esa-sceva/satcom-chunk-collection huggingface dataset:
# Formal Modeling of IoT-Based Distribution Management System for Smart Grids
## 2 Related Work
In [32], a new advanced smart sensor was designed with a self-adjustment setting capability, being coordinated with the rest of the network. The designed sensor was tested with several different scenarios of short-circuits. The sensor proved efficient up to 80% in comparison with other analog and intelligent electronic devices. The technique is comprised of four layers, which work in a hierarchical form. Multi-faceted faults are detected through the islanding search algorithm, and the effectiveness of the designed technique is measured by using simulation tools [33]. The systematic study of smart grid communication infrastructure is presented, including its architectures, several network frameworks, and related technologies, and it is compiled with intelligent functions from the consumer perspective and distribution units of electricity [34]. In [23], the author presented an innovative FLISR solution for distribution networks that uses feeder terminal unit signals along with distribution grid states to rapidly identify and reliably locate the faulted sectors of the network. Particularly, the feeder terminal units, tripping signals of relays, and loss of voltages were used in combination for detecting and locating faults. Post-fault restoration was executed based on 13 different factors, including total operation cost, violation of power flow, and number of switching steps. To assess the electrical connection of the distribution network, a network topology processor was used so that at any time the network topology changed, it would automatically redefine it. We may infer from the literature analysis that no formal modeling of a distribution management system has been done before, and, to support this assertion, we have summarized past works in Table 1.
# The Link between Purine Metabolism and Production of Antibiotics in _Streptomyces_
## 1 Introduction  
Bacteria experience constant challenges, either in the environment or when infecting a host. They utilize various mechanisms to survive such stresses, which may include changes in temperature, pH, or oxygen content as well as limited access to carbon or nitrogen sources. A stress response that is mobilized to deal with events such as nutrient limitation is the stringent response. The purpose of this response is to focus cellular resources on survival, as opposed to growth [1]. To achieve this outcome, bacteria synthesize the hyperphosphorylated nucleotides guanosine pentaphosphate and guanosine tetraphosphate, collectively called (p)ppGpp, from GTP/GDP and ATP. Based on the critical role of (p)ppGpp, it is also called an alarmone, as it responds to starvation or stress and conveys such distressing circumstances to the cellular machineries in order to conserve resources. Exposure to challenging environmental conditions may also induce formation of biofilms, protective bacterial communities that confer a number of fitness advantages, including resistance to nutrient deprivation or antimicrobial agents. The second messenger cyclic-di-GMP (c-di-GMP), which is synthesized from two molecules of GTP, is intimately involved in the switch between a motile planktonic lifestyle and a sessile biofilm community in unicellular bacteria [2]. In multicellular Actinomycetes, such as _Streptomyces_ spp., c-di-GMP is involved in controlling developmental processes [3]. Collectively, these signaling molecules require the presence of purines to sustain their synthesis under conditions of stress and starvation, circumstances under which the bacterial cell must conserve essential nutrients. Purine salvage pathways are, therefore, expected to be important for the generation of substrates for (p)ppGpp and c-di-GMP synthesis. Additionally, members of the genus _Streptomyces_ respond tochanges in environmental conditions by a morphological differentiation and multicellular development that may be closely tied to their production of secondary metabolites, including a wide range of antibiotics. Indeed, two thirds of clinically relevant antibiotics such as chloramphenicol, streptomycin, and tetracycline are synthesized by _Streptomyces_ spp. [4]. In this review, we discuss how purine metabolism affects antibiotic production, with an emphasis on the stringent response.
## 2 Materials and Methods
### Methods
#### 2.3.3 Sharpley Additive Explanations (SHAP)  
Sharpley Additive Explanations (SHAP) play a key role in XAI models, as they provide a framework for interpreting machine learning models, making them more transparent and understandable [36]. Specifically, SHAP values provide a measure of each feature's contribution to a particular prediction, ensuring the model's explainability and interpretability [37].  
