esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Inversion Model Construction, Accuracy Verification, and Salinity Visualization
#### 2.8.3 BPNN  
The back propagation neural network (BPNN) is one of the most fundamental and commonly used training algorithms in the field of deep learning [39]. The core of the backpropagation algorithm is based on the chain rule and gradient descent principle. During training, the network first performs forward propagation to compute the output result. Then, based on the error between the output and the expected output, backpropagation is used to calculate the contribution of each neuron's weight and bias to the total error, and adjust the weights and biases to minimize the error function [40]. As shown in Figure 4, the neural network consists of input layers, hidden layers, and output layers, with each neuron represented by a node.  
The hidden portion can have one or more layers, with nodes from the previous layer connected to nodes in the next layer through weighted connections, forming a directed acyclic graph topology in the feedforward neural network structure. In the BP model, a nonlinear relationship between input and output can be achieved through linear combinations of inputs, as follows:  
\[\boldsymbol{y}_{j}=f\left(\sum_{i=1}^{B}\boldsymbol{\omega}_{ji}\boldsymbol{u}_ {i}-\xi\right)\]  
where \(\boldsymbol{\mu}_{i}\) is the unit input signal; \(B\) is the number of neurons; \(\boldsymbol{y}_{i}\) is the unit output signal; \(\boldsymbol{\omega}_{ji}\) is the connection weight between input unit \(i\) and output unit \(j\); \(\xi\) is the bias; and \(\boldsymbol{f}\) is the activation function of output unit \(j\).  
In this study, the BP neural network (BPNN) was configured with a three-layer structure; the input layer had 75 nodes, the first hidden layer had 100 nodes, the second hidden layer had 50 nodes, and the output layer had 1 node. The activation function used was ReLu, with the optimizer being Adam, a learning rate of 0.001, and weight decay of 0.0001.  
Figure 4: BPNN flowchart.  
Figure 3: RF flowchart.  
The early stopping patience was set to 50, the dropout rate was 0.5, and the maximum training epochs were 2000, with a dropout proportion of 0.05. The loss function used was mean squared error (MSE).
## 3 Results
### Results of Multi-Objective Optimization Model  
A multi-objective optimization model is developed using input variables obtained from the image processing phase, where truck and excavator positions are detected and analyzed. The optimization problem aims to minimize the deviation from required truck-to-excavator assignments while simultaneously minimizing the total ready time of excavators. To systematically explore the trade-off between these two conflicting objectives, the epsilon constraint method is applied, treating the excavator's ready time as a constraint while optimizing the assignment deviation. This approach enables the identification of optimal operational strategies by illustrating how strict enforcement of excavator idle times affects assignment targets and overall system efficiency. The Pareto frontier in Figure 9 reveals that minimizing the total excavator ready time leads to a substantial increase in assignment deviation, reaching approximately 500 units in the most constrained scenario. This indicates that reducing excavator idle time forces truck assignments that deviate significantly from the target distribution, likely due to the prioritization of immediate truck-excavator pairings overachieving the required allocation targets. As the constraint on deviation is progressively relaxed, the total deviation decreases sharply, reaching nearly zero at around 600 s of ready time. This suggests that achieving a balanced assignment distribution necessitates allowing higher truck queue times, which in turn increases excavator waiting times to ensure an optimal allocation of trucks. Beyond 600-700 s of ready time, the total deviation remains close to zero, implying that further increasing excavator idle time does not provide additional benefits in assignment accuracy. This observation highlights a critical threshold beyond which additional waiting time becomes inefficient, as it does not contribute to improved assignment balance but instead results in unnecessary operational delays.  
