esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 8 Conclusions and Future Work  
The research reported in this work has consisted in the use Model Driven Engineering techniques to address the _organizational interoperability problem_: How can we provide means for a set of agents, immersed in a common environment, to evolve, reason, decide and interact with each other based on organizational concepts, since their organizational models may differ? In order to achieve this goal, we have proposed an abstract and integrated view of the main concepts that have been used to specify agent organizations, based on the analysis of several organizational models present in the literature. In this model, we captured the recurring modeling concepts, that were coherently combined into an abstract conceptual structure. We have then presented an adaptation-based solution for the organizational interoperability problem, when we have defined the mappings between different organizational models, by using this abstract conceptual structure. We have built our abstract conceptual structure based on six organizational models (STEAM, MOISE+, AGR, OPERA, TEAMS, ISLANDER), presented in Section 2. For brevity, in Section 5 we illustrated the application of our integration method using three of these models (MOISE+, AGR, STEAM), and concerning exclusively the structural dimension.  
A first extension of this work would be to build an integrated metamodel that could cope with OPERA and TEAMS. Moreover, we could evaluate how the other organizational models mentioned in Section 2.2.7 would affect our abstract conceptual structure. Concerning the MAORI framework, described in Section 6, we have tested its use by interoperating two organizational infrastructures, S-MOISE+ and MADKIT. A second extension of this work would be to test the framework with other organizational infrastructures, like AMELI [13] and ORA4 MAS [52]. Finally, we would like to test our model driven approach to solve other MAS interoperability problems, like the ones mentioned in Section 1.  
**Author Contributions:** Conceptualization, L.R.C, J.S.S. and O.B.; Formalization, implementation and experiments, L.R.C and A.A.F.B.; Supervision, J.S.S and O.B.; Writing and proofreading the paper, all authors.  
**Funding:** \"This research was partially funded by FAPEMA grant number 127/04 and CAPES, Brazil, Grant 1511/06-8\". [PERSON] was partially supported by CNPq and FAPESP, Brazil. [PERSON] was partially supported by grant #010/20620-5 and #014/03297-7, Sao Paulo Research Foundation (FAPESP), Brazil.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Data and Methods
### SSD Network Framework
#### 2.2.1 General Architecture  
The SSD is an object detector in the form of a single convolutional neural network. The SSD architecture works with the corporation of extracted feature maps and generated bounding boxes, which are called default bounding boxes. The network simply performs the loss calculation by comparing the offsets of the default bounding boxes and predicted classes with the ground truth values of the training samples at every iteration by the use of different filters. After that, it updates all the parameters according to that calculated loss value with the back propagation algorithm. In this way, it tries to learn best filter structures that can detect the features of the objects and generalize the training samples to reduce the loss value, thus attaining high accuracy at the evaluation phase [36].  
In the SSD method, a state-of-the-art CNN architecture was used as a base network for feature extraction with additional convolution layers, which produce smaller feature maps to detect the objects with different scales. Also, SSD allows more aspect ratios for generating default bounding boxes. In this way, SSD boxes can wrap around the objects in a tighter and more accurately. Lastly, the SSD network used in this research has a smaller input size, which positively affects the detection speed compared to YOLO architectures (Figure 4). Besides, YOLO has just two fully connected layers instead of additional convolution layers. These modifications are the main differences of SSD from the YOLO and they help to obtain a higher precision rate and faster detection [36].  
In the original SSD research, the VGG-16 model was used as a base network. In this research, the InceptionV2 model was used to reach a higher precision and faster detection as it has a deeper structure than the VGG models. In addition, it uses fewer parameters than VGG models thanks to the inception modules that are composed of multiple connected convolution layers [42]. As an example, GoogleNet, which is one of the first networks with inception modules, employed only 5 million parameters, which represented a 12x reduction compared to AlexNet and it gives slightly more accurate results than VGG. Furthermore, VGGNet has 3x more parameters than AlexNet [18].
