esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm
## 6 Solving Multiprocessor Fair Scheduling Problem with IMSMA  
### Establishment of the Multiprocessor Fair Scheduling Problem Model  
The task scheduling problem on multiprocessors has been proven to be an NP-hard problem. Ensuring fairness in scheduling can improve the utilization of processor resources to some extent. Depending on different application scenarios, the definition of fairness may vary. To accomplish fair scheduling, we focused on the average process time on each processor and aimed to minimize the maximum average execution time on each processor to achieve fair scheduling. We established a model for the multiprocessor fair scheduling problem based on this objective. Assuming n jobs and m processors, let \(P_{ij}\) represent the time required for job \(i\) to be executed on processor \(j\). We introduce a binary variable \(x_{ij}\) to indicate whether job \(i\) is run on processor \(j\) or not. The formulation is as follows:  
\[x_{ij}=\left\{\begin{array}{l}1,condition\\ 0,other\end{array}\right.. \tag{15}\]  
The _condition_ represents the case where job \(i\) is run on processor \(j\), and _other_ represents all other cases. One processor can handle only one task at a time, so the constraint conditions are as follows:  
\[\sum_{j=1}^{m}x_{ij}=1,i=1\cdots n. \tag{16}\]  
Assuming the total execution time on each processor is \(P\), we have the following constraint:  
\[P_{j}=\sum_{i=1}^{n}P_{ij}x_{ij},j=1\cdots m. \tag{17}\]  
The average execution time on each processor \(P_{j}^{avg}\) is represented as follows:  
\[P_{j}^{avg}=\frac{P_{j}}{\sum_{i=1}^{n}x_{ij}},j=1\cdots m. \tag{18}\]  
The following is a representation of the objective function:  
\[F(x)=\min\Bigl{(}\max\Bigl{\{}P_{1}^{avg},P_{2}^{avg},P_{3}^{avg},\cdots,P_{m }^{avg}\Bigr{\}}\Bigr{)} \tag{19}\]  
\[s.t.=\left\{\begin{array}{l}(15)\\ (16)\\ (17)\\ (18)\end{array}\right. \tag{20}\]  
The objective function is constrained by Equations (15)-(18). To facilitate solving the equation, let us consider the continuous approximation of the discrete objective function:  
\[F(x)=\min\Bigl{(}P_{1}^{avg}+P_{2}^{avg}+\cdots+P_{m}^{avg}\Bigr{)}+\mu_{1} \sum_{i=1}^{n}\left(x_{i1}+x_{i2}+\cdots+x_{im}-1\right)+\mu_{2}\sum_{i=1}^{n }\sum_{j=1}^{m}\left(x_{ij}-x_{ij}^{2}\right). \tag{21}\]  
In the equation, \(\mu_{1}\) and \(\mu_{2}\) are two random numbers between zero and one. The two additional terms added afterwards are introduced to represent that \(x_{ij}\) is a binary variable.
# Seismic Response and Recentering Behavior of Reinforced Concrete Frames: A Parametric Study
###### Abstract  
The inelastic response of reinforced concrete (RC) frames under seismic loading is influenced by mechanical and geometrical properties and by the reinforcement arrangement of the beam-column members. In this paper, the seismic response and recentering behavior of RC frames is investigated numerically via cyclic pushover analysis and described by means of three synthetic behavioral indexes, namely a recentering index, a hardening index, and a ductility index. A fiber-hinge formulation is used to describe the inelastic behavior of the RC elements, and the versatile pivot hysteresis model is implemented at the material level to capture the possible pinching effects ascribed to the weak transverse reinforcement and to poor construction details that might be observed in the existing RC structures. This model is first validated against the experimental results from the literature and then applied, within a wide parametric study, to a set of 80 RC frame scenarios featured by various combinations of axial load levels and reinforcing details. As the output of this parametric study, practical design abacuses are constructed to describe the trends of the above-mentioned behavioral indexes, which are usefully related to specific mechanical and loading features of the analyzed RC frames. The reliability of the obtained results and the usefulness of the constructed abacuses in anticipating the overall cyclic behavior of a generic RC building, depending on the actual mechanical parameters of the RC sections at each story level, is finally demonstrated through a nonlinear time history analysis of an eight-story RC frame, representative of the substandard RC frames built in the 1970s in Italy.  
