esa-sceva/satcom-chunk-collection huggingface dataset:
# Domain Model Based Design of Business Process Architectures
## 4 Foundations
### Entity-Centric Process Modeling  
A domain model represents the structure of a domain by showing its main concepts and how they relate to one another as classes and associations, respectively [42], e.g., Figure 3. Complementary to domain models, object lifecycles (OLCs) allow representing possible behavior for instances of domain model classes as states and transitions [43],e.g., Figure 4. Often, the integration of these perspectives within process models tends to be an afterthought. In fact, data modeling is not a goal of BPMN 2.0 [39].  
In the traditional activity-centric approach to process modeling, business processes are elicited based on activities, e.g., [2; 9; 10]. An alternative to this is the elicitation of business processes according to the entity-centric paradigm, e.g., [44; 45; 46]. In entity-centric approaches, process modeling is based on the construct of a _business entity_: a relevant real-world concept handled by the organization [36], e.g., a guest check in a restaurant. A business entity can be represented as a domain model class with an associated OLC depicting how the business entity behaves [47].  
The dBPA method contends, following [8], that based on the entity-centric paradigm, domain models may be used as inputs for the design of a BPA. This is analogous to the use of domain models as first-class citizens in Model-Driven Engineering (MDE) for software engineering [48]. The applicability of the dBPA method is facilitated by the availability of domain models, either by having them already developed within the organization  
Figure 4: Object lifecycles.  
Figure 3: Domain model (**left**) and data dictionary (**right**).  
(e.g., in the context of an MDE project) or by the availability of such models in the literature (e.g., [49; 50; 51; 52]).  
Unlike the dBPA method, prior entity-centric BPA design approaches lack systematic guidance for the use of domain models for building BPA models. The position of the dBPA method regarding business entities and integration of domain models and OLCs is presented in the following.  
First, regarding the identification of business entities and relations:  
* Each domain model class defines, at most, one business entity.
* Each domain model association defines one or more relations between the object lifecycles (OLCs) of the partaking classes.  
Second, integration of domain models and OLCs involves specializing the domain model classes with dynamic hierarchies (in a dynamic hierarchy, objects are allowed to migrate between sub-classes during their lifetime in the system [53], which applies when sub-classes are defined as possible states of the super-class) such that:  
* A dynamic hierarchy is built by specializing the original class with sub-classes that hold a bi-univocal relation with the OLC states.
* The properties of the hierarchy are defined in a way that they are consistent with the organization of OLC states.
# Medical Devices for Tremor Suppression: Current Status and Future Directions
## 3 Electrical Stimulation Systems
### Median and Radial Nerve Excitation
The results, based on 205 patients who completed the study, showed that TENS treatment via Cala Trio resulted in significant improvements in both the TETRAS and subject-rated Bain and Findley ADL scores (\(p<0.0001\)) [71]. Among the 193 patients included in the secondary analysis, 54% experienced a \(\geq\)50% reduction in tremor amplitude [71]. However, 14 patients did not respond to the therapy, suggesting that not all patients with ET will benefit from Cala Trio [71]. It is important to note that the open label, single-arm design of the PROSPECT trial limits the generality of Cala Trio's effect; therefore, future studies with more robust designs (e.g., RCTs) would be valuable to assess its efficacy. Device-related adverse events were mild to moderate in severity. Nonserious adverse events were observed in 18% of patients, including skin irritations (redness, itchiness, and/or swelling); soreness or lesions; and discomfort (stinging and/or sensation of weakness) or burns [71]. These adverse events were all resolved with the use of a topical ointment, decreased stimulation intensity, or discontinued therapy [71]. Contraindications to the use of Cala Trio include having currently implanted electrical medical device (e.g., pacemaker, defibrillator, and deep brain stimulator), suspected or diagnosed epilepsy or other seizure disorders or pregnancy. This device should also not be applied on skin eruptions, open wounds, cancerous lesions, or swollen/infected/inflamed areas. A 50% reduction of tremor amplitude is comparable to the first-line propranolol and primidone pharmacokinetics [17], which are considered clinically useful for the treatment of ET [19]. Cala Trio can play an important role in patients who are not eligible for surgical intervention or do not respond to pharmacotherapy. It has a similar, favorable safety profile to Cala ONE, whose risk and benefit determination met the FDA's requirements. This device is noninvasive, with 85% of patients reporting its convenience and ease of use [71]. Currently, it is uncertain whether Cala Trio could reduce or replace the need of medications in the treatment of ET. Thus, physicians need to evaluate how it will fit along with pharmacotherapy and/or lifestyle interventions for each patient with the consideration of tremor severity. Post-approval studies could address this question and provide further insights into the long-term safety and efficacy of Cala Trio.
