esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A detection or analysis model depends on the type of classifier, detection method, or dataset. It is important to find an optimization model that is suitable for domain and analysis/detection methods. However, it is impossible to search for the optimization model from all existing models (a multitude of models exist). An optimization algorithm can search for an optimization model that is suitable for the classifier/detector or dataset, thus solving this problem. Feature selection is the best solution for the model optimization problem [8] because it can search both the optimization sub-feature set and model. The model optimization problem is similar to finding an adaptive training model in learning algorithms [9]. We proposed an advanced fitness function for anomaly-detection performance. To evaluate the proposed method, we performed experiments using a genetic algorithm (GA) and machine-learning classifiers. Chromosomes were decoded according to the anomaly-detection domain to enhance the detection rate and reduce the error rate. Additionally, we proposed an improved fitness function that finds the most ideal feature set for feature selection, raising the detection rate, reducing the error rate, and enhancing detection speed using only a subset of features. This method is not dependent on a specific algorithm but can be adaptively applied to a variety of detection algorithms. It is possible to reduce overfitting with this method, as problems resulting from imbalanced and skewed data can be partially alleviated through feature selection. The proposed fitness function showed better detection performance compared to other PCA-based feature-selection methods, with only 21 features out of 41 features based on knowledge discovery and data mining (KDD) when it was GA-based. Even when only 10% to 20% of the features were used, the detection performance index showed an F1-score of approximately 0.98. The contributions of our study include the following:* Defining an advanced fitness function for an optimal intrusion-detection model using feature selection. * Technology to build an efficient detection model based on a method that can reduce the dimensions of a high-dimensional intrusion-detection dataset. * Proposal of a function method for intrusion detection usable in optimization algorithms and reinforcement learning. * Proposal of an improved method for dimensionality reduction. This paper is organized as follows. In Section 2, several studies related to anomaly detection and proposed algorithms are introduced. In Section 3, we propose intelligent feature selection using GA for anomaly detection. Section 3 covers the formulations and methods of the proposed algorithm. Section 4 discusses the results of the experiment performed using the proposed algorithm. Conclusions are drawn in Section 5.
## 3 Methodology  
### Basic Workflow  
Figure 2 illustrates a complete working framework model, where the training work is divided into two parts: basic training and transfer training [33]. When training for a specific set of manufacturing parameters, the whole training process goes into the left side of the basic training process, where finite element methods are used to obtain and pre-process the input. After the training stage, the well-trained model parameters are saved. When new manufacturing features, such as print path, print material, and other process parameters, emerge, the training process enters the transfer training process. The saved model parameters can be utilized as the initial parameters to fine-tune the performance of the model. The training cost can be significantly reduced by the transfer training method without losing the accuracy [34]. Thus, the use of transfer training makes the training stage a dynamic process to adapt to different situations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Name** & **Units** & **Value** \\ \hline \multirow{3}{*}{Manufacturing parameters} & DC Current & A & 90 \\  & Voltage & V & 80 \\  & Welding speed & mm/min & 90 \\ \hline \multirow{3}{*}{Material properties} & Density & g m\({}^{-3}\) & 3.525 \\  & Thermal conductivity & W m\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\) & \(1.79e^{-6}T^{2}+4.77e^{-3}T+15.9\) \\ \cline{1-1}  & Enthalpy & J g\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\) & \(3.01e^{-5}T^{2}+0.69T-92.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Manufacturing parameters and material properties in the experiment.
# Post-Disaster Recovery Monitoring with Google Earth Engine
## 2 Materials and Methods
### Methods
#### Accuracy Assessment  
The overall user's and producer's accuracies were calculated for each time step classified maps (i.e., T0-T4) to assess the accuracy of the produced land cover classification results. A proportion of 30% of the collected ROIs for each class were selected using stratified random sampling for testing and computing the accuracy measures [51]. To do so, we adapted the code script used by [PERSON] et al. [43] and executed it within the GEE environment. Selecting the training and ground truth ROIs was challenging for some classes due to similarities between the land cover classes (e.g., crop land and vegetation classes). We tried to minimize the effect of these inaccuracies using different sources (e.g., very high-resolution satellite images) in selecting the ROIs. In addition, considering debris/rubble as the damaged area may cause inaccuracies, e.g., in the case of storm surge that may relocate/wash debris to other regions covering the intact built-up area [13].