Keywords:seismic response; cyclic behavior; recentering behavior; pivot hysteresis; nonlinear analysis; reinforced concrete; ductility; fiber-hinge model; cyclic pushover +
Footnote †: journal: Applied
## 4 The Proposed QoS-Aware Routing
### QoS-Aware Queuing and Scheduling  
Queuing and scheduling have a direct impact on QoS characteristics. The desired QoS-aware routing for multiple classes of traffic, ranging from most critical data packets having constraints on both the delay and reliability to ordinary packets having no such delay or reliability constraints, is assumed to be met by implementing priority queues. In this work, four separate queues in a sensor node are considered; the highest priority queue for the critical packets (CP), the second higher priority queue for the delay-constrained packets (DP), the next lower priority queue for the reliability-constrained packets (RP) and the least priority queue for the ordinary packets (OP), as illustrated in Figure 2. Here, the scheduler uses strict priority logic, _i.e._, it always serves the highest priority queue first. If there is no packet waiting in the higher priority queues, it will serve the lower priority queues.  
A key problem of the above multi-queuing system is that the lower priority traffic may be indefinitely blocked by higher priority traffic, which is commonly known as the starvation problem. A solution to this starvation problem of the lower priority traffic is _aging_, which is a technique of gradually increasing the priorities of packets waiting in the system for a longer period of time. In this work, we use a timeout-based policy that moves a packet to an upper priority queue on expiration of the timeout. Note that the above multi-queuing system implements the in-node packet contention based on priority levels, _i.e._, among the packets of the same node. It is also possible to define inter-node priorities for all traffic classes of the neighboring nodes by modifying the MAC protocol slots and backoff times [15]. However, the implementation of such inter-node prioritization requires important modifications in the MAC layer, which is beyond the scope of this work.
## 3 Environment
### State Representation
#### 3.2.2 Image-like Representation  
In the second approach, the network layout is modeled as a 2D grid-world, which is also one-hot encoded based on the cells' occupancy and the walls that surround the free path of the network. In the formulation of the channels for the representation, we have the same concerns as in the feature-based case, notably, that the current decision maker should be aware of the other agents' position and direction along with the required exit position. However, in this case, there is one more. In the feature-based case, the whole network is a directed graph where it is not allowed to move backward since it is impossible for a train to do so. Consequently, the actions only provide steps in the right direction. However, in the grid-world, the possible steps are the four direction. Hence, somehow, the agents have to be aware of the directed graph nature of the free path in a way that supports generalization. This problem is solved in the first channel.  
* Channel 1: In the first channel, the network's layout is shown to the agent, where the walls are represented with ones and the free path with zeros. The directed graph nature of the network is represented to the agent by putting walls--ones--into the positions next to the agent, which would mean going in the reverse direction. This complement is consistent with the representation since there are walls surrounding the free paths and, thanks to that, supports generalization. Furthermore, it perfectly provides the necessary information about the directed nature of the problem.
* Channel 2: This channel shows the position of the current decision maker in the network by setting the particular cell's value to one, while every other cells' value is set to zero.
* Channel 3: This channel shows the goal position of the current decision maker in the network by setting the particular cell's value to one, while every other cells' value is set to zero.
* Channel 4: This channel shows the position of all the other trains in the network that travels in the same direction as the current decision maker by setting their cells' value to one. While every other cells' value is set to zero.
* Channel 5: This channel shows the position of all the other trains in the network that travels in the opposite direction as the current decision-maker by setting their cells' value to one. While every other cells' value is set to zero.  
An example of the proposed image-like representation is shown in Figure 4.  
Figure 3: The components of the vector-based state representation.
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 3 PID Control Development  
The PID control of the rotorcraft is developed, coupling the roll and pitch angles with the horizontal translation of the rotorcraft. This involves developing an inner loop for the faster dynamics and an outer control loop for the slower dynamics. Preferably, the tuning process for the controller should start in the inner loop, in order to ensure that the rotorcraft is rotationally stable, before proceeding to the outer loop. The outer loop is responsible for the control of the position of the rotorcraft with respect to the Earth-fixed reference frame. The \(x_{d}\), \(y_{d}\) and \(z_{d}\) reference signals are passed into the outer loop transformation, \(\mathbf{R}\), which then passes these signals to the inner control loop. As such, these outer loop signals must be tuned to represent the desired roll and pitch angles in the body-fixed reference frame of the rotorcraft as shown in Figure 2.  