# Distributed Bragg Reflectors for GaN-Based Vertical-Cavity Surface-Emitting Lasers
## 2 Fabrication Techniques and Design Considerations of Distributed Bragg Reflectors (DBRs)
### Optical Design Consideration
In the practical implementation with a finite number of DBR pairs, the actual \"usable\" reflectance bandwidth, \(\Delta\lambda_{\text{usable}}\), for a high performance VCSEL (for example for R \(>\) 0.995) can be a factor of 2 to 3 times narrower than \(\Delta\lambda_{max}\). Figure 2a provides a close-up plot of the evolution of spectrums in Figure 1a, focusing on the high reflectance portion. As the number of pairs increases, the peak of the reflectance spectrum will reach a practical threshold (say, 0.995) above which \(\Delta\lambda_{usable}\) becomes greater than 0. As the number of DBR pairs continues to increase, \(\Delta\lambda_{usable}\) gradually increases toward the respective \(\Delta\lambda_{max}\), defined by Equation (2). Such a transition of \(\Delta\lambda_{usable}\) is plotted in Figure 2b here for two lattice-matched material systems, AlAs/GaAs and AlInN/GaN. The large difference of \(\Delta\lambda_{usable}\) in Figure 2b can be explained by Equation (2) due to approximately a 2\(\times\) difference in wavelength and almost 3\(\times\) difference in \(\Delta n\). Figure 2: (**a**) A close-up of the reflectance spectrums near the peak as the number of pair increases. Here a threshold of R \(>\) 99.5% is used above which the portion of the reflectance spectrum is defined as “usable” with a width of \(\Delta\lambda_{usable}\). (**b**) Plot of \(\Delta\lambda_{usable}\) vs the number of DBR pairs for two lattice matched material systems, AlAs/GaAs and AlInN/GaN. The large difference of \(\Delta\lambda_{usable}\) is due to mainly a 2\(\times\) difference in wavelength and almost 3\(\times\) difference in \(\Delta n\).
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 4 CNN Model for Arabic Short Vowels Classification
### Hyperparameters Tuning  
In deep learning neural networks, the function of optimizers [46] is to reduce the losses to achieve the most accurate results possible. We evaluated different optimizers: Adam, Nadam, RMSprop, and SGD, and found Adam [47] to achieve the best performance in the proposed network. Adam stands for adaptive moment estimation that adaptively estimates the first and second-order moments. It updates the network weights iteratively based on the training data. Specifically, Adam uses the update vector \(\hat{v}_{t}\) and the past gradient \(\hat{m}_{t}\) differently than the previous algorithms, shown in Equation (2):  
\[\theta_{t+1}=\theta_{t}-\frac{\eta}{\sqrt{\hat{v}_{t}}+\epsilon}\hat{m}_{t} \tag{2}\]  
Here \(\theta_{t}\) represents the weights and bias parameters, \(\eta\) stands for the learning rate or step-size, \(\hat{m}\) and \(\hat{v}\) represent the first and second moment vectors and by parameterizing them by \(t\) we get their moving averages over time. Adam combines the advantages of AdaGrad and RMSProp algorithms [26] and compared to other optimizers, it requires less memory, is computationally efficient, and is suited for problems with large data or many parameters. We utilized the learning rate scheduler to dynamically adjust the learning rate to achieve the desired accuracy. Initially, the learning rate was set to 0.001, and after 80 Epochs, the learning rate was reduced by 10% every Epoch. We continuously reduced the learning rate because it helped in achieving optimal weight update by gradually maintaining the training loss and avoiding its oscillating over training epochs [48]. We utilized the early stopping hyperparameter for stopping the model prematurely if the loss was not decreasing anymore. We also used the Tensorboard tool to get training details for analysis and further improvements to the model through the generated time-series graphs, histograms, and distributions [49]. We added 2 batch normalization and 1 dropout layer to this model. These steps are explained in Algorithm 1, where, labeled audio dataset is input to the model that output as training loss, accuracy, and predicted labels. The audio data was first converted to a labeled waveform dataset. Then the transformed to labeled spectrogram dataset. The spectrogram dataset was resized to 32 \(\times\) 32 pixels and input to our optimized sequential DLNN model. The model was executed by passing the callback functions of learning rate scheduler (tr_scheduler), early stopping (callback_Early_stopping), best model checkpoint (model_checkpoint_callback), and tensorboard (tensorboard_callback). Together, hyperparameter tuning and model fine-tuning helped in achieving the testing accuracy of 95%.
