esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 XAI Approaches for Chest X-ray Image Analysis and Report Generation
### Attention-Based Explanation  
In the field of deep learning, the concept of attention has attracted a lot of interest due to its powerful influence on the learning ability of deep neural networks. Studies have been conducted on developing attention-based models that can explain decisions made by neural network models, allowing humans to trust these decisions. Attention is undoubtedly one of the most powerful ideas in the field of cognitive science. Attention focuses on relevant features of input data while fading out the non-relevant ones. Attention allows a neural network to spend more computational power on the relevant features, which represent the critical portions of the data as shown in Figure 4.  
Figure 3: Conceptual view of guided gradient class activation mapping. Guided Grad CAM combines gradient information from the last convolutional layer and guided backpropagation for creating high resolution heat maps. Using attention, a neural network can focus on valuable portions of the input and learn the relationships between them. The concept of attention is implemented in natural language processing (NLP) systems through transformers [26], which have revolutionised the field of NLP. Medical report generation is an NLP problem which will be discussed in the next section. In image analysis, the notion of attention is incorporated in ConvNets using attention modules. In the multi-label chest X-ray image classification problem, the discriminative features of different pathologies must be learned. In general, chest X-rays could contain information of various anomalies, so critical clues are required to classify and localize the different abnormalities in lung regions. Attention has been used with Fully ConvNets (FCNs) in [7; 54] to design a multi-attention convolutional neural network for automatic disease detection in chest X-ray images. Fully ConvNets are an adaptation of the DenseNet-121 model that can process spatial information [54]. FCNs create multiple attention maps for each pathology category being considered via a collection of correlated convolutions followed by a mean-pooling process. Because each channel in an image shows a specific visual symptom for a disease class, the channels have the ability to represent huge intra-class variability. This intra-class variability helps to generate explanations using heat maps based on spatial attention maps.
# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 2 System Model
### Radio Access Network Domain
The packet size for each slice is denoted by \(\omega_{e},\omega_{u},\omega_{np}\), respectively. The packet arrival rate for each slice is denoted by \(\lambda_{e},\lambda_{u},\lambda_{np}\), respectively.
## 5 Illustrative Example
### Identification of GIVs
In the case of variant 3 (see Figure 5), GIVs related to _segmentation_, _simple copies_ and _arrangement in advance_ are similarly exploited as in the case of variant 1 but reflected in a different concept. The GIV requiring _replacement of a rigid system with a more flexible one_ is introduced in variant 3 in the increase of the degrees of freedom of the supporting platform for the working piece and the solution of posing the working tools. For this variant, granularity is \(g_{r}\)= 63% (\(n_{f}\) = 5, \(n_{m}\) = 8), as in the case of variant 2, and the reconfiguration time is \(t_{r}\) = 72 min (\(t_{m}\) = 20 min, \(m_{i}\) = 0, \(m_{a}\) = 3). In the case of variant 3, reconfiguration requires the addition of two modules for drilling, a module for piece orientation and removal of two modules (piece orientation and drilling). Here, the reconfiguration time \(t_{r}\) exceeds the target (120 min). The SCV algorithm considers the target as being also the effective value introduced in Equation (4) for calculating the utility function \(T\). A software application written in Java was developed to calculate the utility function \(T\) from Equation (4) and the convergence index from Equation (5) for each particle, as well as for performing all operations from Equations (8) and (9). For example, a screenshot from this application highlighting the results after four iterations of the SCV algorithm is shown in Figure 12. Making the calculations (see Equation (4)), at iteration 1 the utility function \(T\) for particle 1 is \(T_{1}\) = 0.88, for particle 2 is \(T_{2}\) = 0.94, and for particle 3 is \(T_{3}\) = 0.98. Thus, the maximum value after iteration 1 is attained by particle 3. The convergence index, in this case, is 0.02 (Equation (5)).