The structure of the PID controller is described as follows:  
\[u_{i}(t)=K_{p_{i}}e_{1}(t)+K_{i_{i}}\int e_{1}(t)dt+K_{d_{i}}\frac{de_{1}(t)}{ dt}. \tag{26}\]  
where the error signal, \(e_{1}(t)=y_{d1}-y_{1}\), is the difference between the desired response, \(y_{d1}\), and the actual output, \(y_{1}\). The signal \(u_{i}(t)\) is used to drive the corresponding actuators in the swashplate.  
The first PID controller parameters are found using the manual tuning method. Although this method is effective and has been proven in applications, for the system with multiple loops and a larger number of gains, 18, it becomes tedious and time consuming [30]. The rest of the controllers are tuned using computational intelligence optimisation techniques. The simulation results based on these controllers are given in the next section. The controller optimisation algorithms are also discussed in the next section.
# Effect of Curing Conditions on Microstructure and Pore-Structure of Brown Coal Fly Ash Geopolymers
## 5 Discussion
The macropores fill the gaps between unreacted fly ash particles [52]. The geopolymerisation rate controls the quantity of gel formation. In concrete, the gel fills the cracks/voids between unreacted fly ash particles, aggregates the pore space in the matrix, thus refining the size of the pores. The pores identified for both the brown coal mortar and the concrete are principally in the macropores region, and hence are identified as corresponding to the gaps between unreacted fly ash grains and between the aggregate. This would suggest that little refinement of the pore matrix has occurred within the brown coal fly ash geopolymer materials. The SEM images and the compressive strengths would however, indicate that some degree of refinement has occurred but also show the presence of large macropores in the matrix, correlating with the MIP data, with a significant quantity of macropores being evident. The mesoporous of fly ash based geopolymer is expected to increase with an increase in the geopolymerization rate, which will result in higher strengths. This correlates with the higher strengths observed for the mortar compared to the concrete in the 4-12 h period and also the temperature profile which indicates that the geopolymerisation progresses more rapidly in the mortar than the concrete. Overall, the temperature profiles correlate well with the compressive strengths and porosity data and the proposed reaction kinetics for geopolymer formation [44; 45]. The results show that the geopolymerisation progresses more rapidly for the mortar specimens than the concrete specimens, with 12 to 14 h providing an optimum curing time for the 50 mm mortar cubes and 24 h being the optimum time for the 100 mm concrete cubes. For 50 mm and 100 mm concrete, the specimens' compressive strengths in excess of 30 MPa can be obtained. However, the larger specimens' structural integrity cannot be achieved. This is attributed to the differential in the temperature profile within the specimens meaning that the geopolymerisation reaction is not complete after 24 h of heat curing at 120 \({}^{\circ}\)C in these larger specimens and that longer curing periods are required. However, this may result in micro-cracking in the surface under extended curing periods as the temperature profiles indicate that the reaction in the surface concrete is completed and has reached the point where evaporation of the residual water is likely to occur with longer curing periods. As such, the specimen size and curing time must be taken into consideration in the production of brown coal fly ash geopolymer concrete.
## 5 Models Based on Canonical Piece Wise Linearization
### Magnitude Selective Affine (MSA) Based Linearization  
Extending the previous work reported for DVR and CPWL, the objective of this work is to further reduce the overheads and complexity of the CPWL method by proposing a magnitude selective affine (MSA) function-based model. The advantage of this method is that it requires only a single linear operation for the selected zone leading to a lower complexity and simpler structure.  