## 4 Conclusions  
This study aimed to investigate the removal of iron impurities from Saudi low-grade pyrophyllite ore. The study compared the use of a dry high-intensity magnetic separation approach with a combined one that involves microwave treatment prior to dry magnetic separation. The ore characteristics show that pyrite and hematite were the main iron impurities. Different parameters were studied, including roll speed, feed rate, field intensity, and feed particle size, for the dry high-intensity magnetic separation process. The microwave irradiation time and magnetic field intensity were studied for the combined method. The results indicated that the optimum conditions for dry magnetic separation were a feed rate of 24 kg/h, roll speed 40 rpm, magnetic intensity to 12,000 Gauss, and feed size of 0.125 + 0.045. This achieved a lower iron recovery 21.92%, accompanied by an alumina recovery of 80.47%. On the other hand, 30 min of irradiation and a magnetic field intensity of 2000 Gauss were optimal for the microwave treatment process. This study shows that using dry high-intensity magnetic separation (DHIMS) at 12,000 Gauss achieved an iron recovery of 21.92%, accompanied by an alumina recovery of 80.47% in the non-magnetic portion. The utilization of a combined approach involving microwave treatment and magnetic separation proves to be a promising and effective method for enriching low-grade Saudi pyrophyllite, as it achieves an impressive lower iron recovery of 11.2% in the non-magnetic portion and a removal efficiency of 89%, in addition to the higher the alumina content of 91.31% in the non-magnetic yield. However, according to international market specifications for pyrophyllite, the concentrate obtained through the combined method could fetch USD 480/t [6; 26; 36]. On the economic side, the combined method utilizes a lower magnetic  
Figure 12: Effect of magnetic field intensity on Fe\({}_{2}\)O\({}_{3}\) and Al\({}_{2}\)O\({}_{3}\) recovery% in non-magnetic fraction treated microwave.  
field intensity (2000 Gauss) compared to the high-intensity magnetic separation method (up to 12,000 Gauss), leading to reduced energy consumption and costs. Additionally, the entire process is conducted under dry conditions, eliminating the need for water usage and associated drying expenses, thus maximizing efficiency in workspace utilization.  
Conceptualization, H.A.M.A.; formal analysis, N.K., H.A.M.A. and M.A.A.; resources, M.A.A.; data curation, M.A.A. and H.A.M.A.; writing--original draft preparation, H.A.M.A., M.A. and N.K.; writing--review and editing, H.A.M.A.; visualization, H.A.M.A., M.A.-A. and N.K.; supervision, H.A.M.A.; funding acquisition, H.A.M.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (D1434-135-002). The authors, therefore, gratefully acknowledge the DSR's technical and financial support.  
Not applicable.  
All data are included in the article.  
The authors declare no conflict of interest.
