esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Time-Frequency-Space Resource Allocation Method in Sharing Network
### Mathematical Modeling  
The Shannon theorem, represented by the formula \(C=Blog_{2}(1+S/N)\), describes the relationship between the maximum achievable transmission rate in a channel with finite bandwidth, subject to random noise, and the channel bandwidth and signal-to-noise power ratio. Here, \(C\) represents the maximum rate that the channel can support, often referred to as channel capacity; \(B\) is the channel bandwidth; \(S\) stands for average signal power; \(N\) represents average noise power; \(S/N\) represents SNR. By adjusting the bandwidth and SNR, the maximum throughput for a single carrier can be achieved. To achieve optimal network-level performance in a multi-user, multi-carrier scenario inspired by the principles of the Shannon theorem, we model the allocation of time-frequency-space resources for multiple users as a mathematical optimization problem. Figure 7: The framework of the time-frequency-spatial resource allocation method. Specifically, we divide the bandwidth resource \(B\) into segments for each scheduling time slot, introducing the time-domain parameter \(t\) and the frequency-domain parameter \(b\). Since we aim to allocate bandwidth resources to multiple users, we introduce the user parameter \(k\). Detailed explanations of the parameters are listed in Table 2.  
Firstly, in the scenario of a single-cell and single-user, the Shannon formula can be transformed as follows:  
\[C=\underset{BW_{b}(t)}{argmax}\sum_{t=0.5}^{SchTime}\sum_{b=1}^{Alloc Freq}\left(BW_{b}(t)\bullet log\left(1+\frac{S}{N}\right)\right) \tag{1}\]  
The meanings of each variable in the formula are as follows:  
* \(BW_{b}\left(t\right)\): the \(b\)-th spectrum resource allocated at time \(t\). * _SchTime_: the total scheduling duration. In this paper, _SchTime_ = 1 ms, and the step length is 0.5 ms.
* _AllocFreq_: the number of copies of the allocated bandwidth resource. The granularity is RBG (integer). Secondly, in the scenario of a single-cell and multi-user, the above formula can be further evolved into:  
\[C=\ C_{UE_{1}}+\ C_{UE_{2}}+\ C_{UE_{3}}+\ldots+\ C_{UE_{N}}=\sum_{k=1}^{UserNum }C_{UE_{k}} \tag{2}\]  
\[C=\underset{a_{k,b}(t),BW_{b}(t)}{argmax}\sum_{k=1}^{UserNum}\sum_{t=0.5}^{ SchTime}\sum_{b=1}^{Allocreq}\left(\alpha_{k,b}(t)\bullet BW_{k,b}(t)\bullet log\left(1+ \frac{S}{N}\right)\right) \tag{3}\]
## 3 Controller Design
### Analysis and Comparison of the Simulation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Operating Condition**} & \multirow{2}{*}{**Step**} & \multicolumn{3}{c}{\(\pi_{T}\) (Min/Max)} \\ \cline{3-5}  & & \(H_{\infty}\)/[PERSON] & \(H_{\infty}\) & \(LQR\) \\ \hline \multirow{4}{*}{\(Height=0\) km} & \(N_{L}\) (5\%) & \(0.0021/-0.0020\) & \(0.0076/-0.0083\) & \(0.0031/-0.0032\) \\  & \(N_{L}\) (\(-5\%\)) & \(0.0020/-0.0023\) & \(0.0073/-0.0086\) & \(0.0031/-0.0033\) \\  & \(\pi_{T}\) (5\%) & \(0.0617/0.0381\) & \(0.0980/0.0311\) & \(0.0640/0.0315\) \\  & \(\pi_{T}\) (\(-5\%\)) & \(-0.0380/-0.0620\) & \(-0.0320/-0.1040\) & \(-0.0340/-0.0645\) \\ \hline \multirow{4}{*}{\(Height=0\) km} & \(N_{L}\) (5\%) & \(0.0035/-0.0033\) & \(0.0070/-0.0078\) & \(0.0125/-0.0125\) \\  & \(N_{L}\) (\(-5\%\)) & \(0.0032/-0.0035\) & \(0.0068/-0.0080\) & \(0.0125/-0.0125\) \\  & \(\pi_{T}\) (5\%) & \(0.0670/0.0350\) & \(0.0962/0.0318\) & \(0.0955/-0.0149\) \\  & \(\pi_{T}\) (\(-5\%\)) & \(-0.0350/-0.0680\) & \(-0.0330/-0.1020\) & \(0.0140/-0.1115\) \\ \hline \multirow{4}{*}{\(Height=5\) km} & \(N_{L}\) (5\%) & \(0.0019/-0.0023\) & \(0.0070/-0.0071\) & \(0.0010/-0.0010\) \\  & \(N_{L}\) (\(-5\%\)) & \(0.0018/-0.0025\) & \(0.0069/-0.0073\) & \(0.0008/-0.0009\) \\  & \(\pi_{T}\) (5\%) & \(0.0640/0.0370\) & \(0.1050/0.0290\) & \(0.0582/0.0434\) \\  & \(\pi_{T}\) (\(-5\%\)) & \(-0.0380/-0.0650\) & \(-0.0310/-0.1090\) & \(-0.0435/-0.0561\) \\ \hline \multirow{4}{*}{\(Height=10\) km} & \(N_{L}\) (5\%) & \(0.0045/-0.0048\) & \(0.0100/-0.0139\) & \(-\)NaN\(/-\)NaN \\  & \(N_{L}\) (\(-5\%\)) & \(0.0044/-0.0050\) & \(0.0098/-0.0140\) & \(-\)NaN\(/-\)NaN \\ \(Ma=1.0\) & \(\pi_{T}\) (5\%) & \(0.0670/0.0310\) & \(0.0870/0.0290\) & \(-\)NaN\(/-\)NaN \\  & \(\pi_{T}\) (\(-5\%\)) & \(-0.0280/-0.0690\) & \(-0.0310/-0.1010\) & \(-\)NaN\(/-\)NaN \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The output \(\pi_{T}\) extremes at different conditions. (NaN—Not a Number)  
Figure 13: Magnitudes in different conditions during \(N_{L}\) steps.
# Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors
## 6 Further Discussion  
From the analysis in Sections 3 and 4 and simulation results in Section 5, we may conclude the following points, which highlight the outcome from the major contribution of this paper.  
* The measurement Equation (4) suggests that the optimisation of sensor trajectories is required to reduce state estimation error (or maximize FIM) of the underlying system.
* The three closed-form reward functions derived by approximation are applicable for the optimisation aiming to maximize Fisher information. Identical performances may be achievable when (4) describes a single sensor case (\(S_{1}=S_{2}\)).
* Only the determinant of FIM should be used for the case where cooperative sensing from multiple sensors is involved.
* Since the target state in the FIM calculation can only be approximated by the estimate from tracker, the reward calculated by DetFIM can be senseless if the track error is significantly large.
* In the N-Step ahead sensor trajectory optimisation process described in this paper, computational load can become intractable if a large number of sensors are jointly considered or a large N is chosen. Researchers are investigating other solutions, such as the Rapid Random Exploring Tree numerical sampling method [29], in order to address the computation issue.  
Figure 8: (a) RMS errors of the estimated target trajectory with sensor trajectories optimized under different reward methods for a \(N=2\)-step ahead decision process; (b) RMS errors of the estimated target trajectory with sensor trajectories optimized under DetFIM for \(N=1,2,3,4\). Statistically, there is little difference between \(N=3\) and \(N=4\) due to the bound on the number of hypotheses kept at each step by MaxH.
# PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm
## 4 Functional Requirements for PDCA 4.0
### Real-Time Visualization System to Consult Production Data (R4)  
A real-time visualization system is needed so that relevant information is displayed in real-time, enabling the user to have knowledge about the current state of the production system, and avoiding decision making based on obsolete data. This requirement is present in conceptual studies that indicate the role of MES to determine KPIs, create reports, and provide user interfaces to visualize and manage shop floor operations [56; 57]. Data from the value chain can also be visualized through digital platforms, enabled by eVC [43]. A digital twin, integrating different types of data from the manufacturing site and recreating a production line in a digital space, can also be used as the basis for the display of production and product information [51]. Other authors refer to CPS as useful for real-time data collection, which allows for effective KPI monitoring [9]. Lastly, AR/AsR devices (Sim&Virt) can also be used to display relevant process information, aiding users in problem-solving actions [12]. From a support perspective, IoT may be applied in order to provide real-time visualizations of information [47], and Cloud may be applied in order to access the collected data [9; 12]. This requirement is further present in practical use cases; namely a data collection system that uses a CPS in order to monitor and visualize KPIs in real-time [63], and a RFID (Sens&Act)-based system used to collect data from the shop floor and present process data, with the potential to be integrated with VSM [65].
