted in Table 2. Being post hoc techniques, CAM, Grad-CAM, LIME, LRP and CAV use trained networks to generate explanations, whereas attention-based explanations and explanations generated for image captioning are ante hoc techniques. LIME and CAV are techniques that generate global explanations. CAV can also generate local explanations.  
Two important open research problems that we encountered through our study are the following.  
* An integrated XAI framework is required for automated medical report generation. The framework should integrate the explainability aspect for both image processing and text generation. Currently, existing XAI methods deal with only one aspect of the automated report generation process.
* A reasoning mechanism is required to provide quantitative, and not just qualitative explanations of the decisions of a model. This will be helpful to understand and improve the accuracy of AI models.
## 3 Results
### The Influence of Talbot Power Exponent on Seepage Velocity  
The relationship between seepage velocity, pressure gradient, and Talbot power exponent under ten-stage Talbot power exponent and six-stage different stress levels was shown in Figure 8.  
Figure 7: Linear flow with different Talbot power exponents.  
The seepage velocity is influenced by the Talbot power exponent, pressure gradient, confining pressure, and axial pressure, with the pressure gradient having the most significant effect, as shown in Figure 8. It can be observed that a higher Talbot power exponent and pressure gradient, combined with lower confining and axial pressures, result in a higher seepage velocity. Increasing the Talbot power exponent increases the seepage velocity, while increasing the axial and confining pressures inhibits the ability of the Talbot power exponent to increase the seepage velocity. Specifically, the pressure gradient amplifies the enhancing effect of the Talbot power exponent on seepage velocity, while the axial and confining pressures counteract this effect. Additionally, the Talbol power exponent enhances axial stress and peripheral pressure suppression effects on seepage velocity.  
Among these factors, the enhancement of the promoting effect of the Talbot power exponent on the seepage velocity by the pressure gradient differs slightly from the experimental observations of [PERSON] et al. [16]. This difference is due to subtle variations in experimental principles. In steady-state permeability experiments, the osmotic pressure at both ends of the sample remains constant, ensuring a stable pressure gradient. In contrast, during transient permeability experiments, the osmotic pressure difference between the ends of the sample gradually decreases during the infiltration process.
# Optimization of Motion Planning and Control for Automatic Machines, Robots and Multibody Systems
## 2 Book Overview
### Motion Control  
As already discussed in the Introduction, control is tightly related to motion planning, and concurrent approaches that optimally develop the controller and the planner can be developed too. For example, in order to enhance the stability of hydraulic quadruped robots, a centroid-based controller for quadrupedal pacing is proposed in [15]. The real-time attitude feedback information of the trunk centroid is introduced into the trajectory planning of the trunk centroid. Joint torques that minimize the contact forces are calculated and the positions and attitudes of the robot trunk are adjusted by the spring damper virtual elements. Experimental results show that the proposed approach ensures the smooth motion of the robot trunk.  
The issue of motion control is discussed for autonomous underwater vehicles (AUVs) in [16]. In this paper, a saturation-based nonlinear fractional-order proportional derivative (FOPD) controller is proposed for AUV motion control. The results prove that proposed controllers can achieve better dynamic performance, as well as robustness, compared to traditional proportional derivative controllers. Additionally, the controlled performance can also be adjusted to satisfy different control requirements. The numerical results show the benefit of this novel approach in set-point regulation and trajectory tracking.  
Another marine mechatronic system is investigated in [17], and motion control is performed through a closed-loop strategy. In this work, a command filter-based backstepping sliding mode controller with prescribed performance is developed to perform ship roll stabilization. First, the impact of external disturbances is eliminated by a nonlinear disturbance observer. Second, a command filter-based backstepping control method is adopted. Precise tracking performances and the steady state of the ship rolling angle are guaranteed, as well as high robustness of the proposed control strategy.  
