esa-sceva/satcom-chunk-collection huggingface dataset:
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 2 Technologies for LV Smart Metering--Background Information
### Wired Technologies
#### 2.1.2 Broadband PLC (BB-PLC)  
The concept of BB-PLC was initiated with the scope of providing Internet access applications. In general, the idea was to use BB-PLC for the so-called last-mile communications and for home networking. BB-PLC devices offer bit rate in the order of hundreds of Mbit/s and they do not need new infrastructure. Their frequency band of operation is between 2 and 30 MHz [16]. The Open PLC European Research Alliance (OPERA) project aimed at offering broadband access service by exploiting the BB-PLC technology. Among the project objectives were: to improve both the LV and the MV PLC system in terms of bandwidth, distances reached and network management [17]. Three types of devices are defined, namely the Head-End (HE), the Repeaters and the Customer Premises Equipment (CPE). These devices form a multi-hop system [18]. The project outcomes have been used, among others, for research in the MV BB-PLC field; an example is the work in [19] where a channel estimation algorithm for MV-PLC systems is produced.  
Although the broadband scenario has been widely used for in-home applications and Internet access, it has not experienced major deployment when it comes to smart metering applications [20]. However, there are some examples where the BB-PLC is used for smart metering and in general for smart grid applications. For instance, in [21] BB-PLC has been chosen for realising the communication link between the home controller and the utility control center. In [22] its usage at the MV substation level has been examined and in general its implementation in the MV distribution network field.  
As a first step, industrial specifications were made available for BB-PLC. The most popular ones are the HomePlug 1.0 allowing data rates of 14 Mbps and the HomePlug Turbo allowing for data rates of 85 Mbps. Higher data rates at the scale of 200 Mbps were defined by the HomePlug AV, the UPA and the HD-PLC specifications [8]. The equivalent industry alliances are the HomePlug Powerline Alliance, the Universal Powerline Association (UPA) and the High Definition Power Line Communication (HD-PLC). Afterwards, some standards followed, namely:  
* TIA-1113--It is based on HomePlug 1.0 specification and defines a data rate of 14 Mbps. The Physical layer is based on OFDM while the MAC is based on the CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) scheme [23].
* IEEE 1901--It defines a speed at the rate of 100 Mbps and is based on the HomePlug and the HD-PLC alliance specifications. The Physical and MAC layer are defined by two technologies, the FFT-based OFDM and the wavelet-based OFDM. By this way, compatibility is assured for devices using the two different aforementioned specifications (HomePlug and HD-PLC) [8].
* ITU-T G.hn--This standard gives a high data transfer rate reaching up to 1 Gbps and it refers to Home Networking [24].
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 3 Proposed Method for Signal Detection
### Proposed Novel PSO
#### 3.2.1 Basic Particle Swarm Optimization Algorithm  
The following steps are involved in the basic PSO:* Initialization: For each particle, the initial velocity, initial position, and the best position within the range of dimension are assigned. Here, the best position is normally the starting position.
* Each particle's fitness is compared.
* Check the fitness of each particle against its best previous location. If the current fitness is higher, the particle's best location should be updated.
* The particles' best fitness is compared. The particle with the highest fitness level becomes the swarm's new optimal particle.
* The velocity and position of each particle then are updated.
* The level of fitness for each particle is evaluated. Upon meeting the condition, the outcome of the most optimal particle is returned, and the process of evolution concludes.  
The velocity and position update equations in PSO are given by  
\[v(t+1)=wv(t)+c_{1}r_{1}(p_{best}-x(t))+c_{2}r_{2}(g_{best}-x(t)) \tag{19}\]  
\[x(t+1)=x(t)+v(t+1) \tag{20}\]  
where \(w\) denotes the inertial weight, which is \(>0\). The variables \(c_{1}\) and \(c_{2}\) are the acceleration coefficients while \(r_{1}\) and \(r_{2}\) are random numbers \(\in(0,1)\). \(p_{best}\) refers to the best optimal particle in history, whereas \(g_{best}\) represents the most optimal particle in a particle swarm. The variable \(x(t)\) represents the location of the particle. The complete basic PSO algorithm is given in Algorithm 1.  
