esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in the Neural Network Quantization: A Comprehensive Review
## 2 Quantization Fundamentals
\[s_{in}=\frac{max_{in}-min_{in}}{2^{hw_{in}}-1} \tag{3}\]  
\[D_{in}=\frac{min_{in}-max_{in}}{2^{hw_{in}}-1}\cdot\text{round}\left(\frac{(2^{ bw_{in}-1}-1)\cdot min_{in}+2^{bw_{in}-1}\cdot max_{in}}{min_{in}-max_{in}}\right) \tag{4}\]  
\[s_{out}=\frac{max_{out}-min_{out}}{2^{hw_{out}}-1} \tag{5}\]  
\[D_{out}=\frac{min_{out}-max_{out}}{2^{bw_{out}}-1}\cdot\text{round}\left(\frac {(2^{bw_{out}-1}-1)\cdot min_{out}+2^{bw_{out}-1}\cdot max_{out}}{min_{out}- max_{out}}\right) \tag{6}\]  
\[s_{w}=\frac{th_{w}}{2^{bw_{w}-1}-1} \tag{7}\]  
where \(bw_{in}\), \(bw_{out}\), and \(bw_{w}\) are the bit width of input, output, and weight, of which 8 is commonly used. To effectively quantize the convolutional layer, it is essential to understand its underlying computational principles. The fundamental operation of the convolutional layer is:  
\[\sum\left(\text{input}^{f}_{i,j,k}\cdot w^{f}_{k,n}\right)+\text{bias}^{f}_{ k}=\text{output}^{f}_{l,m,n} \tag{8}\]  
where \(\text{bias}^{f}_{k}\) represents the \(k\)-th bias of the convolutional layer, and \(\text{output}^{f}_{m,n,l}\) denotes the output of the convolutional layer. All these data types are in floating-point format. Based on the computational principles of the convolutional layer and the proposed hybrid asymmetric quantization strategy, the method for quantizing the convolutional layer can be deduced. The activations of the convolutional layer (including both the input and output) use asymmetric quantization mapping, while the weights of the convolutional layer use symmetric quantization mapping. The computation principle for quantizing the convolutional layer is:  
\[\left\{\left(\frac{s_{in}\cdot s_{w}}{s_{out}}\cdot 2^{5}\right)\left(\sum \left(\frac{\text{input}^{f}_{i,j,k}-D_{in}}{s_{in}}\cdot\frac{w^{f}_{k,n}}{s _{w}}\right)\right)+\frac{\text{bias}^{f}_{k}+D_{in}\cdot\sum w^{f}_{k,n}-D_{ out}}{s_{out}}\cdot 2^{5}\right\}\cdot 2^{-5}=\frac{\text{output}^{f}_{l,m,n}-D_{ out trade-off between clipping range and quantization resolution, which significantly influences quantization parameters' computation. There are two main forms of quantization methods: Post-training quantization [14, 29] and quantization-aware training [30, 31]. Post-training quantization (PTQ) is the quick quantization of floating-point weights and activations after the model has been trained. Quantization-aware training (QAT) is the process of updating the weights of the model by considering the quantization process during the training process. PTQ is time-saving and convenient, while QAT can obtain a higher accuracy. In addition to PTQ and QAT, the selection of quantization bit-width and the evaluation of the accuracy loss of deep learning models after quantization are also very important techniques related to quantization. In this paper, we will analyze the development status and challenges of the above related techniques, respectively.
###### Abstract  
Neural networks (NNs) have been successfully applied to channel equalization for optical communications. In optical fiber communications, the linear equalizer and the nonlinear equalizer with traditional structures might be more appropriate than NNs for performing real-time digital signal processing, owing to its much lower computational complexity. However, the optimization algorithms of NNs are useful in many optimization problems. In this paper, we propose and evaluate the tap estimation schemes for the equalizer with traditional structures in optical fiber communications using the optimization algorithms commonly used in the NNs. The experimental results show that adaptive moment estimation algorithm and batch gradient descent method perform well in the tap estimation of equalizer. In conclusion, the optimization algorithms of NNs are useful in the tap estimation of equalizer with traditional structures in optical communications.  