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 4 Method of Calibration
### FBG Sensing Technique  
For the FBG method, the temperature wavelength fitting curve is necessary to determine the sensor's sensitivity. Light can be fed into the fiber from a BBS to calibrate the sensor, while the reflected light from the sensor can be routed to the spectrum analyzer or an interrogator via an optical coupler, as shown in Figure 11. Next, a portion of the grating area is heated in an enclosed environment whose temperature can be precisely controlled to a predetermined value. Alternatively, a heating plate can be used together with a thermocouple to validate the temperature at the desired time. Next, to ensure the mechanical stress-free sensor, the FBG head could be inserted into a ceramic capillary attached to a stainless-steel plate using Kapton tape [114].  
The initial temperature and central wavelength shift are first recorded, then the temperature is raised with a fixed step size, and at each value, the corresponding wavelength shift is recorded until the maximum value is reached. This procedure can be repeated several times to take an average value of the wavelength shift for the number of cycles and calculated at each temperature level. The data obtained can then be used to compute the temperature wavelength fitting curve using a linear regression (\(y\) = mx + c), where c is the initial Bragg wavelength at ambient temperature, \(x\) and \(y\) are the wavelength and temperature at each point, respectively, and m is the slope of the curve, which represents the sensitivity of the FBG sensor. The typical measurement from the literature has shown the slope of the linear fitting in the Kelvin scale to be 10.99 \(\pm\) 0.073 pm/K, with a mean error of \(\pm\)0.5 K [111].  
In [75; 115], the central Bragg wavelengths of the FBG sensors used are 1537 and 1539.9 nm, respectively, exhibiting a sensitivity of \(\pm\)0.2 nm/\({}^{\circ}\)C. After the calibration, the value of the junction temperature estimation can be obtained by measuring the wavelength shifts. For practical measurements, the fiber can be bonded to the chip with a thermal paste of high viscosity and low shrinkage [111]. This is necessary to ensure improved heat exchange between the sensing portion of the fiber and the IGBT chip surface. The thermal conductivity of the thermal paste and the temperature must be factored in during selection to reduce the effect of aging and thermal breakdown [50].
## 3 Problem Description
### Grids and WSNs  
The grid is often used in the geographic information system (GIS) to manage assets and outages and map the location of overhead and underground circuits [1; 2]. The grid separates the area needed to be monitored by sensors into grids with uniformly spaced horizontal and vertical lines. Due to the convenience of use, the grid is adapted, such that the whole WSN monitor area is divided into \(X_{\text{UB}}\times Y_{\text{UB}}\) sensing grids in this study, where \(X_{\text{UB}}\) and \(Y_{\text{UB}}\) mean the maximum radius of the \(x\) axis and \(y\) axis in the grid, respectively. Each grid is a location, object, city, etc., and each sensor is also located in a grid, say \((x,y)\), where \(x=0\), \(1\),..., \(X_{\text{UB}}-1\), and \(y=0\), \(1\),..., \(Y_{\text{UB}}-1\).  
Let WSN(S, AREA, RADIUS, COST) be a WSN with a hybrid topology, where \(S=\{1,2,\ldots,n\}\) is a set of sensors; AREA = [0, \(X_{\text{UB}}\)) \(\times\) [0, \(Y_{\text{UB}}\)) is the area for WSN to cover, monitor, or track, etc.; RADIUS is the radius level for each sensor; and COST is the price corresponding to the RADIUS level for each sensor.  