## 6 Model Building and Analysis
### Fuzzy Logic Modelling
#### 6.3.1 Hierarchical Fuzzy Expert System  
The hierarchical fuzzy model consists of sub-models, which correspond to the three main categories. In addition to the sub-model, there is one more model that combines the outputs of these sub-models in order to generate the rank of the organization. The fuzzy structure of each of the models is identical. The membership function assigned to input variables and the knowledge-based rules differ. When dealing with a high number of variables, as in this study, the total number of shortlisted independent variables is 20, developing a single layer fuzzy model is not recommended.  
The qualitative variables were measured on a different scale compared to quantitative variables. For example, the annual growth rate was measure in percentage, and Entropy was measured in fractions or decimal values. Assigning weight to the membership on one layer is extremely difficult. The weight for an individual factor can be used for comparison within the same category. However, a sub-factor from the non-financial category cannot be compared to sub-factors from the financial category.  
Additionally, for every variable, there should be 7-9 rules. To build a one-layer fuzzy model, the minimum number of rules required is more than 140 rules for 20 variables. The hierarchical model allows us to work with fewer rules. The sub-model for this study will be built using only 7-8 factors [47]. With a usable data set of 75 responses from the survey, the hierarchical system allows us to develop the prediction model. The set of responses required for knowledge-based rule training and testing is satisfied.  
There are three models--two sub-models for financial and non-financial factors, and the third model for the second layer of the fuzzy model--which combine inputs of financial, non-financial, and market diversification factors, as shown in Figure 10. Since there is only one factor under market diversification, a separate sub-model will not be developed. The steps involved are as follows.  
Figure 9: Shortlisted qualitative variables.
# Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation
## 2 Motion Attitude Analysis Based on the MDH Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(i\) & \(a_{i-1}\) (mm) & \(\alpha_{i-1}\) (°) & \(d_{i}\) (mm) & \(\theta_{i}\) (°) \\ \hline
1 & 0 & 0 & 0 & 0 \\
2 & 0 & \(-90\) & 0 & \(-90\) \\
3 & \(L_{1}\) & 0 & 0 & 0 \\ \(h(4)\) & \(L_{2}\) & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: D-H parameter table of the supplied material device. Figure 5: MDH coordinate system model. \[\frac{1}{2}\mathrm{T}= \begin{bmatrix}\cos\theta_{2}&-\sin\theta_{2}&0&0\\ 0&0&1&0\\ -\sin\theta_{2}&-\cos\theta_{2}&0&0\\ 0&0&0&1\end{bmatrix}  \tag{3}\]  
\[\frac{2}{3}\mathrm{T}= \begin{bmatrix}\cos\theta_{3}&-\sin\theta_{3}&0&\mathrm{ L}_{1}\\ \sin\theta_{3}&\cos\theta_{3}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}  \tag{4}\]  
\[\frac{3}{4}\mathrm{T}= \begin{bmatrix}\cos\theta_{4}&-\sin\theta_{4}&0&\mathrm{ L}_{2}\\ \sin\theta_{4}&\cos\theta_{4}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}  \tag{5}\]  
The positional attitude transformation matrix of the mechanical tongs relative to the base coordinates, using Formulas (2)-(5), is as follows:  
\[\frac{0}{4}\mathrm{T}=\prod_{i=1}^{4}\mathrm{i-1}\mathrm{T}=\begin{bmatrix} \begin{array}{ccc}0&\mathrm{R}&0\\ 4&\mathrm{R}&4\\ 0&0&0&1\end{array} = \begin{bmatrix}\mathrm{T}_{11}&\mathrm{T}_{12}&-\sin \theta_{1}&\mathrm{L}_{1}\cos\theta_{1}\cdot\cos\theta_{2}-\sin\theta_{1}\\ \mathrm{T}_{21}&\mathrm{T}_{22}&\cos\theta_{1}&\mathrm{L}_{2}\sin\theta_{1} \cdot\cos(\theta_{2}+\theta_{3})\\ \mathrm{T}_{31}&\mathrm{T}_{32}&0&\mathrm{L}_{2}\sin(\theta_{2}+\theta_{3})- \mathrm{L}_{1}\sin\theta_{2}\\ 0&0&0&1\end{bmatrix}  \tag{6}\]  