Keywords:neural networks; optical communications; optimization; equalizer; tap estimation
## 3 Result and Discussion
### Effects of Reaction Parameters on the Transesterification Process (Biodiesel Yield)
#### 3.3.2 Reaction Time (min)--Catalyst Concentration (wt %)  
In general, by reviewing previous research, it was observed that depending on the type of oil used, catalyst concentration of less than or equal to 1% is required for a successful conversion of oils to fatty acid methyl ester [60]. In this research, the effect of catalyst concentration on biodiesel conversion in the range 1 wt % to 2 wt % was studied. As shown in Figure 4, the 1.0% concentration of catalyst is an optimal point to achieve higher conversion. With the addition of an excess amount of catalyst (greater than 1 wt %) the methyl ester conversion decreases. At higher catalyst concentrations, biodiesel-glycerol emulsion results in the separation of glycerol from the biodiesel becoming difficult. The results of ANOVA in Table 7 show that catalyst concentration (wt %) has a significant effect (at the level of 0.05) on biodiesel conversion. The interaction of the catalyst concentration with reaction time has a significant effect (\(p\)-value < 0.01) on methyl ester conversion.  
Figure 4: Catalyst concentration (wt %)—reaction time (min) on biodiesel conversion (%) when methanol to oil molar ratio equal to 8.11.  
Figure 3: Effect of molar ratio (methanol to oil)—reaction time on biodiesel yield (%) when catalyst concentration (wt %) is equal to 1.5.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 9 Exercises
### 5G Cryptomining Attack Detection Scenario
#### 9.1.3 Application and Integration of Machine Learning for Cryptomining Attack Detection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Max. Depth** & **Num. Estimators** \\ \hline crypto\_spider\_5g\_rf\_10\_a.pkl & \\ (Good performance) & Unlimited & 100 \\ \hline crypto\_spider\_5g\_rf\_10\_b.pkl & \\ (Medium performance) & 8 & 3 \\ \hline crypto\_spider\_5g\_rf\_10\_c.pkl & 4 & 1 \\ (Bad performance) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters for each cryptomining Random Forest Classifier model generatedThe results of Table 3 also reflect the didactic value of this exercise. By testing real traffic against these different models, the blue team will be able to easily observe the contrasting results shown by a decrease in result confidence and inconsistency in detecting attacks in the worse models. This exercise thus provides a scenario that is very similar to a real-life situation, proposing the evaluation of different ML models to check their performance in practice and allowing the end user to judge which one is best suited to their needs. In this way, we teach the student how to extract the full potential of this technology while highlighting the possible pitfalls that can occur when it is applied incorrectly.
## 3 Results
### Summary
#### 3.1.3 Implementation Method  
Section 3.4.1 described the experiments to confirm the feasibility of trajectory estimation. These experiments were conducted in a relatively disturbance-free open space. The estimation was performed for both hand-held walking and hand-swinging walking scenarios. Hand-held walking refers to a state where the device is held in one hand while the user is looking at the screen, whereas hand-swinging walking refers to a state where the user holds the device in one hand with the arm relaxed and swinging back and forth while walking. Section 3.4.2 addresses estimation in scenarios where magnetic disturbances from other devices occur, simulating a crowded environment. Section 3.4.3 focuses on indoor positioning scenarios and performing estimation along complex trajectories in environments with numerous household appliances and other devices. In each experiment, walking is performed continuously without stopping at each step.