```
1:Initialize parameters: \(N\) (swarm size), \(D\) (dimension), \(t_{max}\) (max iterations)
2:Initialize \(w\), \(c_{1}\), \(c_{2}\)
3:for each particle \(i=1\) to \(N\)do
4: Initialize position \(\mathbf{x}_{i}\) randomly within the search space
5: Initialize velocity \(\mathbf{v}_{i}\) randomly
6: Set \(\mathbf{p}_{best,i}=\mathbf{x}_{i}\)
7: Evaluate fitness \(f(\mathbf{x}_{i})\)
8:endfor
9:Set \(\mathbf{g}_{best}=\arg\min_{i}f(\mathbf{p}_{best,i})\)
10:for\(t=1\) to \(t_{max}\)do
11:for each particle \(i=1\) to \(N\)do
12:for each dimension \(d=1\) to \(D\)do
13: Update velocity and position: \(v_{id}(t+1)=wv_{id}(t)+c_{1}r_{1}(p_{best,id}-x_{id}(t))+c_{2}r_{2}(g_{best,d }-x_{id}(t))\)
14:\(x_{id}(t+1)=x_{id}(t)+v_{id}(t+1)\)
15:endfor
16: Evaluate fitness \(f(\mathbf{x}_{i})\)
17:if\(f(\mathbf{x}_{i})<f(\mathbf{p}_{best,i})\)then
18:\(\mathbf{p}_{best,i}=\mathbf{x}_{i}\)
19:if\(f(\mathbf{p}_{best,i})<f(\mathbf{g}_{best})\)then
20:\(\mathbf{g}_{best}=\mathbf{p}_{best,i}\)
21:endif
22:endif
23:endif
24:endfor
25:if stopping criterion is met then
26:break
27:endif
28:endfor
29:return\(\mathbf{g}_{best}\) as the optimal solution
```  
**Algorithm 1** Basic Particle Swarm Optimization.
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 4 Simulation Analysis
### Performance Analysis of Pest and Disease Spread Evolution Process Based on the GSPN Model
#### 4.1.2 Place Idle Probability  
The place idle probability represents the likelihood that a specific place is in a non-busy state at a given moment. In other words, it indicates the probability that there are no tokens in that place under steady-state conditions. In accordance with Equation (18), the idle probabilities for each place were calculated, as shown in Table 9.  
In the GSPN model, the places P\({}_{1}\) to P\({}_{6}\) corresponding to the data observation stage exhibit relatively high idle probabilities, indicating that information accumulation is less likely to occur during the early phase of the plant pest and disease early warning system. Therefore, these stages can be considered key targets for optimization. By enhancing the efficiency of information collection and processing at these points, the overall early warning capability of the system can be improved, thereby playing a crucial role in the effective prevention of pest and disease outbreaks.
# Brain-Inspired Data Transmission in Dense Wireless Network
## 3 Ultra-Dense Wireless Networks and Their Biological Counterparts
### Node-Types and Investigation Scenarios  
Following the above discussion, three types of nodes are considered in the further evaluation, mainly rudimental wireless relaying nodes possessing the neuron-functionality. These neuron-nodes are capable of message passing to the neighboring nodes only if the received signal strength is above the specified threshold (one may think of node activation as an analogy to neuron-cell firing). Next, there are microglia-nodes that do not take any action in message transmission until at least one of the neighboring neurons stops working properly. In such a situation, the microglia-node may detect such a situation, and then turn on its transmission functionality and act as a neuron-node. Finally, an astrocyte-node has the specific functionality to collect and process data about some problems in its vicinity. The astrocyte-nodes leads to, e.g., bypassing the less-reliable area by finding other paths in the network or by leaping it over by increasing the transmit power.