In order to optimize the operations, the coefficients in the zone that have similar magnitude can be coupled together [72; 73]. The comparison between threshold function and magnitude of input samples can select which zone the samples will be falling and which affine functions can be utilized. This simplification will result in the reduction of the CPWL complex operation. Therefore, we can rewrite the first term of CPWL function in Equation (13) in a simplified way in terms of MSA function that can be expressed as:  
\[\begin{array}{l}\sum_{m=0}^{M}\sum_{k=0}^{K}\sum_{l=1}^{L}c_{m,k,l}^{(1)} \Big{|}|x(n-k)|^{2}-\beta_{l}\Big{|}x(n-m-k)\\ =\sum_{m=0}^{M}\sum_{k=0}^{K}x(n-m-k)\Big{(}\sum_{l=1}^{L}c_{m,k,l}^{(1)}\Big{|} |x(n-k)|^{2}-\beta_{l}\Big{|}\Big{)}\\ =\sum_{m=0}^{M}\sum_{k=0}^{K}u_{m,k}^{(1)}(n-k)x(n-m-k)\\ \\ u_{m,k}^{(1)}(n-k)=\sum_{l=1}^{L}c_{m,k,l}^{(1)}\Big{|}|x(n-k)|^{2}-\beta_{l} \Big{|}\\ =\left\{\begin{array}{c}A_{m,k,1}^{(1)}|x(n-k)|^{2}+B_{m,k,1}^{(1)},\;0\leq |x(n-k)|^{2}<\beta_{1}\\ \vdots\\ A_{m,k,L}^{(1)}|x(n-k)|^{2}+B_{m,k,L}^{(1)},\;\beta_{L-1}\leq|x(n-k)|^{2}< \beta_{L}\end{array}\right.\end{array} \tag{14}\]Here in Equation (15), \(A^{(1)}_{m,k,l}\) and \(B^{(1)}_{m,k,l}\) are the linear model coefficients defined for each zone of the MSA function \(u^{(1)}_{m,k}(.)\). The example of hardware implementation is shown in Figure 9. The simplification shown in Equation (15) leads to this realization that input power terms without any magnitude are compared with the thresholds for the offset and linear gain selection for the MSA function. This leads to removal of square root calculation operation. The overall model of Equation (12) in terms of MSA function can be written as:  
\[\begin{split} y(n)=\sum_{m=0}^{M}\sum_{k=0}^{K}u^{(1)}_{m,k}(n-k) x(n-m-k)\\ +\sum_{m=1}^{M}\sum_{k=0}^{K}u^{(2)}_{m,k}(n-k)x^{2}(n-k)x^{*}(n-m -k)\\ +\sum_{m=1}^{M}\sum_{k=0}^{K}u^{(3)}_{m,k}(n-k)x(n-k)|x(n-m-k)|^{ 2}\\ +\sum_{m=1}^{M}\sum_{k=0}^{K}u^{(4)}_{m,k}(n-k)x^{*}(n-k)x^{2}(n- m-k)\\ u^{(i)}_{m,k}(n-k)=\sum_{l=1}^{L}c^{(i)}_{m,k,l}\big{|}\big{|}|x (n-k)|^{2}-\beta_{l}\big{|}\\ =\left\{\begin{array}{c}A^{(i)}_{m,k,1}|x(n-k)|^{2}+B^{(i)}_{m, k,1},\ 0\leq|x(n-k)|^{2}<\beta_{1}\\ \vdots\\ A^{(i)}_{m,k,l}|x(n-k)|^{2}+B^{(i)}_{m,k,l},\ \beta_{L-1}\leq|x(n-k)|^{2}<\beta_{L} \end{array}\right.\end{split} \tag{17}\]
## 2 Materials and Methods
### Robust Federated Learning Based on FL-QLMS  
The proposed system involves multiple electric vehicles collaborating to learn an energy consumption prediction model using the federated learning (FL) approach [19]. However, conventional FL techniques based on simple model averaging, such as FedAvg [20] and FedProx [21], are vulnerable to Byzantine failures [24], i.e., attacks by some malicious participants. This paper proposes the Federated Learning with Qualified Local Model Selection (FL-QLMS) algorithm, which enables more robust model aggregation by overcoming these limitations. The core idea of FL-QLMS is to use a trusted central validation dataset to evaluate the performance of local models received from each electric vehicle at the end of every round and selectively incorporate only the top k% of high-quality models into the global model update. Algorithm 1 outlines the pseudocode for FL-QLMS, distinguishing between the roles of the server and the clients. The Evaluate() function plays a crucial role in FL-QLMS by assessing the performance of individual local models. It takes a local model W and the central validation dataset \(D_{\text{val}}\) as inputs and returns a performance metric, such as root mean square error (RMSE), indicating the model's prediction quality. Within the function, a Byzantine node identification logic is implemented to detect and handle potentially malicious nodes based on their model performance. For example, if a local model's RMSE exceeds a certain threshold, it is suspected to be a Byzantine node, and its impact on the global model can be mitigated through weight adjustment or exclusion. This selective aggregation process based on the Evaluate() function effectively prevents the degradation of the global model quality due to Byzantine failures. By explicitly defining the Evaluate() function and its role in the FL-QLMS algorithm, the robustness and reliability of the federated learning process for energy consumption prediction are significantly enhanced. Figure 3 presents empirical results demonstrating the robustness of FL-QLMS under various types of Byzantine attacks. The proposed algorithm maintains stable performance even in the presence of a significant portion of malicious nodes, validating its effectiveness in ensuring the integrity of the collaborative learning process. Figure 3: Robustness verification results of FL-QLMS against various Byzantine attacks.