The truck-to-excavator assignment results for the 750 s limit on total excavator ready time, as presented in Figure 10, demonstrate the effectiveness of incorporating computer vision-based input variables into a dispatching algorithm for fleet management in open-pit mines. By leveraging real-time image processing, the model accurately designates truck and excavator positions, enabling an automated and data-driven approach to optimizing fleet operations. This integration of computer vision enhances decision-making by providing  
Figure 9: Pareto frontier of 2 objective functions.  
dynamic, real-time input variables, ensuring that truck assignments align with operational constraints while minimizing deviation from target distributions. The results highlight the potential of computer vision-assisted optimization in improving the efficiency of mine dispatching systems, ultimately reducing delays and enhancing overall productivity.
## 4 Results
### Mental Workload Evaluation
The results were similar for temporal demand, \(\chi^{2}(2)=27.780,\)\(p<0.001\), as [PERSON] reached the highest mean rank with 159.75, followed by [PERSON] with 116.36 and [PERSON] with 102.91. For performance in the second task, \(\chi^{2}(2)=6.298,p=0.043\), [PERSON] reached the highest mean rank with 141.17, followed by [PERSON] with 125.01 and [PERSON] with 112.89. For effort, \(\chi^{2}(2)=21.322,p<0.001\), [PERSON] again reached the highest mean rank with 155.00, followed by [PERSON] with 119.44 and [PERSON] with 104.53. Lastly, for frustration, \(\chi^{2}(2)=12.55,p=0.002\), [PERSON] reached the highest mean rank with 149.20, while [PERSON] and [PERSON] reached similar values with 115.29 and 115.01, respectively. The effect sizes observed in the first task indicate large effect sizes for most of the observed subscales (\(0.1458<\eta^{2}<0.3491\)). A small effect size was observed only on the subscale for temporal demand (\(\eta^{2}=0.0185\)), while a medium effect size was observed on the performance subscale (\(\eta^{2}=0.0872\)). For the second task, most subscales indicated a medium effect size (\(0.0616<\eta^{2}<0.1035\)). A small effect size was observed for performance (\(\eta^{2}=0.0173\)) and frustration subscales (\(\eta^{2}=0.0424\)). As both NASA-RTLX and SEQ measure the required effort to complete the task (at least in some form), the correlation between these tools was further analyzed. A Kendall's tau-b correlation was run to determine their relationship on the entire sample of answers (all tasks, with all tools). There was a strong, positive correlation between SEQ and NASA-RTLX, used for measuring the perceived workload, which was statistically significant (\(\tau_{b}=0.493,p<0.001\)). Additionally, Point-Biserial Correlation was used to analyze the errors recognized in the second task with NASA-RTLX responses, which proved insignificant (\(r=-0.092,p=0.144\)). Correlation between the error rates in the second task and SEQ was analyzed with Pearson Chi-Square, though again, no statistically significant association between them was observed \(\chi^{2}(5)=5.704,p=0.336\)).
## 1 Introduction
### Computation and Communication Efficiency  
FL in heterogeneous industrial IoT scenarios also presents challenges in terms of computation and communication efficiency. Heterogeneous industrial IoT devices often possess varying computational resources, processing capabilities, and communication bandwidth, which can affect the overall performance and efficiency of a federated learning system.  
In heterogeneous industrial IoT environments, devices with limited processing power may struggle to keep pace with more capable devices during the local training process. This discrepancy can lead to longer training times and slow convergence of the global model. Additionally, the energy constraints of battery-powered IoT devices may further exacerbate the problem, as these devices need to balance local computation with energy consumption.  
Moreover, communication between IoT devices and the central server is critical for FL, as local model updates need to be transmitted and aggregated to form the global model. Heterogeneous industrial IoT devices often have varying communication capabilities and network conditions, which can lead to communication bottlenecks and increased latency.  
Figure 2: Scenario of heterogeneous FL in an IoT network environment. This study focuses on the heterogeneity in device specifications and the non-Independent and Identically Distributed (non-IID) nature of datasets among individual devices.