## 3 Results and Discussion
### Implementation Details  
The machine learning techniques that were suggested were implemented using Python 3. On a PC with an Intel Core i5-8265U 3.9 GHz CPU and 16 GB RAM, algorithms were executed (Figure 8). The DNN algorithm's hidden layer makes use of 1024 units. The Adam [53] optimizer was used with 1000 epoch. Because of the objective of regression, the output layer lacks an activation function while the input layer has RELU. Moreover, the batch size is 100 and the learning rate is 0.001. Before utilizing it in any other machine learning techniques, the data were scaled using a typical min-max scaler. Also, parameters were optimized by using GridSearchCV 1.7.0 [54]. The ANN algorithm has a hidden layer with 1024 units. The epoch is 200, the activation function is linear, the batch size is 100, and the learning rate is 0.05. The ABR algorithm parameters: n_estimators is 50, learning_rate is 1, loss is linear, and random_state is 1. The DTR algorithm parameters: criterion is friedman_mse, splitter is best, max_depth size is none, and random_state is 0. The SVR algorithm parameters: kernel is linear, degree is 2, epsilon is 0.1, cache_size is 100, and max_iter is \(-1\). The GBR algorithm parameters: loss is squared_error, learning_rate is 0.1, n_estimators is 50, criterion is friedman_mse, max_depth is 3, and random_state is none. The RFR algorithm parameters: n_estimators is 50, criterion is squared_error, max_depth is 3, and random_state is 1. The XGBR algorithm parameters: n_estimators is 1000, max_depth is 5, eta is 0.1, and colsample_bytree is 0.8.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Variable** & **Properties** & **Min** & **Max** & **Mean** & **Std** \\ \hline \multirow{3}{*}{Input} & I1 & 0 & 499.3 & 315.8848387 & 145.8439869 \\  & I2 & 1.34 & 6.27 & 3.198125 & 1.364334737 \\  & I3 & 179.4 & 363 & 286.3933333 & 53.08540895 \\ \hline Output & O1 & 0 & 7 & 3.5 & 2.327950781 \\ \hline \end{tabular}
\end{table}
Table 7: Experimental measurement values of different steel materials.  
Figure 8: A flow chart of the prediction process with ML algorithms.
# High-Quality Classroom Dialogue Automatic Analysis System
## 4 System Design and Key Modules
### Speaker-Turn Aware Dialogue Classification
#### 4.2.2 Utterance-Level Embedding  
Context plays an important role in modeling dialogue, which should be taken into account when performing dialogue classification. We introduce three conversation-invariant speaker embeddings (Teacher, Student, and Group activities). These embeddings have the same size as the utterance embeddings, which are obtained by looking up from a randomly initialized trainable matrix \(W_{s}\in\mathbb{R}^{3\times D}\) with speaker labels as input. Subsequently, our model will concatenate the [_CLS_] token and the word-level embedding separately with  
Figure 5: Architecture of the speaker-turn aware dialogue classification model.  