## 2 The Mobile Robot with Range Sensors
### Obstacle Detector  
Obstacle avoidance relies on the detection of obstacles for the real-time operation of mobile robots. The sensor configuration is shown in Figure 3. A DUR5200 Ultrasonic Sensor and GP2Y0A21 YK Sharp Infrared Sensor are equipped on the mobile robot. There are three sonars (Sonar 1, Sonar 2, and Sonar 3) and four infrared sensors (IR 1, IR 2, IR 3, and IR 4) on the mobile robot, where \(\theta_{1}\) equals \(12^{\circ}\), \(\theta_{2}\) equals \(18^{\circ}\), and \(\theta_{3}\) equals \(15^{\circ}\). The detecting range of an ultrasonic sensor is from 4 to 255 cm, while the detecting distance range of the IR sensor is between 10 and 80 cm. The sensors' update rate are both 10 Hz. We assume the obstacle is located in the direction of the sensor if only a single sensor detects an obstacle. Otherwise, when two sensors detect an obstacle at the same time, we assume that the obstacle lies on the bisector of these two sensors' directions. Figure 4 shows the detection of an obstacle. For instance, if Sonar 2 detects an obstacle, the obstacle is located in front of the robot with an azimuth angle \(0^{\circ}\). Likewise, Sonar 1, Sonar 3, IR 1, IR 2, IR 3, and IR 4 detect the obstacle with azimuths of \(45^{\circ}\), -\(45^{\circ}\), -\(30^{\circ}\), -\(12^{\circ}\), \(12^{\circ}\), and \(30^{\circ}\), respectively. If both Sonar 2 and IR 3 detect an obstacle,  
Figure 2: The mobile robot’s kinematic model with the curvature constraint, where a positive curvature denotes a right (clockwise) turn. The robot is assumed as a circle. The robot velocity \(U\) and its \(x\)-component \(v_{x}\), \(y\)-component \(v_{y}\), the radial acceleration \(a_{R}\), and tangential acceleration \(a_{T}\), are shown. The O-XY coordinate system is the global coordinate frame and a local frame is attached to a representative point (the center) of the circular mobile robot.  
then the obstacle's direction will be \(6^{\circ}\), which is between Sonar 2 and IR 3. The estimated obstacle location is transformed into the global frame for the motion planner. The estimation and localization errors are accommodated by a safety distance \(r_{Safe}\) in the practical implementation of the navigation system, as in our experiment presented in Section 5.
## 4 Experiment
### Experiments on Flavia  
As illustrated in Figure 7, there are 1907 leaves from 32 plant species in the Flavia [44] dataset; the image number is about 50-70 for each species. We follow [38] to adopt the common setting: 70% images per species are selected as training images; the other 30% as testing images. There are 1352 and 555 leaf images in total in the training and testing set, respectively. The recognition results of our method and the competing methods are tabulated in Table 4. Our method achieves the highest classification rate of 99.89%, which is the average value of 100%, 99.82%, 99.82%, 100%, 99.82% for the five repeated experiments. The accuracy gain of our method over the best handcrafted MMNLBP [28] is 0.59%. The improvement of our method over the second best neural network-based method, KernelPool [36], is 0.18%. The main reasons for the superior performance of our method are twofold: complementation information utilization between various CNNs and layers; the discriminative features highlighted via feature recalibration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \# Layers & Configuration Index & Layer Configuration \\ \hline  & 1 & 16-26-36 \\  & 2 & 48-58-68 \\
3 & 3 & 78-90-100 \\  & 4 & 110-120-130 \\  & 5 & 140-152-162 \\ \hline  & 6 & 16-26-36-48 \\
4 & 7 & 58-68-78-90 \\  & 8 & 100-110-120-130 \\  & 9 & 140-152-162-172 \\ \hline  & 10 & 16-26-36-48-58 \\
5 & 11 & 68-78-90-100-110 \\  & 12 & 120-130-140-152-162 \\ \hline  & 13 & 16-26-36-48-58-68 \\