In the formula,  
\[\mathrm{T}_{11}=\cos\theta_{4}\cdot\cos\theta_{1}\cdot\cos(\theta_{2}+\theta _{3})-\sin\theta_{4}\cdot\cos\theta_{1}\cdot\sin(\theta_{2}+\theta_{3});\]  
\[\mathrm{T}_{12}=-\sin\theta_{4}\cdot\cos\theta_{1}\cdot\cos(\theta_{2}+\theta _{3})-\cos\theta_{4}\cdot\cos\theta_{1}\cdot\sin(\theta_{2}+\theta_{3});\]  
\[\mathrm{T}_{21}=\cos\theta_{4}\cdot\sin\theta_{1}\cdot\cos(\theta_{2}+\theta _{3})-\sin\theta_{4}\cdot\sin\theta_{1}\cdot\sin(\theta_{2}+\theta_{3});\]  
\[\mathrm{T}_{22}=-\sin\theta_{4}\cdot\sin\theta_{1}\cdot\cos(\theta_{2}+\theta _{3})-\cos\theta_{4}\cdot\sin\theta_{1}\cdot\sin(\theta_{2}+\theta_{3});\]  
\[\mathrm{T}_{31}=-\cos\theta_{4}\cdot\sin(\theta_{2}+\theta_{3})-\sin\theta_{4 }\cdot\cos(\theta_{2}+\theta_{3});\]  
\[\mathrm{T}_{32}=\sin\theta_{4}\cdot\sin(\theta_{2}+\theta_{3})-\cos\theta_{4 }\cdot\cos(\theta_{2}+\theta_{3}).\]  
The MATLAB R2016b robotic toolbox can be used to construct a motion model of the supplied material device to obtain the spatial position of the terminal executor at its initial state, as shown in Figure 6.  
Figure 6: Initial spatial position of the mechanical tongs.
## 5 Decentralized Decision-Making
### Decentralized Decision-Making Mechanisms
#### 5.3.5 Federated Consensus  
Using cooperation among several distinct groups, federated consensus methods offer a fresh method to reach distributed consensus in blockchain networks. Unlike conventional consensus systems like proof-of-work (PoW) or proof-of-stake (PoS), in which every network member validates transactions and adds fresh blocks to the blockchain, federated consensus lets a subset of trustworthy entities take part in the consensus process. Fundamentally, federated consensus is a federation--a collection of pre-selected nodes or validators in charge of attaining consensus on the blockchain's state of affairs. Usually selected according to criteria including reputation, dependability, and experience, these validators work together to reach distributed consensus [77]. Comparatively to conventional consensus systems, federated consensus has two major benefits: scalability and efficiency. Federated consensus is well suited for applications needing high speed and scalability, as it may achieve better transaction throughput and reduced latency by restricting the number  
Figure 14: DAOs architecture.  
of validators engaged in the consensus process. Furthermore, since federated consensus systems do not need significant computing capability to evaluate transactions and network security, they can be more energy-efficient than PoW-based consensus systems. For use cases where energy consumption is a factor, including sustainable finance or environmental preservation, federated consensus especially appeals.  
However, federated consensus processes encounter difficulties and compromises, regardless of their benefits. An essential obstacle is the attainment of decentralization while preserving the efficacy of the consensus process. Centralization may occur in federated consensus when the federation becomes too small or if the criteria for selecting validators are not strong enough. Moreover, federated consensus techniques are vulnerable to collusion or manipulation by malevolent entities present inside the federation. Validators have the potential to conspire in order to suppress transactions, influence the consensus process, or partake in other types of nefarious activities, which can undermine the security and integrity of the blockchain network [78].  