## 6 Conclusions and Future Works  
Accurate classification in WQI is crucial for ensuring the availability of potable water and monitoring pollution. Consequently, effective planning and management of water resources significantly benefit from precise predictions of groundwater levels. This study aimed to develop an IoT-based framework within which a new model would be capable of accurately predicting the water quality in groundwater, rivers, and lakes by learning from WQI evaluations in regions of China. We also tested the model accuracy using unseen data from Bangladesh, on which the model performed very well. Some traditional systems are available for assessing water potability for drinking and other household activities. While these approaches are trustworthy to local communities, they tend to be expensive and time-consuming. Meanwhile, using this IoT-based wireless framework, people and organizations can easily assess the quality of the water. Not only can they monitor the parameters of the water but also the sensors' statuses and whether they need maintenance or not. Also, the proposed ensemble model is very accurate, so people can rely on it. Furthermore, the visualization can be seen from anywhere in the world, which is a good thing because people are always busy, and they often have to move around too. There is no need to be concerned about storage and connectivity issues, as the AWS cloud service and the InfluxDB database are both scalable. Additionally, the Grafana interface provides the most effective visualization and surveillance of the data, which is beneficial for comprehending the parameters and making classifications. The proposed wireless IoT framework is a monolithic service; however, it can be adapted to a microservice system if an organization wishes to expand their monitoring of water quality. In the future, we intend to incorporate additional sensors and visualization of the water flow from one location to another, as well as the water's velocity.  
Conceptualization: H.R.R. and M.S.B.S; supervision: [PERSON]; methodology: M.S.B.S. and H.R.R.; data curation: M.S.B.S. and A.R.; software: H.R.R. and M.S.B.S.; formal analysis: M.M.I., M.A.U. and M.Z.M.; writing--original draft: M.A.U., M.S.B.S., H.R.R., M.K.H.S. and A.R.; investigation: M.M.I., M.Z.M. and M.K.H.S.; writing--review and editing: M.M.I.; project administration, M.A.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data will be made available on request.  
This research was conducted in the Emerging Data Science Lab, the Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.  
The authors declare no conflicts of interest.
# The Impact of Distributed Energy Storage on Distribution and Transmission Networks' Power Quality
## 4 Results
### Comparing the Variants' Power Quality Performance Results with Expectations
#### 4.3.2 Correlation between ESS Integration Architecture and Power Quality Improvement  
The result of the data analysis illustrates that between the Distributed ESS Integration Architecture and power quality improvement exists a positive and significant relationship to the Centralized ESS Integration Architecture. In particular, the performance of the DN-D_All variant wherein the integrated ESS provided as high as 4.57% in an average improvement of voltage magnitude and 2.07\({}^{\circ}\) in voltage angle compensation. Having said that, the outperformed centralized ESS integration architecture variant, DN-C-STH, was a better performer than the TN-D_All variant despite the fact that the TN-D_All variant employed the Distributed ESS Integration Architecture.