# Long-Lifetime Event-Driven Wireless Monitoring System for Pole-Mounted Transformers
## 5 Experimental Results
As this frequency decreases, the energy consumption in the proposed monitoring system tends to be dominated by the always-on EC block. The difference between the calculated and measured power consumption tends to increase as abnormality occurrence frequency increases, because the switching power is neglected. Figure 18 compares the  
Figure 16: Measured power and energy consumption in each block for (**a**) conventional monitoring system without an EC and (**b**) proposed event-driven monitoring system. Figure 17: Energy consumption of the proposed event-driven monitoring system as a function of abnormal input occurrence frequency. lifetime of a 7500 mAh battery in a conventional monitoring system without an EC and in the proposed event-driven monitoring system. The battery lifetime in the conventional monitoring system without an EC, as shown in Figure 4a, was calculated based on data sheets and obtained as approximately 48 days, regardless of abnormality occurrence frequency. By contrast, the same battery in the proposed event-driven monitoring system lasts a maximum of 8572 days, when the occurrence frequency is low. This means the battery lasts up to 120 times longer than in a conventional system. The simulation and experimental results for the SNR of the proposed event-driven monitoring system are compared as the input RMS noise level is varied in Figure 19.  
The experimental SNR is lower than the simulated SNR by up to 1.86 dB because of the additional noise added to the RMS converter and the peak detector in the experiments. Figure 20 shows the frequency response of \(v_{\mathrm{PEAK}}\) with a pure sine wave as input, excluding the BPF to allow investigation over a wide frequency range. The 3 dB point, at which the output power drops to half, is as high as 21 MHz. Figure 19: SNR of the proposed event-driven monitoring system with varying input RMS noise level. Figure 18: Lifetime of a 7500 mAh battery in different monitoring systems as a function of abnormality occurrence frequency.
# A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning
## 4 A Genetic Crow Search Algorithm (GCSA) for Optimization of Operation Sequencing
### Evaluation Criterion of the Operation Sequencing Problem  
Numerous studies conducted to this day have taken into account two most common evaluation criteria for operation sequencing, manufacturing (production) time and cost. According to [6, 8], detailed information about tool paths and machining parameters is not available and therefore it is not possible to accurately determine the machining cost and machining time. Hence, the total cost of production is adopted as the optimization criterion in this research. The total production cost with weight coefficients consists of the five cost factors:  
1. **Total machine cost (TMC)** -- the total sum of costs of used machines where machine cost indices denote the constant cost values for each machine.
2. **Total tool cost (TTC)** -- the total sum of costs of used cutting tools, where tool cost indices denote the constant cost values for each tool.
3. **Total machine change cost (TMCC)--the product of the number of machine changes and machine change cost indices, which denote the cost values of machine replacement.
4. **Total tool change cost (TTCC)--the product of the number of tool changes and tool change cost indices, which denote the constant cost values of tool replacement.
5. **Total setup cost (TSC)--the product of the number of setup changes and setup change cost indices which denote the constant cost values of a single machine setup.**  
The full description of all cost factors can be found in [4, 5, 6, 7, 8, 11, 14]. The total weighted production cost (TWPC) finally represents the sum of these five cost factors:  
\[\textbf{TWPC}\ =\ w_{1}\cdot TMC+w_{2}\cdot TTC+w_{3}\cdot TMCC+\ w_{4}\cdot TTCC+w_{5}\cdot TSC \tag{3}\]  
where \(w_{1}\)-\(w_{5}\) are the corresponding weight coefficients of cost factors.