The Sharpley value for a feature \(j\) in a model \(f(x)\) is calculated as the average of the marginal contributions of the feature \(j\) to every possible coalition of features. The SHAP value for feature \(j\), denoted as \(\phi_{j}\), is given by the following [38]:  
\[\phi_{j}=\sum_{S\subseteq N\smallsetminus\{j\}}\frac{|S|!(|N|-|S|-1)!}{|N|!}[f(S \cup\{j\})-f(S)] \tag{3}\]  
where \(N\) is the set of all features, and \(S\) is a subset of the features excluding \(j\). \(f(S)\) is the model's prediction using only the features in \(S\), and \(f(S\cup\{j\})\) is the model's prediction using the features in \(S\cup\{j\}\).  
To sum up, XGBoost is a powerful algorithm for creating predictive models, while SHAP provides a robust framework for understanding and explaining the predictions made by such models. By using SHAP values with an XGBoost model, this study breaks down the prediction into the contributions from each feature, making the model more interpretable.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions
### Adaptive ML for Easy/Adversarial Environment  
So far, stochastic MAB algorithms are applied in several wireless communications applications. These algorithms are designed for stochastic stationary environments, which are not suitable for adversarial/dynamic environments like D2D communications. On the other hand, some of ML techniques (e.g., online learning algorithms) have theoretical performance guarantees in the worst-case scenarios. These techniques, however, do not perform better in practice since the environments are not always so adversarial and they do not fully take advantage of such easiness in the settings. Easy data approaches in ML like in [31], attempt to develop algorithms that perform adaptively in both the best and worst cases simultaneously. This approach would be helpful in solving different D2D problems, where the environments sometimes stationary and some other times are dynamic.
## 4 Discussion  
### Validation of Selected Material Laws  
#### 4.1.1 Fung Law for Wild-Type, Sgcd\(-/-\), and Mdx Mice  
The constants of the constitutive relation proposed in [23] for wild-type mice seem to be the least accurate of the three mouse models considered. The predicted shapes of the \(p_{int}\)-\(D_{out}\) curves in the FEM simulations are not exactly correlated with the experimental data from [24]. The experimental \(p_{int}\)-\(D_{out}\) response presents a noticeable inflection point for the internal pressure close to 50 mmHg, whereas the inflection point for the FEM estimations is rather hardly noticeable or occurs at very low \(p_{int}\) values. To assess the computational response of wild-type mice arteries from the quantitative point of view, the predictions of RMSE/nRMSE between the FEM results and the experimental measurement are calculated. The lowest RMSE/nRMSE is obtained for the 102704 wild-type mouse specimen and equals 19 \(\upmu\)m/4%. Therefore, this set of constants is a reasonable approximation of the experiment, although the shape of the \(p_{int}\)-\(D_{out}\) is not perfect. As mentioned in Section 2.6, we propose a threshold value for acceptable accuracy at the level of \(\sim\)20 \(\upmu\)m, being 5% of a typical outer diameter of the mouse carotid artery, which we find to be approximately 400 \(\upmu\)m [19; 23]. The FEM response predicted employing the mean data set (RMSE = 29.7 \(\upmu\)m/nRMSE = 6%), and the one for the specimen 110304 (RMSE = 26.2 \(\upmu\)m/nRMSE = 5%) is a bit less accurate than the preceding one. The Fung constants sets denoted as 012705 and 011305 have poor accuracy, because the RMSE/nRMSE values are 46.2 \(\upmu\)m/9% and 49.2 \(\upmu\)m/9%, respectively. The Fung law for specimen 110104 is inaccurate, since the RMSE = 101.8 \(\upmu\)m/nRMSE = 20% and this is large compared to the measured diameter of the artery. On the other hand, the results of the sgcd\(-/-\) FEM analyses quite well resemble the experimental behavior from [24] when speaking about the shape of the \(p_{int}\)-\(D_{out}\) response. The square errors for the mean set of Fung law constants (RMSE = 10.2 \(\upmu\)m/nRMSE = 2%) and for the specimens 122104 (RMSE = 13.1 \(\upmu\)m/nRMSE = 3%) and 120304 (11.2 \(\upmu\)m/nRMSE = 2%) are low. Therefore, these constants have been successfully validated. The calculations performed for the combination of the Fung law parameters denoted with numbers 011704 and 012005 are also quite close to the experimental data. The RMSE/nRMSE for these sets are 27.3 \(\upmu\)m/6% and 29.3 \(\upmu\)m/6%. Nevertheless, the accuracy of the constitutive laws, denoted as 011905 and 011405, is rather poor, since the RMSE/nRMSE equals 47 \(\upmu\)m/10% for both of them. Validation of the mdx mice Fung constitutive relation parameters from [23] is also quite successful. The slope of the pressure-diameter curve is reasonably appropriate for almost all of the material law sets presented in [23] under consideration here. The quantitative analysis of the FEM results against the experimental ones indicates that the
## 5 Discussion and Analysis
### Signaling Cost  
Figure 9 shows how the signaling cost varied with mobile producer speed, starting from 0 ms to 100 ms after the simulation was run using the NS3 software based on the parameter sets.  