# Fast Numerical Reconstruction of Integral Imaging Based on a Determined Interval Mapping
## 2 Numerical Reconstruction of the Integral Imaging
### Proposed Numerical Reconstruction
In other words, the \(\Delta\tau\) is inversely proportional to the magnification factor \(M\) because of the relation of \(M=L/g\). Hence, when the reconstruction plane moves further away from the virtual pinhole array, the calculated value of the interval \(\Delta\tau\) becomes smaller. The number of pixels of the refocused image is different according to the conventional and proposed method. In the conventional method, the resolution \(S_{c}(x,y;L)\) of the refocused image at the distance \(L\) can be expressed as \(\{(K_{x}+M-1)s_{x},(K_{y}+M-1)s_{y}\}\) when \(K_{x}\) and \(K_{y}\) are the total numbers of EIs along \(x\)- and \(y\)-direction. If the reconstruction plane \(L\) is increased, the resolution of \(S_{c}(x,y;L)\) will also increase due to the relationship between \(M\) and \(L\), where \(M=L/g\). However, since the proposed method does not use interpolation to magnify each EI, the number of pixels of each shifted EI is not changed regardless of the distance \(L\). Thus, the number of pixels \(S_{p}(x,y;L)\) of the superimposed image by the proposed method is expressed as  
\[S_{p}(x,y;L)=\left(\frac{K_{x}+M-1}{M}\times s_{x},\frac{K_{y}+M-1}{M}\times s _{y}\right) \tag{2}\]  
In Equation (2), we can realize that \(S_{p}(x,y;L)\) of the superimposed image is inversely proportional to the value \(M\). Thus, \(S_{p}(x,y;L)\) in the proposed method decreases depending on the increasing reconstruction plane \(L\) because of the relation of \(M=L/g\). Additionally, when the reconstruction plane \(L\) is moving close to the virtual pinhole array, \(S_{p}(x,y;L)\) increases. This variation of \(S_{p}(x,y;L)\) can affect the computation time during the numerical reconstruction of II.
## 4 Proposed Optimizations
### Optimizing Instruction Scheduling to Reduce Hazards and Hardware Resources  
In order to optimize hardware resources and reduce hazards, it is common practice to perform instruction scheduling. As seen in Column three of Table 2, \(Instr_{7}\) and \(Instr_{8}\) are executed in two separate clock cycles, where \(T_{3}\) is first computed as the result of \(T_{2}\times T_{2}\), and then used as an input for the subsequent multiplication in \(Instr_{8}\) to compute \(Z_{d}\). However, an alternative approach is described in column six of Table 2, where the squarer unit is used immediately after the multiplier unit. This allows \(Instr_{7}\) and \(Instr_{8}\) to be executed in a single clock cycle, resulting in several benefits. Firstly, it reduces the total number of instructions required, leading to better performance. Secondly, it reduces the number of required storage elements from \(14\times m\) to \(10\times m\), thereby saving valuable hardware resources. Lastly, it also reduces the clock cycles required when computing PM for \(m\) bit (233).  
The scheduling approach outlined in column six of Table 2 has the advantage of resulting in only a single RAW hazard in the context of pipelining, as noted in column seven. When computing the PA and PD formulations using a 2-stage pipelined architecture, this approach requires a total of 16 clock cycles, which is fewer than the number of clock cycles required when executing \(Instr_{7}\) and \(Instr_{8}\) in separate cycles. Overall, the reduction in the number of clock cycles required, coupled with the reduction in the number of storage elements needed, makes this approach a more efficient use of hardware resources. Additionally, by reducing the total number of instructions required, this approach can also improve performance.
## 5 Experiment
### Inference Efficiency Analysis  
We have implemented a prediction model using batch processing with a batch size of 128 data points at a time. This ensures that the model predicts 128 data points in parallel, thereby increasing the prediction speed. It is important to note that setting the batch size hyperparameter depends on the GPU configuration. Note that if we use a dataset consisting of 20% of the original sampling signal for training, the remaining 80% of the data points need to be predicted. The more data we use for training, the fewer test points we have for prediction. The total time required for predicting the entire dataset can be calculated by multiplying the batch size by the number of batches. Since the model predicts 128 data points simultaneously (due to the batch size of 128), the model only needs to predict a certain number of batches. The overall prediction time of the model is then divided by the total number of data points to obtain the average prediction time per data point. By organizing and optimizing the prediction process in batches, we can leverage parallel processing and enhance the prediction speed, especially when dealing with large datasets [64].  