## 3 Experiment and Analysis
### Experimental Results and Analysis
#### 3.2.2 Configuration of Experimental Parameters  
In order to achieve a better convergence effect and detection speed in the experiment, the Adam optimizer was selected during model training, and the initial learning rate wasset to 0.0032. The learning rate was adjusted by using the periodic cosine annealing strategy, the cosine annealing hyperparameter was set to 0.12, and the learning rate momentum was set to 0.843. In order to improve the recognition accuracy of the test training, the picture training size was normalized and adjusted to 640*640, and the Batchsize was set to 32. The maximum number of iterations was 2000, a hot reboot was performed after every ten epochs, and training stopped when the maximum number of iterations was reached. The IOU threshold was set to 0.6 and the confidence threshold to 0.35 during the test. During the experiment, the confidence parameters were set to 0, 0.25, 0.35, and 0.8, and the IOU thresholds were set to 0, 0.35, 0.6, and 1, respectively, to conduct the training and debugging of the model. When the final confidence parameter was 0.35 and the IOU threshold was 0.6, a better experimental effect could be achieved. Considering that the size of the graphics card's capacity for picture training was adjusted to 640*640, and considering the actual situation wherein the size of Batchsize would have a certain impact on the performance of the model and the configuration of the hardware and software of the experimental equipment, the values of Batchsize were selected as 16, 32, and 64 for the test, and we decided to set the final value to 32. The optimal configuration of the maximum number of iterations, epoch intervals for hot restarts, and other parameters was selected based on the actual size of the experimental data set.
# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 1 Introduction
### Contributions  
The primary focus of this research is to develop a consistent and reliable model for predicting the video quality as perceived by users, which is of vital importance when managing network performances in a wide range of streaming scenarios. We seek to contribute to the ongoing pursuit of enhancing user-centric video streaming experiences and empowering service providers with robust tools for content delivery optimization. The outline of the main scientific contributions of this research is as follows.  
* Our study involved an in-depth analysis of the dataset, revealing intricate relationships between four key video properties (video frame rate, compression rate, spatial information, and temporal information) and user subjective ratings;
* We developed a fuzzy logic-based video quality assessment model (FLAME-VQA) capable of assessing the quality for a wide range of streaming scenarios based on the four video properties;
* The model incorporates an inference system that effectively tackles uncertainty and vagueness in the data. By employing fuzzy clustering and membership functions, our model enables more human-like decision-making;
* The proposed model successfully bridges the gap between objective and subjective evaluation and paves the way for more refined multimedia delivery systems that cater to users' preferences and expectations.
## 3 Proposed Adaptive KF-Based Tracking
### The Proposed Algorithm for Carrier Tracking  
In the carrier tracking loop, \(\mathbf{z}_{k}\) is a scalar in the epoch \(k\), then \(\mathbf{d}_{k}\) and \(\mathbf{\beta}_{k}\) are both scalars. Hence, the \(\mathbf{\beta}_{k}\) has the \(\chi^{2}\) distribution with 1-DOF:  
\[\mathbf{\beta}_{k}\sim\chi^{2}(1) \tag{31}\]  
Figure 3 shows the proposed AKF algorithm. The functions of the two orange blocks are to detect the system stability and adjust the process noise covariance \(\mathbf{Q}_{k}\), respectively. Apart from these two blocks, other parts in Figure 3 mainly follow the standard KF algorithm. Accordingly, the proposed tracking loop architecture is based on Figure 1, while the KF-based loop filter module is replaced by the proposed AKF algorithm. In the proposed algorithm, the measurement noise covariance matrix \(\mathbf{R}_{k}\) remains constant during the filtering process. Reference [32] stated that the \(\mathbf{Q}_{k}\)-only adaptive algorithm is superior to the \(\mathbf{R}_{k}\)-only and both \(\mathbf{Q}_{k}\) and \(\mathbf{R}_{k}\) adaptive cases in practical estimating performance.  
Figure 3: The proposed AKF algorithm’s flow chart.  
Figure 2: The PDF of the chi-square distribution.