In order to reduce these concerns, federated consensus methods frequently incorporate strategies such as rotating validator roles, randomly selecting validators, and employing cryptographic techniques to guarantee fairness, transparency, and security during the consensus process. Furthermore, continuous research and development efforts are now being conducted to investigate novel methodologies and enhancements to federated consensus mechanisms, with the aim of overcoming existing limits and bolstering their resilience and decentralization.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 8 Conclusions  
This work has presented performance issues in differentially private federated learning models running in different mobility scenarios. The accuracy performance and level of protection have been evaluated by considering three factors: learning models, vehicle mobility, and a privacy algorithm. The accuracy of the privacy-enhanced learning models was mostly affected by vehicles' mobility and the traffic situations on roads. It was also observed that the conventional learning model widely used in federated learning did not work well in the mobile, vehicular environment, especially when enhanced with a privacy method. This is mainly attributed to the performance of the participant selection mechanism, which is highly affected by vehicles' mobility. An additional observation is that the adaptive clipping method applied in this work is more useful than prior methods because it guarantees model performance and the target degree of privacy preservation and thus can proceed with model learning.  
This study focused on privacy and the associated trade-offs in mobility-based federated learning. We plan to consider a weight pruning method, which is a recently proposed alternative to membership inference attacks, to enable more efficient and accurate learning while also showing improved security results. Future works will also examine how well privacy-enhanced learning models protect data against variants of inference attacks and expand on experiments by considering more mobility scenarios.  
Conceptualization, E.-j.K. and E.-K.L.; Methodology, E.-j.K. and E.-K.L.; Software, E.-j.K.; Validation, E.-j.K.; Investigation, E.-K.L.; Resources, E.-K.L.; Data curation, E.-j.K.; Writing--original draft, E.-j.K. and E.-K.L.; Writing--review & editing, E.-K.L.; Visualization, E.-j.K.; Supervision, E.-K.L.; Project administration, E.-K.L.; Funding acquisition, E.-K.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-RS-2023-00259061) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
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.
# Sensitivity Analysis for Microscopic Crowd Simulation
## 1 Introduction  
Pedestrian dynamics is an interdisciplinary research field that aims to understand pedestrian movement and crowd behavior. Social scientists from psychology and sociology, natural scientists, such as physicists, mathematicians, and computer scientists, as well as engineers from civil engineering and fire safety study different aspects of the subject. Lately, there have been several collaborations combining the different backgrounds and experiences [1, 2].  
Researchers mainly follow two approaches: on the one hand, there are empirical investigations [3], comprising field observations and surveys, as well as experiments to study behavior in a controlled environment. On the other hand, there are simulations to help explain and predict crowd phenomena. While each approach on its own has its advantages and disadvantages, there are multiple links among them that can and should be used. Observation of crowds reveals important situations to be studied in experiments or simulations. Experiments can be used to validate and calibrate the simulations. Simulations allow for studying phenomena for which experiments would be too costly, too time consuming, or, when human participants are involved, unethical.  
The main application for pedestrian dynamics is the evaluation of building designs and evacuation concepts. This includes planning emergency routes, identifying potential bottlenecks, and estimating capacity at events, in public spaces, or in large infrastructures, such as airports or railway stations. Independent of the approach, application, or background, the community shares an overall objective: making crowds safer.  
In this contribution, we look at prediction of crowd movement through microscopic simulations. Crowd simulation software often describes three behavioral layers: strategical layer (activity choice), tactical layer (route choice), and operational layer (locomotion) [4]. Our focus in this work is on a small scenario in which the movement can be solely modeled by a locomotion model, which is, on the operational layer.  
Common model choices for the locomotion layer can be categorized according to their treatment of time and space: (1) time and space discrete models are, for example, cellular automata approaches [5] (2) discrete time and continuous space models are typically differential equations based approaches, such as the social force model [6; 7], or velocity-based models [8; 9], due to discretization. An extensive overview over modeling of crowd behavior is given in [10]. Here, we use the Optimal Steps Model [11; 12], which is pseudo-continuous in time and continuous in space.