Based on Table 2, we can analyze the effectiveness of DeepChaos+ compared to other methods. Note that DeepChaos+\(t_{tiny}\), DeepChaos+\(s_{small}\), DeepChaos+\(m_{medium}\), and DeepChaos+\(t_{large}\) are referred to as student models in this context. Looking at the runtime performance, both BiLSTM and GRU-D exhibit increasing runtime values as the percentage of the original data increases. However, the DeepChaos+ models consistently show significantly lower runtimes across different data percentages. Even the largest student model, DeepChaos+\(t_{large}\), has remarkably lower runtimes compared to BiLSTM and GRU-D. As the  
Figure 3: The training performance of DeepChaos+ in the 60% dataset is shown in the **left** figure, while the learning performance of the student model is depicted for different sizes in the **right** figure. The red line in the right figure represents the training time, indicating that, as the size of the student model increases, the training time also lengthens.  
model size increases from tiny to small, medium, and large, the DeepChaos+ models have slower execution times as the number of parameters increases.
## 1 Introduction
Additionally, TCP/IP's connection-oriented approach does not natively support the data-driven and location-independent workflows required for INC, such as dynamic task migration or localized processing within the network fabric. This underscores the need for networking paradigms that natively integrate computation within the communication fabric [8]. In this context, Information-Centric Networking (ICN) stands out as a promising solution [9]. By shifting the focus from host addresses to data, ICN enables inherently flexible and efficient communication that aligns with the principles of INC. Its features, such as built-in caching and data-centric routing, facilitate adaptive task placement, dynamic resource allocation, and reduced data redundancy. Moreover, ICN's ability to decouple data from its physical source provides a robust foundation for addressing mobility and scalability challenges, making it a natural enabler for in-network computing. In this paper, after introducing the INC concept, we delve into the existing literature that positions ICN as its enabling networking paradigm. We examine critical components for deploying INC via ICN, including service-naming schemes, executor selection strategies, compute reuse mechanisms, and security considerations, presenting a structured overview of the state of the art. Moreover, we outline open challenges and potential solutions, emphasizing the need for specific advancements, including semantic routing, collaborative caching, and adaptive service placement to fully realize the potential of INC via ICN. As shown in Table 1, several surveys have been published so far that separately consider ICN and INC approaches. However, to the best of our knowledge, this is the first work to comprehensively explore INC through the lens of ICN, with a particular emphasis on addressing network layer challenges. The rest of this paper is organized as follows: Section 2 introduces the INC paradigm with two representative use cases. Section 3 presents the ICN paradigm and focuses on a specific implementation, namely Named Data Networking (NDN). Section 4 discusses INC via ICN, by considering several building blocks like service naming, executor selection strategies, resource discovery mechanisms, compute reuse and security. Section 5 discusses research perspectives, before concluding this paper in Section 6.
# A 920-MHz Dual-Mode Receiver with Energy Harvesting for UHF RFID Tag and IoT +
## 4 Proposed Dual-Mode Receiver
### Entvelop Detector with Voltage Bias  
The proposed envelop detector is shown in Figure 7. The voltage bias is turned off and the circuit can extract the envelope of the received OOK modulated RF signal without power consumption in LP mode.  
The amplitude of the input RF signal and the amplitude of the envelope detector's output signal versus input RF power are shown in Figure 8. The output amplitude of the passive envelope detector is over 100 mV when the input power of RF signal is larger than \(-18\) dBm, which is easy to demodulate. But for input power less than \(-18\) dBm, the amplitude of the output envelope signal is almost zero [20].  
When operating in HS mode, the envelope detector is biased by an internal voltage reference and buffer. The sensitivity is improved, and it becomes more suitable for operating at low input power. With an input power from \(-40\) dBm to \(-20\) dBm, the amplitude of the output envelope signal is from 10 mV to 100 mV, which cannot meet the needs of the demodulation circuit in the subsequent stage. The signal is appropriately amplified and then demodulated. The bias voltage is generated by \(V_{BE}\) of a BJT device \(Q_{1}\) and voltage divider (\(M_{10}\)\(\sim\)\(M_{13}\)). The CTAT type bias voltage can compensate the worse sensitivity of the envelope detector at low temperature.  