The issue of motion control is critical in teleoperation robotic systems. To overcome the limitations of human motion accuracy, a paper [18] introduces new interaction logic, a scalable human-robot motion mapping mechanism and a single axis mode to balance teleoperation efficiency and accuracy. In order to meet the requirements of complaint assembly skill, a vibration-based force feedback system was developed to let the operator feel the contact force. An active force control mechanism was also designed to restrict the contact force within a safe range. The gesture-based teleoperation system is tested with a pick-and-place and peg-in-hole case study and the results prove its effectiveness and feasibility in tight, tolerant and complaint assembly tasks.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion
### Countermeasures
#### 4.2.6 QKD Protocols  
Only the disruption of decoy state BB84 QKD has been analysed here. Other discrete-variable (DV-QKD) protocols and associated transmission architectures should be investigated including standard BB84, Ekert 91 [19], twin-field QKD [4], and likely future protocols.  
Unlike DV-QKD, continuous variable QKD (CV-QKD) [20; 21] modulates and measures the quadrature of the electric field of the light, rather than detecting the presence, or otherwise, of a single photon. This protocol is less mature than the most common DV-QKD protocols, but holds great promise because it can exploit conventional telecommunications hardware. Once developed sufficiently to deploy on board a satellite, CV-QKD will be able to apply much more stringent spectral filtering to the incoming light [22]. This will make it necessary to carefully target the correct transmission frequency, including adjusting for the Doppler shift due to the satellite's motion. Uncertainty in the QKD transmission frequency will greatly increase the overall power required from the laser terminal in order to cover the expected frequency range with sufficient power to cause disruption.
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 4 Characterizing Heavy Rain Event
### Correlated Characteristic between Weather and Outage  
Most power components are exposed to weather events that can cause unexpected power outages. Power components are designed to endure normal intensity weather; however, if the weather intensity exceeds the power component's design threshold point, a power outage occurs. In outage data, wind and rain events are recorded as the causes of power outages and hence it is difficult to specify the exact type of event that induces the power outage. Moreover, heavy-rain events generate a large amount of precipitation and sometimes occur simultaneously to strong wind conditions. Therefore, two types of events must be considered as the cause of power outages under heavy-rain conditions.  
There were no historical outage cases predominantly recorded, indicating that the distribution of outage data was significantly distorted. In other words, the dataset has zero-inflated characteristics since the power outage events occur with low probability even under heavy-rain conditions. Therefore, the probability density distribution of this dataset has a right-skewed distribution rather than the normal distribution. A log-transformation was performed to improve interpretability and to reduce the skewness of the data. The zero values of the outage customers were not included when analyzing the relationship between weather and outages. Figures 2 and 3 show that with higher accumulated rainfall and wind speed, the number of outage customers tended to be larger. The Pearson correlation coefficients of rainfall and wind speed with the number of outage customers are calculated as 0.3145 and 0.2354, respectively. The color bar represents the probability density, estimated via the kernel density estimation method, and the red line represents the regression line of the scatter plot. These figures show that major outages occurred with a low probability and minor outages occurred with a relatively high probability.  
model can capture a larger portion of the variance. Although all models have a low R-squared value, this analysis suggests that wind speed could help to predict the power outages even under heavy rain conditions. Moreover, the model with wind and rain has the highest R-squared value, accounting for 8.21% of the variance. This suggests that the multiple weather effect could play an important role in causing the power outage event.
# Privacy and Security in Federated Learning: A Survey
## 5 Open Issues and Future Trends
### Research Directions  
Recent contributions such as [59] propose innovative approaches that consist of detecting communities of clients that share similar behaviors. The aim is next to build up a general model that integrates all those populations in an equal way and thus reduces bias. Such an approach could also help detect specific outliers such as poisoning sources, thus increasing security. Such a direction is indeed promising; however, the identification of those communities may lead to new issues related to user privacy. In addition, the semantics and ethics behind the identified clusters should also be clearly reported. Addressing those challenges is, therefore, of real interest.  
A complementary research direction relates to partial federated learning recently introduced in [60]. Such an approach considers the fact that clients may need to share only a subset of their knowledge and still improve each other. As for real applications related to healthcare, autonomous driving, and home automation, clients may have different sensors but try to solve the same problem or try to solve different tasks with the same sensors. A given model may thus be considered as a set of cascading functions, some being local and private while others are shared. Such an approach has many potential advantages. It better corresponds to real-life applications and could also reduce global costs while improving generalization behaviors for a variety of complementary problems. In addition, it provides means to increase security and privacy by sharing only a fraction of the knowledge. We believe that this research direction is promising, and suggest associating this with bias detection and mitigation research.  