For instance, in Figure 1, the WSN has AREA = [0, 99) \(\times\) [0, 99) and three sensors are labeled at A, B, and C located at (5, 75), (12, 75), and (25, 50), respectively. The RADIUS \(r(x,y)\) and COST(\(r(x,y)\)) for each sensor in the WSN in Figure 1 are provided in Table 1. From Table 1, the price needed for the sensor located at A to have RADIUS 10 is 4 units of cost, i.e., \(r(5,75)=10\) and COST(\(r(5,75)\)) = 4.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Bus Faults
#### 3.1.6. LIVRD Algorithm  
To finish the fault analysis at bus 2, the LIVRD algorithm is tested. As explained in Section 2.6, for bus faults, the \(\text{V}_{\text{bus}}\) shows a lower value than the \(\text{V}_{\text{link}}\). As a result, both the VR and VRD demonstrate positive values exceeding one. The predefined threshold values for the LIVRD algorithm are \(\text{VR}_{\text{THR}}=2\) and \(\text{VRD}_{\text{THR}}=200\). These values will enable proper fault discrimination. These thresholds are determined by the relationship between the VR and the VRD. Under normal operating conditions, the VR equals one, and its derivative (VRD) is close to zero, though noise and other factors may cause small fluctuations. During a link fault, for example, the voltage ratio (VR) drops, typically ranging between zero and one, while the derivative (VRD) changes significantly. The chosen thresholds effectively capture these variations, ensuring accurate fault detection while minimizing false positives.  
Figure 7: Fault at bus 2, differential algorithm at R21 and R24: (**a**) pole-to-pole fault, and (**b**) pole-to-ground fault.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fault at Bus 2**} & \multicolumn{2}{c}{**Detection Time at R21-R24 (ms)**} & \multicolumn{2}{c}{**Detection Time at R21-R24 (ms)**} \\ \cline{2-4}  & \multicolumn{1}{c}{**P2P**} & \multicolumn{1}{c}{**P2G+**} \\ \hline \multirow{3}{*}{DIFF} & R = 0.01 \(\Omega\) & 0.010 & 0.010 \\ \cline{2-4}  & R = 2 \(\Omega\) & 0.010 & 0.010 \\ \cline{2-4}  & R = 20 \(\Omega\) & 0.010 & 0.010 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fault at bus 2, detection times at R21 and R24, for the differential algorithm.  
In bus faults, the \(\mathrm{V_{bus}}\) drops faster than the \(\mathrm{V_{link}}\), so the VR increases to a very high value rapidly.  
Observing Figure 8, it can be seen that there is no difference in the detection times between the pole-to-ground and pole-to-pole fault scenarios.  
As the fault resistance increases, the calculated VR and VRD values are lower, but with the pre-selected thresholds, the fault detection time remains unaffected. For every simulated scenario, the fault detection time is 0.01 ms. The combination of two algorithms makes the LIVRD algorithm more reliable under fault conditions and the simulated fault resistances do not affect the detection time, as depicted in Table 6.
# Nanofiber-Based Materials for Persistent Organic Pollutants in Water Remediation by Adsorption
## 6 Recommendations and Conclusions
The initial level of contamination should be justified by the occurrence of the selected contaminant in the environment rather than chosen as a function of the available solution characterisation technique, as adsorbent performance may be affected by a significant change in contaminant concentration. Isotherm analysis was found to be in over 80% of the reported isotherm studies fitted to Langmuir and Freundlich models, with in more than 40% of cases a report of the Freundlich constant with a correlation coefficient below 0.90 for performance comparison with other adsorbents. Adsorption isotherms reflecting the adsorption mechanisms at stake should be more carefully studied to avoid inconsistent performance comparisons. So far, engineered nanofibers showed the highest performance in the one-off removal of pesticides and disinfectants. However, these adsorbents also show versatility in the capture of contaminants. Ways to create selectivity for a target molecule, such as fiber layers of different pore channels and surface chemistry, could be further investigated. Adsorption being a conventional water treatment technique, the adsorbent material fabrication and operational costs are critical to the implementation of any new classes of materials. Industrial applications of nanofiber adsorbent material solutions should be rigorously assessed and benchmarked against commercially available fabrication routes. The adsorption kinetics and material dosage are also to be considered when evaluating adsorbent life cycle and operational costs. Industrial-scale nanofiber fabrication devices by electrospinning or multi-component fiber extrusion were reported to yield mass output varying between 0.02 kg/h and 0.2 kg/h, and up to 6.5 kg/h, respectively, with a production cost of $2-5/kg [121; 122]. Other competitive techniques such as force-spinning have been developed to simultaneously increase mass output and fiber quality [123]. Furthermore, to develop a sustainable industry, adsorbent recyclability must be further investigated. As the best-performing materials in the removal of POP may not offer the best recyclability, it is critical to systematically investigate the adsorbent hysteresis of adsorption-desorption. Regeneration of the adsorbent without increasing toxicity concerns linked to adsorbed contaminants must also be investigated. This work was supported by Deakin University and the Australian Research Council with the ARC Linkage Project LP140100374. LD also thanks the ARC for awarding his DECRA 2018 DE180100130 fellowship. [PERSON] performed the literature survey, did a major part of the analysis and paper writing. [PERSON] contributed in the data analysis and paper writing. [PERSON] helped to conceive the paper structure and in the discussion orientation. The authors declare no conflict of interest.