Figure 6: Architecture of proposed dual-mode direct envelope detector based receiver (ED-Rx).  
Figure 7: Schematic of proposed dual-mode envelope detector circuit.  
The output noise of the envelope detector is shown in Figure 9. By calculating the signal-to-noise ratio of the output port of the envelope detection circuit, the sensitivity of the receiver can be estimated in the most direct way [8]. The output signal-to-noise ratio can be calculated by  
\[SNR_{OUT,ED}=S_{ED,OUT}{{}^{2}}/\sigma_{EO,BB}{{}^{2}} \tag{4}\]  
\[P_{Sensitivity\ by\ SNR}=\sqrt{2\cdot SNR_{min}\cdot\sigma_{EO,BB}{{}^{2}}/K_{ \
u}{{}^{2}}} \tag{5}\]  
For a 200 kHz baseband signal bandwidth, the output port noise power of the envelope detection circuit \(\sigma_{EO,BB}{{}^{2}}\) is approximately \(9\times 10^{-8}V^{2}\), \(K_{\
u}\) is the amplitude ratio of envelope detector's output and input signal. Assuming the minimum amplitude of the output envelope signal is 10 mV, then the SNR is calculated to be about 30 dB, and the receiver sensitivity estimated from the SNR is \(-35\) dBm. Considering the passive gain of impedance matching, the sensitivity of the receiver in HS mode is estimated to achieve \(-45\) dBm.  
Figure 8: (**a**) Amplitude of the input RF signal versus input RF power; (**b**) amplitude of the envelope detector’s output signal versus input RF power.  
Figure 9: Output noise of proposed envelope detector in high-sensitivity (HS) mode.
## 5 Results and Discussion
### Accuracy Analysis  
The F1-Score [45], used in this evaluation, is the harmonic mean between precision and recall. It provides a balanced measure of model performance, especially useful when the dataset is imbalanced, as in wildfire prediction tasks. All models demonstrated exceptional accuracy, with values consistently approaching 1.0 across experiments (Figure 4). The Transformer architecture achieved the highest mean accuracy (0.9995), followed by the CNN (0.9992) and Actor-Critic (0.9990). Statistical significance testing (paired \(t\)-test, \(p<0.05\)) confirmed that the numerically small differences represented statistically significant performance variations.  
Table 5 presents the confusion matrix results for each wildfire prediction model. True Positives (TP) represent correctly classified wildfire occurrences, while True Negatives (TN) correspond to correctly identified non-wildfire events. False Positives (FP) indicate cases where a wildfire was incorrectly predicted, and False Negatives (FN) denote missed wildfire events.  
The results show that the CNN and Transformer models achieved a perfect classification, with no False Positives or False Negatives. The Actor-Critic model slightly underperformed, with one False Negative (FN), meaning it failed to detect one wildfire case. This slight misclassification could be due to the stochastic nature of reinforcement learning, which sometimes struggles with precise classification boundaries [46].  
These findings indicate that deep learning models, particularly CNNs and Transformers, are highly reliable for wildfire prediction, achieving zero FP and FN rates across all experiments. Meanwhile, the Actor-Critic model remains a promising alternative, though it may require additional fine-tuning or hybrid approaches to reach the same level of precision. Future research could explore ways to enhance reinforcement learning techniques by integrating auxiliary loss functions or combining supervised learning with reinforcement mechanisms [47].  
Figure 8: AUC-ROC comparison across 100 experiments. Note: The three curves are fully overlapped, as all three models achieved identical AUC-ROC values (1.0).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **TN** & **FP** & **FN** & **TP** \\ \hline CNN & 88 & 0 & 0 & 198 \\ Transformer & 88 & 0 & 0 & 198 \\ Actor–Critic & 88 & 0 & 1 & 197 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix for each modelAll performance metrics in Table 4 are the result of 100 independent experimental runs using randomized data partitions. Each run included approximately 164,450 wildfire-positive cases and 45,790 non-wildfire instances.