The results were encouraging, despite the broadcasting nature of the three methods for the mobile producer and the domain restriction application. KITE and the PMSS had minimal handoff signaling costs. However, the handoff signaling cost of hybrid NeMO was nearly equal to or less than that of KITE and the PMSS, with about 4% of signaling cost, compared with those of KITE and the PMSS. The result of hybrid NeMO was good, even though no significant improvement against KITE and the PMSS was found at certain mobile producer speeds, such as 30 to 50 m/s, 50 to 60 m/s, and 70 to 100 m/s.  
With regard to hop-count performance, Figure 10 shows that the KITE operation sent a greater number of messages than the PMSS or hybrid NeMO did. When the speed increased, the hybrid operation increased the number of messages to be sent. Therefore, mobility speed did not affect the performance of the transmission data packet and the interest packet to the consumer, because of the stability of BIT. Message drop was also encountered, but message delay generally happened during transmission.  
Figure 10: Average signaling by each method.  
Figure 9: Signaling cost versus speed.  
Figure 10 shows that the hybrid method had the highest number of transmitted messages, compared with the PMSS and KITE. Even though a recent study stated that the PMSS and KITE have minimal handover signaling overhead during transmission, compared with anchor and anchorless producer mobility methods, the performance of the hybrid method in terms of signaling cost was more efficient than that of these other two methods.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### LPWAN Standards
#### Standard Development Organizations (SDOs)  
A few notable SDOs are discussed as follows:
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Multipath TCP (MP-TCP)  
MP-TCP [5] is a transport layer solution to allow multipath transport. An MP-TCP socket consists of sub-flows with different pairs of addresses/ports while presenting a single TCP socket to the application. While it presents a TCP socket API to the application, additional options can expose MP-TCP-specific information. MP-TCP-capable hosts will initiate the three-way handshake with an MP-TCP-specific option, MP-CAPABLE, for setting up an MP-TCP main flow with a single IP address. Then, sub-flows are added using MP-JOIN to add another address. As the name suggests, it is a solution specific to TCP, so is not usable for UDP applications, e.g., real-time applications.  
Although its original goal was multipath transport, by adding and removing subflows during transmission, it can be used for mobility [25]. There are proposals for integrating mobility management to enable mobility for MP-TCP [26]. However, there are no formal standards or implementations present at the time of writing.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.2 Path Planning and Robot Control  
In [23], the authors present a novel workflow for integrating ABB industrial robot manipulators with an ROS with the objective of seamless integration between the industrial ABB robots and the ROS ecosystem. The objective is to improve industrial automation solutions by leveraging the capabilities of ABB robots and the ROS. ABBs are known for their precision, reliability and widespread use in various industries, but integrating them into complex systems can be challenging. The workflow presented in [23] (as shown in Figure 3) facilitates the exploration and implementation of advanced control techniques, such as RL, in an industrial setting with ABB robots.  
The authors in [23] demonstrated the integration of an ROS and RL by employing different RL algorithms (PPO, A2C, and DDPG) to train a robot to collect parts from a conveyor belt located in varying positions and orientations. The authors mention that it can also be combined with external sub-frameworks such as MLPro (for more information about MLPro, please refer to [60]). This simulation environment aims to weld two steel hook segments together, as shown in Figure 3. In this figure, adjacent to the ABB arm, a large blue box serves as the camera site, which can determine the position and orientation of the moving components, as well as the gripping point. In this setting, the PPO algorithm outperformed the A2C and DDPG algorithms. The ROS served as a middleware that facilitated communication between robot components.  