6 & 14 & 78-90-100-110-120-130 \\  & 15 & 120-130-140-152-162-172 \\ \hline  & 16 & 16-26-36-48-58-68-78 \\
7 & 17 & 90-100-110-120-130-140-152 \\  & 18 & 110-120-130-140-152-162-172 \\ \hline
16 & 19 & 16-26-36-48-58-68-78-90-100-110-120-130-140-152 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Layer number configuration of ResNet used in our experiment.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline \multicolumn{2}{c}{**Methods**} & **Accuracy** & **Year** \\ \hline \multirow{8}{*}{Hand} & SC+DP [9] & 88.12 & 2002 TPAMI \\  & MCC [23] & 94.75 & 2004 TCSVT \\  & TAR [12] & 95.97 & 2007 PR \\  & IDSC+DP [10] & 94.13 & 2007 TPAMI \\  & MDM [24] & 93.60 & 2012 TIP \\  & MARCH [3] & 97.33 & 2015 IS \\  & MTCD [13] & 96.31 & 2016 ECAI \\  & MGLLDP [26] & 98.40 & 2020 ACCESS \\  & IMRT [25] & 99.35 & 2020 ACCESS \\  & MED [14] & 97.60 & 2019 SPICE \\  & MTD+LBP-HF [27] & 98.48 & 2021 PR \\  & MMNLBP [28] & 99.52 & 2023 DSP \\ \hline \multirow{8}{*}{DNN} & AlexNet+relu5 [58] & 98.67 & 2012 NIPS \\  & VGG16+relu5\_2 [54] & 98.67 & 2015 ICLR \\  & Dual-Path CNN [19] & 96.28 & 2017 ICIP \\  & Deep Plant [18] & 97.54 & 2017 PR \\  & HGO-CNN [20] & 96.83 & 2018 TIP \\  & MaskCOV [35] & 98.27 & 2021 PR \\  & KernelPool [36] & 99.87 & 2022 MTA \\  & MTD+relu5\_2 [38] & 99.47 & 2023 ESA \\  & Ours & **99.97**(\(\pm\)0.06) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification accuracy (%) comparison on Swedish dataset with identical settings.  
Figure 7: Example images for Swedish, Flavia, MEW2012, ICL datasets.
# Internet of Things (IoT) and the Energy Sector
## 3 Enabling Technologies  
IoT is a paradigm in which objects and elements of a system that are equipped with sensors, actuators, and processors can communicate with each other to provide meaningful services. In IoT systems, sensors are used to sense and collect data, and through gateways route the collected data to control centers or the cloud for further storage, processing, analytics, and decision-making. After the decision is made, a corresponding command is then sent back to the actuator installed on the system in response to the sensed data. As there are variety of sensor and actuator devices, communication technologies, and data computing approached, in this section, we explain the existing technologies which enable IoT. Then, we provide examples from the literature how these technologies are used in the energy sector.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 1 Introduction  
Orthogonal time frequency space (OTFS) modulation [1, 2, 3, 4] has emerged as a promising candidate for the air interface of sixth-generation (6G) wireless networks, particularly in high-mobility scenarios such as vehicular communications, aerial platforms, and satellite systems [5]. Unlike conventional modulation schemes, OTFS transforms the time-varying wireless channel into a two-dimensional (2D) quasi-static representation in the delay-Doppler (DD) domain [6], thereby offering enhanced robustness to both delay spread and Doppler effects. Compared to orthogonal frequency division multiplexing (OFDM), OTFS simplifies signal detection by converting the time-varying channel into a nearly static 2D convolutional model.  
In communication environments, multipath propagation and Doppler effects cause significant variations in the time-frequency characteristics of signals. Accurately capturingthese variations is crucial to fully harness the performance advantages of OTFS modulation. Although various detection algorithms have been proposed, many of them suffer from high computational complexity, which limits their applicability in practical real-time systems. Consequently, designing reliable yet low-complexity OTFS detection schemes remains a critical challenge for enabling high-performance 6G communications.  