## 8 Restrictions and Future Work
### Future Work  
Considering the aforementioned limitations, future research should focus on the following directions:  
1. Enhanced Data Collection and Management: Developing a standardized safety investment accounting system across the mining industry would facilitate more accurate and comprehensive data collection. This, in turn, would improve the reliability and generalizability of the model.
2. Exploring Advanced Modeling Techniques: Adopting more complex machine learning algorithms, such as Random Forest or Support Vector Machines, could enhance the model's fitting ability and prediction accuracy. Additionally, exploring the integration of emerging technologies like federated learning could enable collaborative learning across multiple mining sites without sharing raw data, further improving the model's generalizability.
3. Practical System Development and Integration: Developing a practical application system based on the digital twin five-dimensional model and integrating it with existing mining operations would be a crucial step towards overcoming implementation barriers. This system should be designed to adapt to various mining environments and regulatory frameworks, ensuring its widespread applicability.
4. Continuous Model Validation and Optimization: Continuously validating and adjusting the model in practical applications is essential to ensure its effectiveness in changing environments and data characteristics. This process should involve collecting feedback from industry stakeholders, identifying and addressing emerging challenges, and incorporating new data and insights to refine the model's predictive accuracy and generalizability.
###### Abstract
Commonly used surrogate models include response surfaces [12, 13], support vector machines [14, 15], neural networks [16, 17], and the Kriging model [18, 19, 20]. This paper focuses on the Kriging model due to its validity being independent of the existence of random errors, and it has a good fitting effect for the problems of high nonlinearity and sudden changes in the local response. The Kriging surrogate model is used for global and local approximations [21, 22, 23, 24, 25]. When it comes to the pyrotechnic igniter, however, the current method to evaluate the reliability of the pyrotechnic igniter is mainly through ignition tests, which are time-consuming and expensive. To our knowledge, there are few numerical studies on the ignition reliability analysis of pyrotechnic igniters. Therefore, it is essential to develop an efficient and accurate numerical way to conduct the ignition reliability analysis for the pyrotechnic igniter. Based on coupling the interior ballistic model and Kriging model, this paper proposes an ignition reliability analysis procedure of the pyrotechnic igniter concerning the failure mode of unstable combustion at low temperature. The method is applied to evaluate the reliability of a pyrotechnic igniter and is verified by comparing reliability results with that of MCS. The importance ranking of design variables extracted from propellant combustion parameters, geometric size, and material properties are obtained with sensitive analysis to assist the pyrotechnic igniter design and optimization. The remainder of this paper is organized as follows. In Section 2, the working principle of the pyrotechnic igniter is introduced, and the function failure mode of a pyrotechnic igniter is defined to establish an implicit function. Section 3 conducts the function deterministic simulation and failure modes analysis of the pyrotechnic igniter based on the interior ballistic model. In Section 4, the Kriging model method is constructed to display the function, and the effects of independent random parameters on reliability and reliability sensitivity are developed. Then, by comparing with the results of MCS, the validity of this method is verified. To validate the result of reliability sensitivity, igniting experiments were performed. Finally, the conclusions are provided in Section 5.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 4 Algorithm Description
### Routing  
Due to the dynamic nature of underwater nodes, establishing a good complete path directly from the sink is quite inapplicable. Therefore, communication between clusters needs to select the next hop forwarding node in real time using the information within the communication range of nodes. At this point, the selection of the best forwarding node needs to consider the remaining energy of the forwarding node and its distance from the sink.
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 4 Proposed Resource Management Solution
### Local Decision Process  
Equation (1) points out that the transmission energy consumption of an implanted sensor is related to the communication distance. To minimize the energy consumption of the implanted sensors, the relay selection algorithm is proposed as:  
\[\psi(i)=\frac{\sqrt{\left|\left|\mathbf{w}_{k}-\mathbf{w}_{i}\right|\right|^{2}}}{E_{ i}-E_{i}^{min}}, \tag{24}\]  
where \(E_{i}\) is the initial energy of relay \(i\) and \(E_{i}-E_{i,min}\) is the residual energy status of relay \(i\). The relay with the minimal value of \(\psi(i)\) will be elected as the best relay. Relay \(i\) receives task \(C_{i}\) from the implant sensor while simultaneously executing the on-body task \(D_{i}\). If relay \(i\) can execute task \(C_{i}\) by itself, then we employ Equations (4)-(8). Numerous key QoS metrics are obtained and one round is finished. If relay \(i\) cannot executes task \(C_{i}\) by itself according to \(U_{i}\), it will trigger the offloading process, and task \(C_{i}\) will be offloaded to another relay. This method is summarized in Algorithm 1.  