the speaker embedding \(s_{i}\), serving as the initial embedding representations for hierarchical classification:  
\[es_{i}=e_{i}\oplus s_{i},\ \ \ \ \ cs_{i}=[CLS]\oplus s_{i}. \tag{8}\]  
We further use Bi-LSTM to aggregate utterance representations for each level. Then, we obtain \(U=[\overrightarrow{e_{i}},\overleftarrow{\xi_{i}}]\) and \(U^{\prime}=[\overleftarrow{\xi_{i}},\overleftarrow{\xi_{i}}]\), which summarize the context information around sentence \(i\). \(es_{i},cs_{i}\in\mathbb{R}^{|W|\times D}\), and \(|W|\) denotes context window size, indicating how much contextual information should be aggregated. Then, a softmax classifier is added to predict the probability of each level label:  
\[y_{ij}=softmax(W\text{{U}}_{L_{1}}+b_{L_{1}}), \tag{9}\] \[y_{ik}=softmax(W\text{{U}}_{L_{2}}+b_{L_{2}}). \tag{10}\]  
We set loss functions for the first and second level labels, respectively, and then add them weighted together as the final loss. The cross-entropy loss between the real label \(r\) and the predicted probability \(y\) is defined as:  
\[\mathcal{L}=-\frac{1}{N}\sum_{i=1}^{N}(\lambda\cdot\sum_{j=1}^{C_{L1}}r_{ij} \log(y_{ij})+\gamma\cdot\sum_{k=1}^{C_{L2}}r_{ik}\log(y_{ik})), \tag{11}\]  
where \(N\) represents the total number of utterances, and \(C_{L1}\) and \(C_{L2}\) represent the number of labels at different levels, respectively. \(r_{ij}\) denotes the ground truth label distribution for the \(i\) sample in level one, and \(y_{ij}\) signifies the predicted label distribution. Similarly, \(r_{ik}\) and \(y_{ik}\) represent the same information at level two. \(\lambda\) and \(\gamma\) are hyperparameters that indicate hierarchy weighting predefined before training.
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 4 Simulation  
In this section, we carry out two simulation experiments to verify the effectiveness of the proposed method. In Experiment 1, we investigate the suppression effect of bistatic sea clutter with varying degrees of spectrum peak splitting and broadening at different signal-to-clutter ratios. In Experiment 2, we simulate bistatic sea surface moving target detection and compare the results before and after sea clutter suppression.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 2 Materials and Methods
### The Measuring Method of Modular WIM System Based on Transformer-GRU
#### 2.3.2 Weight Measuring  
The part of axle loads and gross vehicle weight are accurately located in Figure 4b. For example, for a four-axle truck, the input sequence and the ideal output sequence of the axle recognition model are shown in Figure 4a. In Figure 4b, the axle recognition signal which comes from Figure 4a, and the weighing signal with multiple weighing platforms are drawn. Although the three signals come from different sensors, they maintain the same time scale, allowing the recognition results of the entering and exiting signal to be related to the weighing signal, and reflecting the position change in the vehicle on the platforms under different sampling points.  
From Figure 4b, it can be seen that axle 1 starts weighing at time point 1 and stabilizes at time point 2; axle 2 starts weighing at time point 3 and stabilizes at time point 4. Therefore, the weighing signal between point 2 and point 3 is the load of axle 1, and that between point 4 and point 5 is the loads of the sum of axles 1 and 2. At time point 6, axle 6 is completely on the scale, and axle 1 stops weighing at time point 7, therefore, the weighing signal between point 6 and point 7 is the gross vehicle weight. Similarly, the weighing signal of other individual axles or axle clusters can be determined.  
When calculating the mean weight between point 6 and point 7, the signal that contains noise is first denoised based on Variational Mode Decomposition (VMD). VMD is used to decompose the signal into multiple Intrinsic Mode Functions (IMFs), and then find a set of K IMFs to minimize the residual between the original signal and their linear combination [20]. To achieve the parameter self-optimization of VMD, the central frequency method [21] is used to determine the mode decomposition number K, and sample entropy (SE) is introduced to identify effective modal components and noise modal components. After extensive testing, the SE threshold is set at 0.5, and the low-frequency IMF component with sample entropy values below this threshold is retained. A smooth denoising signal is obtained by reconstructing the low-frequency IMFs. The first and last maximum points in the signal are identified as the starting and ending points, respectively, for calculating the average value as the weight.