The above-mentioned work provides a comprehensive workflow that addresses the practical challenge of bridging the established industrial robotics domain of ABB robots, including a detailed workflow and simulated welding settings. It showcases the flexibility of the proposed workflow through an example of RL integrated with an ROS, demonstrating its applicability in various complex industrial setups and potentially enhancing the industrial arm applications.  
Figure 3: ABB RobotStudio workflow. For more information, please refer to Table 4.
## 3 Experimental Results
### Characteristics of Interlayer Sliding Acceleration  
To further observe the acceleration characteristics of the interlayer sliding, two \(5\times 5\) pixel square windows were selected approximately 25 mm above and below the interface in the middle and on the left and right ends of the speckle observation area. The connection line between the two square windows was perpendicular to the interface of the specimen. By calculating the x-co-ordinates of the center points of the two square windows in each image and their difference, we analyzed the characteristics of the interlayer sliding displacement and acceleration change during loading. The time interval between each image was approximately 0.2 s. The arrangement of the measuring points and the change in the interlayer sliding displacement are shown in Figure 8a. When the load reached approximately 20% of the peak value, the interlayer sliding displacement at measuring points 1-3 began to change significantly, and the interlayer sliding displacements at the different measuring points did not vary synchronously. The maximum interlayer sliding displacement during the rock bending deformation was 0.046 mm and was located at measuring point 3. The rock fracturing caused a sudden increase in the interlayer sliding displacement, with instantaneous increases of 0.051, 0.156, and 0.081 mm at measuring points 1, 2, and 3, respectively. Among them, the interlayer sliding displacement at measuring point 1 exhibited a negative growth trend during the rock fracturing, followed by a positive growth trend. This was caused by the inconsistent directions of the interlayer sliding caused by the rock fracturing at measuring point 1 and by the bending deformation.  
Figure 8: Shear strains of composite rock layer specimens at (**a**) Point 1; (**b**) Point 2; (**c**) Point 3; (**d**) Point 4; (**e**) Point 5; and (**f**) Point 6.  
The change in the interlayer slip acceleration (Figure 9b) exhibited intermittent characteristics during the bending deformation stage, with a maximum interlayer slip acceleration of 0.6 mm/s\({}^{2}\) at measuring point 2. During the rock fracture deformation stage, it exhibited a sudden increase, and the slip increased earlier at both ends of the rock than in the middle of the specimen, with a maximum interlayer slip acceleration of 3.8 mm/s\({}^{2}\) at measuring point 2. Therefore, the rock bending led to an interlayer slip deformation, while the rock fracturing greatly promoted the interlayer slip.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 3 Artificial Intelligence and Internet of Things (IoT) Data Fusion  
The fusion of artificial intelligence and internet of things (IoT) can be a significant method to address the issue of crop monitoring. Wireless sensor network (WSN) is an infrastructure of artificial intelligence and IoT systems that could be used to monitor crop parameters and manage the proper irrigation. Meanwhile, intelligent system can inform harvest initiation. The fusion of both pieces of the aforementioned information helps making crop and crop parameter-related decisions. Therefore, it can suggest the farmers about the precise quantity of water for irrigation purposes and avoid wasting the irrigation water due to bad irrigation management and strategies [136].  
An efficient IoT system, particularly for field conditions such as agricultural fields, consists of low-cost things, which contain a series of limitations, i.e., mini-batteries, low processing, and storage chambers. Moreover, it needs to deal with the data errors of sensors and low accuracy due to atmosphere, weather, traditional sensors, communication problems, and data noise. Data fusion can help address these issues, as it is one of the highly accepted methods for improving sensor accuracy, displaying the global perspective, and delivering highly accurate decisions [137].  