```
The relay and implanted sensor sets are denoted as \(\mathcal{N}\) and \(\mathcal{K}\). Initialization: each relay is assigned a unique ID;  select the \(x\)-th relay as the target to connect;  for a specific implanted sensor \(k\);  current round \(r\);  current residual energy status \(E_{res}\); For\(i\in\mathcal{N}\)do  calculate the distance between the implanted sensor and the \(i\)-th relay: \(\sqrt{\left|\left|\mathbf{w}_{k}-\mathbf{w}_{i}\right|\right|^{2}}\)  calculate the relay's residual energy: \(E_{i}-E_{i}^{min}\); If\(\psi(x)\leq\psi(i)\)then // do nothing, \(x\) is still the best choice so far; Else \(x=i\); // \(i\)-th relay becomes a better choice; End if Endif  endfor  establish a link between the implanted sensor and the \(x\)-th relay; Update\(\mathbf{E_{res}}\),\(\mathbf{r}=\mathbf{r}+\mathbf{1}\)
```  
**Algorithm 1** Proposed Iterative Relay Selection Method
## 3 Results  
### Hardware Testsetup  
In this section the hardware test setup we utilize for our experiments is introduced. It is shown in Figure 13 and represents a basic two 1-U satellite setup. Both units are joined physically and are sharing the same Electric Power Supply (EPS) unit for simplicity reasons. The upper unit contains three wireless connected STM32F4 SKITH nodes, which are described in Section 2.2. The SKITH nodes can be reconfigured depending on the scenario to run either exclusively actuator-, sensor- or controller SW units, acting in exclusive node mode or to run a multi-purpose mix of SW units if the node is configured as a combined node. If the node is in exclusive mode, the node represents the single type of SW unit it is running, e.g., it represents an actuator node if it runs an actuator SW unit. In combined node configuration the otherwise external physical network used for the data exchange between the SW units of different type running on the node is simulated using the Markovian network model introduced in Section 2.3. Using the publisher/subscriber and Hardware-Abstraction-Layer (HAL) architecture of RODOS allows a transparent transfer of a SW unit from an exclusive to a combined HW node. Combined nodes have the advantage that multiple SW units, e.g., multiple controllers, actuators and sensors could be run on a single HW with more control over the performance influencing parameters like the network between those units. However this advantage comes at the cost of higher load on the HW and influence of network modelling errors. The lower unit contains the EPS used to power all HW nodes and another two configurable nodes that are wirelessly connected with the nodes in the upper unit. The two units are stacked on an electrical interface board which allows monitoring of all HW involved from an external PC.  
Figure 12: Diagram of the networked control loop architecture extended to deal with controller losses.
## 3 Proposed Skyline Query Processing Method Based on DCS  
### Problem Definition  
In this paper, we propose a skyline query processing method based on DCSs which geographically order sensor readings across a sensor network. As shown in Figure 1, finding skyline query results in such DCSs looks like a straightforward problem. If all sensor nodes store sensor readings, we only need to scan boundary sensor nodes such as sensor nodes 4, 9, 14, 19, 20, 21, 22, 23 and 24 to find the most polluted area. In DCS, some sensor nodes may or may not have stored data in the sensor network. Therefore, we need some additional data structures for each sensor node and a node traverse algorithm to search for correct query results. It is also important to reduce the number of messages for query processing. When broadcasting query processing messages to neighbor nodes, some unnecessary messages can be transmitted, and a sensor node may receive same messages repeatedly. We carefully take into account the mentioned problems in our proposed skyline query processing method. Our proposed query processing method will be described in detail in the next subsection.
# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
## 2 Related Work
### Sentiment Analysis vs. Deep Emotion Modelling  
Contemporary research studies have advanced beyond sentiment as basic polarity to detect extended categories of emotion. Unsupervised approaches such as word embeddings have been used recently for emotion detection and to understand the distributed representations of words based on syntactic and semantic word relationships [21; 22; 23]. Several studies have published emotion lexicons that combine general expressions for each emotion that can be used to categorize texts [12; 24; 25]. Moreover, many studies have developed supervised machine learning models to predict the category of emotions on Twitter and online reviews related to tourism and hospitality that depend on previously annotated data [6; 26]. In this space, deep learning models and language models have been extensively used to extract sentiments from online hotel reviews [27; 28]. However, most of the approaches have focused on only positive and negative sentiments and are thus incapable of exploiting the richer meanings of deeper human emotions ingrained in the reviews. For example, the emotions 'anger' and'sad' are classified as 'negative sentiments', although they convey vastly different feelings. [PERSON] et al., in their recent research, outline that sentiments accentuate only the broad category of emotions as positive, negative and neutral; therefore, future studies should pursue creating a more granular form of emotion representation [28]. The identification of the emotions of customers at a granular level from their online reviews is crucial to properly exploit the power of social media [29]. These models rely on previously annotated data to train the machine learning model and lack deeper exploration of the derived emotions which could be used for decision-making.  
A significant improvement in the current state of the art using sentiment analysis in online reviews is the capture and modelling of deep emotions with quantifiable levels of intensity. This facilitates underlying (even subtle) variations in opinions and preferences in customer experiences to be represented and highlighted, which will provide deeper insights into the causality of reviews in quantified form.
# Effects of Age and Sex on the Kinematics of the Sprinting Technique in the Maximum Velocity Phase
## 4 Discussion
The more upright body position in prepubescent athletes should be examined together with the orientation of the pelvis. It is suggested that the orientation of the pelvis has an impact on the locomotion of the legs [70]. A backward orientation of the pelvis is not favorable for developing maximum-speed sprinting [70], and thus constitutes a technique element affected by age. Another factor affecting sprinting performance may be adults' lower antagonist activity, which was beyond the aims of this study. Initially, antagonist activity was proposed as a mechanism that reduces shear forces and enhances joint stiffness and stability [71]. Studies in single-joint movements have shown no differences in the coactivation level for the knee [72; 73] and the elbow muscles [74] but higher level of coactivation in the plantar flexors in children [75]. However, in complex movements such as walking or running [76], children tend to demonstrate higher values of coactivation indicating that during dynamic and multi-joint movements, children rely more on simultaneous activation of agonist and antagonist muscles to achieve joint stabilization and control. This higher coactivation of children observed during both the braking and propulsive phases of jumps has been attributed and related to coordination challenges or the immaturity of the neuromuscular system [77] and may have a negative impact on their sprinting performance as well. The time required to achieve maximal propulsive force may also be a contributing factor. Adults reach their maximum force output more quickly than children in SSC actions [67; 68] and this probably enables them to generate maximal force at a faster rate during sprinting as well. Rate of force development is highly related with the muscle-tendon unit properties. The muscle-tendon unit should be optimally stiff to store elastic energy and to release it efficiently during the propulsive phase in SSC actions. Children present lower muscle tendon stiffness compared to adults [78], resulting in prolonged contact times in SSC actions, as also found in this study.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
###### Abstract  
The Ad Hoc On-demand Distance Vector (AODV) is a routing protocol for mobile ad hoc networks (MANETs) and other wireless ad hoc networks. The vanilla AODV protocol is simple and easy to implement because it only uses the hop count as a routing metric. Single-metric route determination also causes problems, such as network congestion and energy exhaustion, which limit the usage of AODV in resource-limited applications. To solve these problems, the authors propose a new routing protocol that combines the analytic hierarchy process (AHP), the entropy weight method (EWM), and AODV. The proposed protocol uses energy, congestion, and the hop count as metrics and weights these three metrics using AHP and EWM. To address the importance of energy in applications, such as drones, the proposed protocol chooses different comparison matrices for AHP at different node residual energy levels. Finally, the node chooses the best route link according to the score (sum of weighted metrics). It is also suitable for wireless sensor networks because the proposed protocol considers the residual energy of the node. The simulation results show that the improved routing protocol can effectively reduce the average end-to-end delay and energy consumption and prolong the lifetime of the whole network.  