Finally, new models of artificial intelligence approaches that specialize in blockchain technology seem relevant, sharing similar ideas. For instance, Swarm Learning (SL) [61] takes the main principle of FL, where the data are not shared with a central server. However, the computation of the global model is deferred to the customers. In each training cycle, the operation falls to a node that the others have elected. SL implementations have been applied to the medical field [61, 62]. In such a sensitive context, it has been shown that SL can push the BlockFL approach further by using smart contracts to protect the model from misleading participants and keep data and learning safe [63]. Nevertheless, some security issues related to computation and leader node selection still remain, but show interest in blockchain-based methods. This direction, which maintains connections with federated learning while providing an innovative communication paradigm, is therefore of interest for further research.
## 2 Materials and Methods
### Experiments and Data Analysis  
In this work, sEMG signals recorded from 20 intact-limb subjects with high SNR made up the source domain, as the source model performance may influence the transfer learning. The target model was established for each amputated subject independently to explore the transfer learning efficacy. All the sEMG signals were segmented into samples of \(200\times 12\), as described in Section 2.3.1. Samples from repetitions 2 and 5 were used for testing, while repetitions 1, 3, 4, and 6 were used for training.  
The source LECNN model was first trained on 20 intact subjects to learn general knowledge of the gesture recognition. Then, eleven models (for there were eleven amputees) were trained using the target training data (i.e., repetitions 1, 3, 4, and 6 of each amputee) without transfer learning, which were then used as the referential models. Finally, the eleven models with the proposed transfer learning strategy were trained using the same target data to improve the recognition performance. Consequently, there were 23 models established in our work: (i) a source model, (ii) eleven subject-specific models without transfer learning (named LECNN-Onlys), and (iii) eleven target CNN models combined with transfer learning (named LECNN-TLs).  
The model performance was evaluated based on the classification accuracy and weighted F1-score (Equation (2). A one-tailed Wilcoxon signed rank test was used to compare the recognition performance of the LECNN-Onlys and LECNN-TLs. Considering that higher average accuracies after transfer could be the result if the strategy worked well on some gestures but not others, the accuracies of each gesture were analyzed. Additionally, since clinical amputation parameters could significantly impact the recognition performance, the correlations of the classification accuracy and two clinical parameters (i.e., the remaining limb percentage and the years since amputation) were investigated using a Pearson's correlation analysis:  
\[\text{F}_{1}\text{-score}=\frac{1}{\sum_{\text{l}\in\text{L}}\big{|}\hat{y}_ {1}\big{|}}\sum_{\text{d}\in\text{L}}\big{|}\hat{y}_{1}\big{|}\text{F}_{1} \big{(}y_{1},\hat{y}_{1}\big{)} \tag{2}\]  
\(\text{F}_{1}\big{(}y_{1},\hat{y}_{1}\big{)}\) is defined as Equation (3):  
\[\text{F}_{1}\Big{(}y_{1},\hat{y}_{1}\big{)}=2\frac{\text{P}\Big{(}y_{1},\hat{y }_{1}\big{)}\times\text{R}\Big{(}y_{1},\hat{y}_{1}\Big{)}}{\text{P}\Big{(}y_{ 1},\hat{y}_{1}\big{)}+\text{R}\Big{(}y_{1},\hat{y}_{1}\Big{)}} \tag{3}\]  
\(\text{P}\Big{(}y_{1},\hat{y}_{1}\big{)}\), \(\text{R}\Big{(}y_{1},\hat{y}_{1}\Big{)}\) are the Precision and Recall defined as Equations (4) and (5):  
\[\text{P}\Big{(}y_{1},\hat{y}_{1}\Big{)}=\frac{\big{|}y_{1}\cap\hat{y}_{1}\big{|} }{y_{1}} \tag{4}\]  
\[\text{R}\Big{(}y_{1},\hat{y}_{1}\Big{)}=\frac{\big{|}y_{1}\cap\hat{y}_{1}\big{|} }{\hat{y}_{1}} \tag{5}\]  
where L is the set of labels, y\({}_{1}\) is the true label and \(\hat{y}_{1}\) is the predicted label.