Different terminologies, such as artificial intelligence fusion, sensor fusion and data fusion, are used to describe some fusion characteristics. Yet, these terms are recognized and used interchangeably [138]. These concepts are widely accepted and may vary depending on the studies. [PERSON] et al. [138] defined them as combination of multiple sources to achieve the developed form of data. This study adopted the definition of sensor and data fusion, reviewing to display the developed data quality and decision-making process.
# Intra-oral 3D Scanning for the Digital Evaluation of Dental Arch Parameters
###### Abstract  
Intra-oral scanning technology has brought a completely new approach to examination methods in dentistry. In comparison to traditional plaster casts, it allows more precise digital analysis of dental arch components during the treatment of dental disorders. Data acquired can also be used for the creation of three-dimensional (3D) models using 3D printers. This paper describes a data acquisition system, the mathematical processing of resulting three-dimensional model, and the statistical analysis of selected parameters of the dental arch. The study aims at comparing the results achieved from 20 models of the same individual acquired by different specialists. The proposed methodology includes mathematical rotation of objects into the optimal plane defined by the teeth tops' location to increase the accuracy of the resulting parameters. The mean evaluated distances between canines (3-3) and premolars (5-5) are 24.29 mm and 37.98 mm, respectively, for the lower dental arch. The proposed object rotation increased these distance by 0.9% and 1.4%, respectively, reducing the rotation error. Moreover, the variability of results decreased and the mean value of the coefficient of variation was lowered by 12%. Image registration was then used to evaluate changes of dental arch parameters. This paper presents the advantages of digital models for visualisation of the dental arch allowing its flexible rotation and evaluation of its parameters.  
intra-oral scanning; three-dimensional digitisation; spatial modelling; dentistry; computational intelligence; image registration Article
## 5 Optimization of the Three-Layer One-Segment Tube
### Pattern Generation and ANN Training
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Pattern** & & & & **Number** & **Time Consumption** \\
**Set** & \(\mathbf{\lambda^{\text{min}}}\) & \(\mathbf{\lambda^{\text{max}}}\) & \(\mathbf{\Delta\lambda}\) & **of Patterns** & **Whole Set** & **One Pattern** \\ \hline \(\mathcal{P}_{1}\) (\(\mathbf{\cdot}\)) & \(-75\) & \(+75\) & 30 & 216 & 53 min & 14.6 s \\ \(\mathcal{P}_{2}\) & \(-87\) & \(+88\) & 35 & 216 & 51 min & 14.0 s \\ \(\mathcal{P}_{3}\) (\(\mathbf{\cdot}\)) & \(-90\) & \(+90\) & 30 & 343 & 82 min & 14.3 s \\ \(\mathcal{P}_{4}\) & \(-90\) &The errors shown in Table 4 are commonly used error measures in ANN training, i.e., standard deviation \(\sigma\), Mean Squared Error (MSE), and \(f_{1}\) prediction Average Relative Error (ARE\({}_{1}\)). Assuming that **y** is the ANN prediction of natural frequencies,  
\[\textbf{y}=\left\{y_{1},y_{2},\ldots,y_{10}\right\}=\text{ANN}\left(\boldsymbol{ \Lambda}\right). \tag{13}\]  
MSE and ARE\({}_{1}\) are given by the following formulas:  
\[\text{MSE}=\frac{1}{P}\sum_{j=1}^{P}\|\textbf{f}^{j}-\textbf{y}^{j}\|=\frac{1} {P}\sum_{j=1}^{P}\sum_{i=1}^{10}(f_{i}^{j}-y_{i}^{j})^{2}, \tag{14}\]  
\[\text{ARE}_{1}=\frac{1}{P}\sum_{j=1}^{P}\frac{f_{1}^{j}-y_{1}^{j}}{f_{1}^{j}} \cdot 100\%, \tag{15}\]  
where \(P\) is the number of testing patterns (see Equation (10)). The results shown in Table 4 prove that the applied deep networks are properly trained and can predict the values of the natural frequencies of a laminate tube with very high accuracy. However, the accuracy is significantly lower in two cases, namely, ANN\({}_{5}\) (trained using random patterns only) and ANN\({}_{13}\) (trained using a very limited number of patterns).