Previous studies have shown that linear detectors such as minimum mean square error (MMSE) and linear MMSE exhibit limited performance in highly dynamic environments. In contrast, nonlinear methods, including maximum likelihood (ML), generalized approximate message passing (GAMP), and expectation propagation (EP), improve detection accuracy but incur significant computational costs [7; 8; 9]. More recently, deep learning techniques have been introduced to OTFS systems, leveraging their strong nonlinear modeling capabilities to enhance detection accuracy while reducing the need for explicit signal detection [10; 11].  
In this paper, we propose a low-complexity signal detection method for OTFS systems based on separable convolutional neural networks (SeCNN-OTFS). Our work provides the following key contributions. Firstly, we design a lightweight yet effective feature extraction SeparableBlock structure that integrates global residual learning in the identity path and recursive learning in the residual path, enabling efficient deep feature learning with minimal computational overhead. Secondly, compared to conventional two-dimensional convolutional neural network (2D-CNN)-based detectors, our model achieves an 81% reduction in parameters while maintaining near-optimal detection accuracy, making it highly suitable for resource-constrained systems. Thirdly, the proposed SeCNN-OTFS is particularly advantageous for high-mobility communication scenarios, such as unmanned aerial vehicle (UAV) and vehicle-to-everything (V2X) systems, where computational efficiency is critical.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Frequency Sampling Methods  
The idea of frequency sampling is conceived from the frequency domain perspective. This kind of method takes uniform spacing sampling of an ideal frequency response \(H_{d}(e^{i\omega})\), which is given by  
\[H_{d}(k)=H_{d}(e^{i\omega})|_{\omega=\frac{2\pi}{N}k}. \tag{14}\]  
Then \(H_{d}(k)\) is used as the sampled values of the actual linear phase FIR filter, written as  
\[H(k)=H_{d}(k),\quad k=0,1,\cdots,N-1. \tag{15}\]  
The \(N\)-point inverse discrete Fourier transform (IDFT) for \(H(k)\) yields the following impulse response  
\[h(n)= \text{IDFT}\Big{[}H(k)\Big{]} \tag{16}\] \[= \frac{1}{N}\sum_{k=0}^{N-1}H(k)W_{N}^{-kn},\quad n=0,1,\cdots,N-1,\]  
where \(W_{N}=e^{-j\frac{2\pi}{N}}\).  
The exponential form of \(H(k)\) is given by  
\[H(k)=A(k)e^{i\theta(k)}, \tag{17}\]  
where \(A(k)\) and \(\theta(k)\) are amplitude sampling and phase sampling, respectively. To design a linear phase FIR filter in practical applications, the condition in Equation (2) should be satisfied. Thus certain constraint conditions are imposed on \(A(k)\)  
\[\begin{cases}A(k)=A(N-k),&\quad N\text{ is odd},\\ A(k)=-A(N-k),&\quad N\text{ is even},\end{cases} \tag{18}\]and also on \(\theta(k)\)  
\[\theta(k)=-\omega\bigg{(}\frac{N-1}{2}\bigg{)}\bigg{|}_{\omega=\frac{2\pi k}{N}}=- \bigg{(}\frac{N-1}{N}\bigg{)}\pi k. \tag{19}\]  
A series of frequency sampling based filter design methods have been reported according to the above formulations. Some of commonly used methods are described in the following subsections.