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 2 Materials and Methods  
### Overview of the Methodology  
Several-advanced machine learning techniques like Extreme Learning Machine (_ELM_), Deep Neural Network (_DNN_), Support Vector Regression (_SVR_), Random Forest (_RF_), LASSO regression (_LASSO_), Partial Least Square Regression (_PLS_), Ridge Regression (_Ridge_) Kernel Ridge (_KRidge_), Stepwise Regression (_Stepwise_), Least Square Regression (_LS_), and Genetic Programming (_GP_) have been applied for modeling 875 samples. Multiple input parameters, including the wet density (\(Y_{h}\)), the dry density (\(Y_{d}\)), the degree of saturation (_Sr_), the plasticity index (_PI_), the water content (_w_), the void ratio (\(e_{0}\)), the liquid limit (_WL_), sample depth (\(Z\)), and the fine contents (_FC_) have been used. Firstly, from an effective viewpoint, the suitable input variables and nonlinear components are of considerable importance for efficient prediction. Thus, the Principal Component Analysis (_PSA_), Gamma Test (_GT_) and Forward selection (_FS_) methods have been used to select the optimal set of input variables. Afterward, the advanced machine learning techniques have been applied for modeling optimal inputs, and their accuracy models were evaluated through numerous statistical indicators. To assess the predictive capability of the best model, the k-fold cross-validation approach based on 10 splits has been utilized. Finally, to answer the question \"Which input variables have the most or less influence on \(C_{s}\) through the proposed model?\", a sensitivity analysis has been carried out using the step-by-step selection method.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 6 Simulation Experiments and Results
### Simulation Scenarios  
The simulation scenario considers a centrally located access point and a fixed number of uniformly distributed nodes or stations. To observe the protocol performance under different network loads, we considered a number of stations that was varied from 1 to 256. We also considered that each one of them behaves as a \"greedy\" source, which leads to what is frequently known as saturation conditions. That is, as soon as a node successfully sends a data packet, it generates another one to be transmitted. All simulations were run with a data rate of 1 Mbps and a packet payload of 65 bytes. These values of data rate and packet size are commonly found in different types of IoT applications [39; 40]. For the chosen data rate and packet size, we configured each simulation scenario with a value of bit error rate (BER) so that it produced 1% packet loss. When a packet is lost, the AP emits an error message and the affected packet is retransmitted in the following CRI. In addition, for comparison purposes, the RREQ size was chosen to be 20 bytes, which is the same length used by DCF for the RTS messages. However, the RREQ size can be decreased, since all that is required is the ID of the transmitting station. For 2 CA-R2, the predictor order to estimate N was set to 1. Each case was simulated 10 times in order to obtain mean values and provide their confidence intervals with a 95% confidence level. The whole set of parameter values is shown in Table 5.
## 2 Materials and Methods
### CNN Architecture and Hyper-Parameters Overview  
The architecture of the proposed CNN model, shown in [36,37], which comprises one input layer responsible for receiving external data, two hidden layers accountable for filtering the inputs, a fully connected layer responsible for the classification and an output layer. Each hidden layer comprises a convolutional layer, a batch normalisation layer, an activation layer and a max-pooling layer. The convolutional layer convolves the local input regions with filter kernels and then generates the output features by computing the activation function. The \(ReLU\) function is used as the activation layer to sort out the issue of vanishing gradients. The batch normalisation layer, placed between the convolutional layer and the activation layer, helps to reduce the CNN's sensitivity to network initialisation. Finally, a max-pooling layer is placed between the first activation layer and the second convolutional layer. This is known to improve the CNN's accuracy. The fully connected layer comprises as many neurons as there are labels--four, in this case. The \(softmax\) function is used as an activation layer for the latter.  
Every ML algorithm aims to minimise a loss/cost function or maximise a likelihood function, depending on the existing model, to find its optimal values and achieve the most accurate prediction. In a CNN, every unit within a convolutional layer is a slight regression model trained by learning the filter kernel's parameters. The loss function, in this case, is defined by the task. For image classification, the cross-entropy loss function is used to compute the difference between the \(softmax\) output probability distribution and the label probability distribution. The stochastic gradient descent with momentum is used to minimise the cross-entropy loss function. It is an upgraded version of the stochastic gradient descent that accelerates gradient vectors in the right direction. The hyper-parameters for the proposed CNN are shown in Table 3. Furthermore, the training options for the SGDM are set to 0.01 for the learning rate, 15 epochs, a mini-batch size of 35 and a frequency of network validation in 50 iterations. These parameters have been tuned by trial and error.