## 3 The Proposal
### Validation and Benefit of the Framework  
To validate and to verify the consistency of the framework, we collect our data from the Ministry of Communication and Informatics (based on MNOs data), OOKLA crowdsourcing (based on mobile broadband user data), Statistics Indonesia (based on per inhabitants' national survey), Ministry of Finance (related to regional finance), and Ministry of Internal Affairs (population). These data collections show that these are well adapted to the different sizes of districts or whether it helps differentiate conditions in urban, semi-urban, or rural areas. The benefit of a framework may refer to the results' interpretation. For example, 5G deployment requires frequencies. Since the radio frequency spectrum is a limited natural resource, its utilization must be optimized. The ideal usage of the radio frequency spectrum resembles a \"totally charged cube\" on three axes, including the frequency domain, the geographic domain, and the time domain. Figure 7 depicts the timetable plan (time axis) for the provision of a 5G band (frequency axis) in Indonesia. While this research's result \"Grid Indonesia\" illustrates the geographical axis. This geographic specific proposal could adapt to any country. Figure 7: The Three Axes of the Radio Frequency Spectrum Flexibility Policy Philosophy. In addition, the strategy for completing blank spot villages in Indonesia can be implemented if the regional government, the Ministry of Communication and Informatics, and the MNOs collaborate. Local administrations are responsible for making proposals to regions and potential communities. The mission of the Ministry of Communication and Informatics (MCI) is to map blank spot locations, give mapping documents covering non-3T areas that are still blank spots and need to be optimized, and provide regional options to MNOs for constructing BTS or optimizing the quality of service in mapping documents. The role of the cellular network operators is to construct a BTS or to optimize the network's quality based on the selected region. Telecommunications network operators (APJATEL) are responsible for providing the backbone. Further policy and regulatory instrument suggestions are shown in Table 6. For more details on the grid-based implementation approach, please see Supplementary Material File S4 which compiled based on various references: [17; 33] and provide 2 figures (Figures S10 and S11) as well as 1 table (Table S2).
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 5 Numerical Results
### Performance on Multi-Task MNIST Dataset  
We establish a parallel multi-task handwritten digit classification scenario utilizing the MNIST dataset. Specifically, Cell 1 and Cell 2 are assigned distinct subsets of digit classes, labeled as 0-4 and 5-9, respectively. This configuration introduces task heterogeneity, as each cell processes non-overlapping label distributions. Both cells employ an identical convolutional neural network (CNN) architecture for local model training. The architecture comprises two convolutional layers with rectified linear unit (ReLU) activation functions and kernel sizes of \(5\times 5\), containing 32 and 64 channels, respectively. Each convolutional layer is followed by a \(2\times 2\) max-pooling operation. Subsequently, the network includes a fully connected layer with 1024 units, culminating in a softmax output layer for classification. During local training, all devices utilize a fixed mini-batch size of \(m_{b}=128\). The performance of the models is evaluated using the average test accuracy and loss function values of both cells to provide a comprehensive assessment of classification efficacy and convergence behavior within the multi-task learning system.  