###### Abstract  
In order to ensure the stable operation of blast furnace production, it is necessary to keep abreast of the trends in the gas permeability index of the blast furnace. As one of the key parameters to be monitored in the process of blast furnace smelting, the gas permeability index directly reflects the performance of the blast furnace in the actual production of the furnace. Continuous monitoring of the permeability index is required in the actual production of the blast furnace in order to effectively guarantee the stable and smooth operation of the blast furnace. The aim of this study is to accurately predict the trend in the blast furnace gas permeability index by constructing an intelligent prediction model and utilizing a data-driven approach to monitor the gas permeability index and ensure the stable operation of the blast furnace. First, based on the actual production data of a H2 blast furnace of an iron and steel enterprise, an isolated forest algorithm is applied to detect and eliminate the outliers in the original data, and then a data driver set is constructed after normalization of the deviation. Second, by analyzing the coupling mechanism between the blast furnace permeability and gas flow, as well as Spearman correlation analysis and MIC maximum information coefficient (MIC) analysis, key parameters are screened out as feature variables from the data-driven set. Finally, a wavelet neural network algorithm is used to construct an intelligent prediction model of the blast furnace gas permeability index. Compared with a BP neural network (BP), a particle swarm-optimized BP neural network (PSO-BP), and XGBost, the wavelet neural network shows obvious advantages when the error is controlled in the range of \(\pm\)0.1, and the prediction accuracy can reach 95.71%. The model is applied to the actual production of a #2 blast furnace of an iron and steel enterprise, and the results show that the predicted value of the blast furnace permeability index is highly consistent with the actual value of real-time blast furnace production, which verifies its excellent characteristics.  
blast furnace permeability index; coupling mechanism; data-driven; wavelet neural network +
Footnote †: journal: Applied Physics Letters  
1
# Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems
## 2 Preliminary
### Bidirectional LSTM  
The structure of a conventional LSTM model is illustrated in Figure 1. Here, we provide a short introduction to the LSTM model. For further details, we refer the interested reader to [18], LSTM [18] was designed with special memory cells to store temporal information. This structure allows LSTM to remember long-range features better than conventional recurrent neural networks. With a multilayer model, components of a cell at time step \(i\) at layer \(l\) in the forward direction can be implemented by the following functions:  
\[\mathbf{f}_{i}^{l}=\sigma\left(\mathbf{W}_{(f)}^{l}\overrightarrow{\mathbf{h} }_{i}^{l-1}+\mathbf{V}  
where \(\mathbf{i}_{i}^{l}\), \(\mathbf{f}_{i}^{l}\), \(\mathbf{o}_{i}^{l}\), \(\mathbf{g}_{i}^{l}\), \(\mathbf{C}_{i}^{l}\), and \(\overrightarrow{\mathbf{h}}_{i}^{l}\) are the input gate, forget gate, output gate, candidate gate, cell state, and hidden state, respectively, all of which are of the size of an \(N^{l}\)-dimensional vector. In (6)-(9), the \(\mathbf{W}^{l}\) terms are the weight matrices between cells of layer \((l-1)\)-\(l\), the \(\mathbf{V}^{l}\) terms are the weight matrices between consecutive cells of layer \(l\), and the \(\mathbf{b}^{l}\) terms are the bias vector at each layer. The weight matrices and bias values in a cell are shared along the length of the sequence, thus reducing the total number of weights and hidden neurons in the network. The sigmoid function \(\sigma\) and hyperbolic tangent function are used as activation functions, and \(\odot\) denote element-wise multiplication.  
A bidirectional LSTM can process the data in both the forward and backward directions using two separate LSTM layers. The forward hidden state, \(\overrightarrow{\mathbf{h}}_{i}^{l}\), calculated by the formulas above, and the backward state, \(\overleftarrow{\mathbf{h}}_{i}^{l}\), calculated similarly, are concatenated and then fed forward to the next layer, as demonstrated in Figure 2:  
\[\overleftarrow{\mathbf{h}}_{i}^{l}=\ \left[\begin{array}{c}\overrightarrow{ \mathbf{h}}_{i}^{l}\\ \overleftarrow{\mathbf{h}}_{i}^{l}\end{array}\right], \tag{11}\]  
where \(l=0\) is the input layer. BDLSTM is better at obtaining the relations among elements in a whole sequence by utilizing information in both directions, instead of remembering the features in only one direction such as that of the conventional LSTM. Furthermore, by using the parameter-sharing technique in a layer, the BDLSTM model requires less memory to solve the problem compared to the conventional FNN and CNN.