## 2 Basic Theory of the TPOD Method and the Rotor Model with Coupling Faults
### The Effects of Systematic Parameters
#### 3.2.4 Eccentricity  
Finally, we study the effects of the eccentricity variation to the coupling fault system. Figures 20 and 21 present the comparison of bifurcation and amplitude-frequency of two different eccentricities (0.00015 mm and 0.00012 mm). As is shown in Figure 21, the amplitude increases when the eccentricity magnifies at the frequency of primary resonance. 1/2 sub-harmonic resonance is more obvious near the frequency of \(2\omega_{c}\), it is clear that the annulus of the period-doubling bifurcation is larger. When the frequency is over \(2\omega_{c}\), the amplitude decreases as the eccentricity increases, this result indicates that increasing the eccentricity appropriately can improve the stability of the system under a certain speed. However, in the high frequency region, the bifurcation behaviors will be more complex when the eccentricity increases, in other words, the non-linear dynamic characteristics will be more complex.  
Figure 19: Comparison of the amplitude–frequency curves.  
Figure 18: Bifurcation diagrams for coupled fault system. (**a**) \(k=500\). (**b**) \(k=1000\).  
As is discussed above, the eccentricity of the system has more impact to dynamical characteristic of the amplitude-frequency and bifurcation at the frequencies of primary resonance, \(2\omega_{c},3\omega_{c}\), high frequency. The eccentricity may affect the visualizations of different faults at each frequency range.  
**Remark 5**.: _The systematic parameters play a key role in the rotor models, the variations of the parameters will affect the dynamical characteristics of the rotor-bearing system. We make a preliminary study on the effects of cracked depth, clearance between rotor and stator, stator stiffness, eccentricity to the bifurcation diagrams and amplitude-frequency curves. This research is only a beginning to discuss the variations of the dynamical characteristics with the systematic parameters. We can also use the frequency-spectrum analysis to describe the effects of the variations of the parameters._  
Figure 21: Comparison of the amplitude–frequency curves.  
Figure 20: Bifurcation diagrams for coupled fault system. (**a**) \(b=0.00015\). (**b**) \(b=0.00012\).
# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
## 3 Design Issues in Reinforced Concrete Silos
### Design Factors
#### 3.1.3 Mass Flow and Funnel Flow Design Issues
The researcher observes that the only viable solution to segregation and tilting was to store bulk solids with the same size (no fines or bulkier particles). However, this might be impractical considering that agricultural grains have different shapes and sizes. Beyond the design codes, the selection of a silo design is influenced by the following parameters; permeability, wall friction, compressibility, internal friction, and cohesive strength. A major limitation is that the design of the hoppers is not influenced by the types of grain stored despite the fact that different types of bulk grains have distinct interval voids ratio, cohesion, internal angles of friction, internal bulk density, coefficient of wall friction and the Poisson's ratio, that directly influence the structural integrity of the walls [38]. The relationship between flow, lateral pressure, and the internal angle of friction are depicted in Table 7. Additionally, the bulk solids have different rates of water absorption and moisture content (hydration properties) [69], that should be taken into account during the design of silo hoppers. The variability in the hydration properties of different grains is linked to unique molecular absorption, capillary absorption, storage conditions, and storage conditions (relativehumidity) [70]. In brief, the design of silo hoppers is based on key assumptions due to the wide variability of silo hopper designs. According to new research evidence by [PERSON] [47], the proposed design solutions outlined above might have limited efficacy in mitigating the design risks due to the following factors. First, the flow of the bulk solids follows an undefined pattern of flow compared to gases and liquids [47]. The net effect of this phenomenon is the uneven transfer of the initial fill and flow loads within the structure, leading to the generation of significant shear stress on the walls of the hopper and other compartments that come into contact with the grains in both dynamic and static conditions. The change in pressure profile varies along the column of the hopper from linear to exponential forms [47]. The design-induced failure factors and repair considerations are discussed in Sections 3 and 4.