mobile ad hoc network; analytic hierarchy process (AHP); entropy weight method (EWM); route link selection; AODV
###### Abstract  
Free-space optical (FSO) communication can be subject to various types of distortion and loss as the signal propagates through non-uniform media. In experiment and simulation, we demonstrate that the state of polarization and degree of polarization of light passed though underwater bubbles, causing turbulence, is preserved. Our experimental setup serves as an efficient, low cost alternative approach to long distance atmospheric or underwater testing. We compare our experimental results with those of simulations, in which we model underwater bubbles, and separately, atmospheric turbulence. Our findings suggest potential improvements in polarization based FSO communication schemes.  
polarization; DOP; optics; turbulence; FSO; simulation; SOP; COMSOL
## 5 Conclusions  
As a study focusing on the work transference network (WTN) for the workflow knowledge discovery, our contributions in this paper include the following:  
* Formal definitions and procedure of the framework for discovering a WTN from event logs.
* Brief description of the implemented system of the framework that can handle event logs of different timestamps.
* Experimental analytics of the discovery of WTN using real-life event log dataset.  
Based on the contributions above, we confirmed that the proposed framework and its implemented system are valid in discovering WTNs and acquiring primitive knowledge. To recap, the information and knowledge that the proposed framework can provide are listed as follows:  
* A structure of the WTN observed during a long-term period (from accumulated event logs).
* A structure of the WTN for a single workflow instance (from an event trace).
* Total numbers of performers and activities associated with certain workflow processes and their occurrences.
* Patterns showing how work transference relationships are made (e.g., self-transferring).
* Degree of strength in terms of the work transference between two performers.  
Conclusively, we consider this study as a meaningful step towards the workflow fidelity assessment from the organizational aspect that has not been supported with a systemic way. Despite the feasibility of the framework, our work is still lacking regarding the suggestion of possible and attractive applications for business analysts. Therefore, as future study, we need to improve our system to provide more sophisticated analytical capabilities to effectively discover and deliver more valuable knowledge. In addition, we have a plan to conduct a case study in which we will apply our system to massive real-life event logs to verify how beneficial the discovery framework is to workflow-supported organizations.  
[PERSON] and [PERSON] conceived and designed the framework. [PERSON]-L.P. implemented the mining system. [PERSON] and [PERSON]-L.P. performed experiments and analyzed the results. All authors have contributed in writing and proofreading the paper.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT, Grant No. NRF-2018R1C1B5086414). Also, this work was partially supported by Basic Science Research Program through the NRF of Korea funded by the Ministry of Science, ICT & Future Planning (Grant No. 2017R1A2B2010697).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline Involved activities & 29 & 27 & 24 & 23 & 22 & 21 & 20 & 19 & 18 & 17 & 16 & 15 & 14 \\ Work transferences & 2 & 1 & 1 & 1 & 2 & 5 & 5 & 8 & 6 & 5 & 7 & 4 & 9 \\ \hline Involved activities & 13 & 12 & 11 & 10 & 9 & 8 & 7 & 6 & 5 & 4 & 3 & 2 & 1 & Total \\ Work transferences & 8 & 12 & 15 & 23 & 27 & 43 & 71 & 148 & 247 & 435 & 740 & 1560 & 2526 & 5911 \\ \hline \end{tabular}
\end{table}
Table 2: Numbers of different cases of work transferences and their involved activities.