## 1 Introduction
To meet the capacity requirements of the 5G cellular system, a project was deployed and it is known as METIS (mobile and wireless communications enablers for the twenty-twenty information society). The METIS has recently been funded by the European Union [23]. Regarding exploiting advantages of D2D into NOMA, the authors in [24] investigated a model of the integration of a downlink NOMA system with D2D communications. D2D reported in [25] with resource allocation scheme is promising approach. They further derived expressions of the outage probability that both users obtain higher rates in NOMA under a fixed power control strategy. In addition, the uplink multi-carrier is considered in NOMA with support of D2D underlaid cellular networks [26]. More specifically, an iterative algorithm applying Karush-Kuhn-Tucker conditions is proposed to solve the power allocation problem in D2D NOMA [26]. The authors in [27] studied the device-to-device (D2D) assisted and NOMA-based mobile edge computing (MEC) system by deploying D2D communication for enabling user collaboration and reducing the edge server's load. In this paper, we consider a D2D transmission existing in a downlink PDMA system. The selected antenna at one BS communicating has two D2D far receivers with the aid of D2D implementation. Different from existing works on D2D PDMA [26; 27], where the end-user operating half-duplex, we assume that the D2D users operate in the FD mode and investigate outage performance taking into account both the downlink and D2D links. The key contributions of this study are summarized as follows:* In the presence of a downlink under support of multiple antenna based BS, two D2D users exhibit different outage performance. We individually investigated the performance of each end-user in such a MISO NOMA system. Compared to most existing cooperative PDMA schemes, FD scheme is enabled at the end-user. To look how good performance two far users have, two far D2D users with different QoS requirements can be paired with each other and get benefit from D2D transmission. * Different from [25], transmit selection and full-duplex are joint investigated in this study. Most important is that we provide simulation results of integration of a D2D connection to a downlink two-user PDMA system. * We provide simulation results showing that, under the fixed power allocation strategies, D2D users achieve outage behavior in the NOMA scheme. The results also indicate the probability that both D2D users obtain improved outage performance in MISO PDMA depends on the power level of the BS and the required target rates. * For system performance evaluation, the closed-form expressions for the outage probabilities are derived for both two D2D users. To highlight the impact of the system parameters on the outage performance, outage probabilities achieved at both two D2D users are simulated to verify derived expressions. **Notation:** The cumulative distribution function of a real-valued random variable \(X\) is denoted by \(F_{X}(.),f_{X}(. )\) stands for probability density functions, while \(Pr(. )\) symbolizes outage probability.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 2 Fault Types
### Electrical Faults  
Electrical faults mainly involve the incorrect connection of the motor windings, grounding errors, short-circuit of stator phase windings, and the open-circuit of the whole phase [8].  
Stator faults constitute 38% of all motor faults [9]. Among them, the inter-turn short circuit of the stator windings is the most common fault in PMSM [1; 10]. As the running time increases, due to insulation wear, overheating or overload, there may be a breakdown of the stator winding insulation system in the motor, resulting in a short-circuit fault between the stator turns [11].  
A short-circuit fault is tricky because it produces a great circulating current in the shorted path with excessive heat [12]. As a result, if not discovered and eliminated in time, it can damage more wire insulation and quickly propagate into more stator windings, which may cause the phase-to-phase fault, phase-to-ground faults and even demagnetization [13].  
The internal stator winding failure may make one of the phases open-circuited [14], although this is often due to the problems of the drive system. It can cause large electromagnetic torque fluctuations and severe mechanical vibrations of the motor. Starting and running the fault PMSM without timely treatments can result in secondary damage and even the catastrophe of the entire system [15].