Figure 6 illustrates the impact of communication rounds \(N\) on the overall performance of parallel multi-task Air-FL over the MNIST dataset. From Figure 6a, all schemes demonstrate a continuous improvement in average test accuracy as \(N\) increases, with convergence observed in the later stages. The benchmark without AirComp serves as the performance upper bound, which achieves optimal accuracy. Notably, the proposed distributed scheme closely approaches this ideal benchmark throughout the training process, with an optimal test accuracy difference within 1%. This proximity demonstrates its near-optimal classi  
Figure 5: Convergence behavior of the proposed distributed scheme.  
fication performance, which surpasses both the benchmark without interference and the benchmark with maximum power, thereby validating its superior convergence speed and learning efficacy. Figure 5(b) reveals that the average loss function value decreases across all schemes, albeit with varying rates of decline and final convergence levels. The proposed distributed scheme achieves a loss function value closer to the optimal performance of the benchmark without AirComp compared to other baseline schemes. This outcome highlights its capability to effectively enhance the model's generalization performance.
## 7 Practical Applications of NCS
### Cloud Computing  
The cloud and networked control systems amalgamate the benefits of cloud computing technology, advanced theory of networked control system, and other recent developed wireless and communication-based approaches. Figure 21 represents the cloud computing-based NCS. This system consists of plants to be controlled, a gateway computer, the cloud, and the network. The cloud is equipped with a large quantity of computational and storage resources; however, the plants have restricted resources. The plants send the sensor data to the cloud through the available network, so that the desired control signals can be evaluated by the cloud. These control signals are then sent back to the plants as the input of the actuator.  
In a large-scale networked control system, the bagged information consists of information captured by universal information-sensing mobile devices, sensory technologies related to aerials, such as remote sensing, cameras, microphones, RFIDs, and WSNs [164].  
Figure 20: Quasi decentralized system for two synchronized plants.
# Meso-Scale Simulation of Concrete Based on Fracture and Interaction Behavior
## 6 Parametric Study
### The Effect of Frictional Behavior  
Coulombic friction is considered a key factor that influences the ultimate strength and residual bearing capacity of concrete. The results of the direct shear test and uniaxial compression test showed that the concrete with higher compressive strength has a greater internal friction angle. Although a desirable strength of concrete specimens can be achieved in experiments by adjusting the material types and aggregate gradation, it is hard to control the maximum allowable frictional stress. Therefore, the frictional angle \(\alpha\) is taken as the variable to investigate the effect of frictional behavior.  
As shown in Figure 18, the internal frictional behavior of concrete affects the final failure mode. With the increase of frictional angle, the inclination angles of main shear bands decrease. The local cracks tend to propagate dispersedly into concrete blocks rather than in the direction of the shear bands. The stress-strain curves of varying frictional angle \(\alpha\) are shown in Figure 19. As expected, the increase of frictional angle value has a positive influence on the concrete bearing capacity in the whole loading process. The influence strengthens once the plastic deformation appears and weakens when the specimens' bearing capacity declines by about a third. The sum of the crack length of the specimens is calculated as shown in Figure 20.  
Figure 17: Effect of material parameter on global stress-strain relationship under (**a**) tension and (**b**) compression.  
Figure 19: Effect of frictional angle on global stress-strain relationship under compression.  
Figure 20: Effect of frictional angle on crack propagation under compression.  
Figure 18: Failure modes of a specimen under the different frictional angles.  
When the tangential interaction is frictionless (\(\alpha\) = 0), the compressive strength and energy dissipations are 35.9% and 77.1% lower than that of the standard model (\(\alpha\) = 54.4\({}^{\circ}\)), respectively. It is noticed that the crack propagates more sufficiently and the inclination angle of the shear bands decreases with the increase of frictional angle. Although the cracks' local propagation paths are different, the main shear bands of the specimens are similar. The friction stress could constrain the crack propagation at the beginning of the damage, which benefits the ultimate bearing capacity. In the softening stage, the friction stress could help reestablish the loading path and make more blocks participate in bearing load, which may cause more cracks.