## 3 Results and Discussion
### Quaternary Nitrides as Superconductor  
Our investigation into ternary systems led to an improvement and we want to expand to quaternary systems with the goal of further improving the Tc. This introduces two transition metals along with Nb. We selected the NbTaTiN system as our quaternary system because NbTaN and NbTiN have been extensively studied in experimental investigations. Additionally, in Figure 5, Ta and Ti remain close to Nb in terms of the HR. Figure 9 presents a visualization of how the Tc varies across different compositions within the Nb-Ti-Ta-N system. The corners of the ternary diagram represent the pure nitrides (NbN, TiN, and TaN), with each point within the diagram denoting a unique alloy composition based on its position. The color gradient, ranging from blue (low Tc) to red (high Tc), illustrates the predicted Tc values for each composition, highlighting the compositional trends that govern superconductivity. Higher Tc values are observed in the middle of the triangle, indicating that a balanced composition among the elements (Nb, Ti, Ta) is crucial for Tc enhancement (Table 2). While our model provides a valuable starting point for identifying potential substitutions in the NbN superconductor, it is important to recognize that it tends to underestimate the actual critical temperatures that have been observed in bulk materials [11]. Experimental validation of these systems would further enhance confidence in this work; although given the large initial dataset, which included multi-component systems, the experimental test set was significant, giving confidence in the model. Additionally, under-prediction of the Tc values is preferred to over-prediction, to ensure that the model does not suggest systems that may not be useful for superconducting applications. Tc does not just depend on the chemical makeup of the material; it is also significantly influenced by the specific  
Figure 8: Predicted variation of Tc with substitutional doping in NbN. The graph depicts the influence of the partial replacement of Nb by various other elements on the predicted Tc of nitride compounds. Each curve corresponds to a different dopant in the NbN matrix, as indicated by the legend. The x-axis represents the fraction of Nb replaced by the dopant, while the y-axis shows the predicted Tc in kelvin (K). The different symbols and colors represent various doped compounds. The peak Tc value is identified by a red arrow, which indicates the optimal doping level for the highest Tc (11.143 K at x = 0.45 for the chosen dopant). processing conditions used during synthesis. Aspects such as the purity of the materials, the heat treatment protocols, and even the microstructure of the final product can all impact superconductivity. In fact, by carefully tuning these processing parameters, researchers have been able to achieve enhanced superconducting properties beyond what might be expected from the chemical composition alone. These experimental successes underscore the potential to elevate the superconducting performance of NbN-based materials when both the chemistry and the processing techniques are optimized in tandem.
## 2 System Model  
The schematic representation of the proposed system is shown in Figure 1. The proposed system consists of a centralized monitoring-cum control room and object showcase room. From the centralized control room, the precious object presented in the object showcase room is continuously monitored by object detection and the tracking system, in that a Kalman filter algorithm is used to detect and track its location. The detection results obtained from the Kalman filter algorithm are used to control the alarm system. The same results are given to the computer to generate command information, which is transmitted as a stego image to the object protection system through a proposed closed-loop covert visual MIMO communication system. In this closed-loop security system, the surveillance camera present inside the showcase room captures the display screen along with the precious object. From the captured image, display screen region is given to the data image extraction algorithm, while the object portion of the image is fed to the object detection and tracking system. In the proposed covert visual MIMO communication system, the color cover images that contain information about the history of the precious object are used to embed the data images. Each color cover image consists of three planes, i.e., red, green, and blue, to embed the data images. After embedding, these three planes are combined to form a colour stego image, as shown in Figure 2. The combined stego images are given to the display screen. In this display screen, each 8 \(\times\) 8 pixel data block hided in the stego image is considered as a transmitting antenna. The public visiting the object showcase room can see only the cover images displayed on the screen. They cannot visualize it as an array of transmitting antennas and embedded information. The surveillance camera present inside the showcase room captures the display screen along with the precious object. Inside the object showcase room, various image processing techniques are used to segment the display screen from the captured image. The segmented display screen is considered as the received stego image. In the received stego images, each recovered 8 \(\times\) 8 pixel data block is considered as a receiving antenna. Various processes that happen in the proposed system are presented in a detailed manner in the subsequent subsection.  
Figure 1: Block diagram representation of a proposed system.  
Figure 2: Screen-Camera based visual MIMO Communication Illustration.
## 8 Conclusions and Future Work  
In this paper, we proposed a decentralised CP-ABE scheme designed to achieve secure and efficient access control in resource-constrained IoT environments. By leveraging data outsourcing, our scheme assists IoT end devices with computationally intensive encryption and decryption tasks, ensuring data confidentiality and integrity. By eliminating a central certificate authority, our architecture simplifies key management, reduces dependency on centralised entities, and enhances system flexibility.  
Although experimental results indicate that the proposed scheme does not achieve the shortest execution time across all phases, it offers significant advantages in security, scalability, and adaptability. The incorporation of randomisation within proxy keys effectively reduces the risk of key leakage, providing robust protection against potential attacks. Furthermore, the scheme performs exceptionally well in multi-attribute environments, making it highly suitable for complex IoT applications, such as smart cities, emergency rescue operations, and industrial automation.  
Nonetheless, to ensure the scheme's long-term viability in the face of evolving cryptographic challenges--particularly those posed by quantum computing--further attention to post-quantum resilience is imperative.
## 2 Electromagnetic Wave Propagation Behavior and the Principle of the Reflected Voltage
### Relative Dielectric Constant  
The relative dielectric constant of a material is the ratio of its (absolute) permittivity to the vacuum permittivity. The formula of the relative dielectric constant is expressed as follows:  
\[\varepsilon_{r}=\frac{\varepsilon}{\varepsilon_{0}} \tag{1}\]  
Different media have different dielectric constants, which will generate different reflected energies, as shown in Table 1. The dielectric constants are public information.
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
## 4. \(AsRAN\) throughput Model
\begin{split} Gain\_of\_ASRAN&=\frac{ASRAN\_total\_throughput}{CIBIC\_total\_throughput}\\ &=\frac{\sum y_{ASRAN}}{\sum y_{CUBIC}}=\frac{101.162}{62.375}=1.622.\end{split}  \tag{3}\]  
In the general case, to see the CWND fluctuation of TCP-CUBIC transmission, congestion avoidance phase is shown continuously over the whole transmission phase regardless of throughput increase phase or throughput decrease phase [22]. Considering unstable network performance of wireless multi-hop network, congestion avoidance occurs in almost every time and \(ASRAN\) can be activated in whole transmission time. Therefore, in the recovery phase right after segment loss, the throughput of \(ASRAN\) can be expected 1.622 times larger than that of TCP-CUBIC. In the evaluation section, the real experiment result will show similar value to the result calculated by the proportion from modeling.
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 9 Conclusions
### Final Thoughts and Remarks
Several related smart systems have integrated image processing-based security schemes with non-image processing-based schemes in terms of security [46; 47] since image processing has played an effective role in strengthening security;
* Many IoTSs that provide location detection services have made use of image processing techniques; (a) IoTSs will be able to provide improved services in terms of object localization if image processing is used in conjunction with the cloud and IoT settings [61]; (b) It is mentioned that image processing and position detection methods like GPS have been employed and exploited to increase a variety of services that are significant and critical in our everyday activities, such as the identification and prevention of plant catastrophes [62]; (c) UAVs, wireless sensors, drones, cloud computing, and other technologies have all been used in conjunction with image processing to improve position recognition in open areas that may be covered by the image capture process using UAVs [25];
* Many IoT healthcare applications use image processing, such as [65]. To make IoT-related services more convenient, image processing has been used, where collected images may be analyzed remotely by people; then the necessary action can be taken via the IoT platform;
* Performing computations via the cloud in recent years, an increased emphasis has been made on the need for security in relation to both the preservation of medical data and images as well as disease prediction. The healthcare business creates enormous amounts of data on a daily basis as a result of the progress of medical technologies. Cloud computing allows for the storage and management of these enormous amounts of data in a manner that is exceptionally secure. A more secure zigzag image encryption technique is utilized to identify illnesses utilizing image processing in a safe cloud computing environment [66].