# Green Analytical Chemistry--Recent Innovations
## 3 Innovations in Green Analytical Techniques
### Green Separation Techniques
#### 3.1.2 Supercritical Fluid Chromatography (SFC)
These advancements, combined with the introduction of sub-2 \(\upmu\)m particles, have improved resolution, efficiency, and peak symmetry, further establishing silica-based columns as indispensable in modern SFC systems [43]. Furthermore, the coupling of SFC with mass spectrometry (MS) has opened new avenues for high-throughput analysis, enabling real-time monitoring and reducing the need for extensive sample preparation [44]. Innovations in instrumentation, including improved pressure control systems and automated solvent recycling units, have further reduced energy consumption and optimized the green credentials of SFC systems. Moreover, the development of advanced interfaces for SFC-MS, such as the \"Pre-BPR splitter with sheath pump\" and the \"BPR and sheath pump with no splitter\", has enhanced compatibility and ionization efficiency. These interfaces prevent solute precipitation and improve desolvation, ensuring stable baselines and higher sensitivity [45]. Additionally, the integration of ultra-high-performance supercritical fluid chromatography (UHPSFC) technology and sub-2 um particle size columns has significantly improved resolution and reduced analysis times [46]. These advancements have expanded the scope of SFC-MS for the analysis of complex matrices, ranging from food contaminants and nutrients to pharmaceuticals, while maintaining its environmental sustainability  
Additionally, the use of supercritical CO2 contributes to carbon neutrality, as it can be sourced from industrial waste streams, promoting a circular economy. Significant opportunities exist for increased utilization of CO2 generated from industrial processes. Large-volume sources, such as fossil-fuel power plants, cement production, and ammonia manufacturing, provide a steady supply of CO2 that can be captured and repurposed for various applications, including its use as a solvent in supercritical fluid processes [47]. As SFC continues to evolve, it not only provides a sustainable alternative to conventional chromatographic techniques but also demonstrates that high analytical performance and environmental consciousness can coexist. These advancements position SFC as a vital tool in the pursuit of greener analytical methodologies across diverse scientific and industrial domains.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
## 4 Experimental Research
### Time-Frequency Analysis with WPD
#### 4.3.2 Selection of Optimal Wavelet Packets  
In this section, the four-level WPD is implemented for the MI-EEG sample on the time interval 3.5-6.5 s, and a series of WPD coefficients is obtained. Then, the mean variance of the wavelet packet coefficients is calculated for each subspace \(s(4\),\(n)\) (\(n=0\), \(1\),..., \(15\)) according to Equation (18). The results are shown in Figure 5.  
It can be seen from Figure 5 that the difference of the wavelet packet variance is relatively obvious for the subspaces of \(s(4\),\(1)\), \(s(4\),\(2)\), \(s(4\),\(3)\), \(s(4\),\(5)\), \(s(4\),\(6)\), \(s(4\),\(7)\) and \(s(4\),\(11)\), which are just the subject-specific optimal wavelet packets, and their corresponding sub-bands are 4-8 Hz, 8-12 Hz, 12-16 Hz, 20-24 Hz, 24-28 Hz, 28-32 Hz and 44-48 Hz, respectively.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Posture-Based Routing
#### 3.1.2 Energy-Efficient and Distributed Network Management Cost Minimization (NCMD)  
An efficient and distributed opportunity routing NCMD with minimal network management costs was proposed by [PERSON] et al. [19]. Experiments show that when WBAN is in a dynamic scene, the network topology is always in an intermittent and continuous state, which causes large transmission delay and higher network maintenance costs. Additionally, with the increase of mobile speed, various Qos indicators become worse and worse. To solve the above problems, this paper proposes an opportunistic transmission link establishment algorithm, to minimize the cost of network management and optimize Qos. The overall framework of the protocol is shown in Figure 5.  
Figure 4: Routing Node Arrangement Diagram of MHRP Protocol.  
Through a lot of theoretical deduction and analyses, this paper establishes an optimal opportunity dynamic connection cost function, by using the price-based method, as follows:  
\[c^{t}_{OC_{tot}}=\sum_{i=1}^{N}\sum_{j=1}^{M}\Gamma_{ij}\Bigg{[}1+\frac{\beta^{t }_{ij}}{\beta_{\max}}(c^{t}_{opp}-c^{t}_{opp_{th}})\Bigg{]} \tag{2}\]  
where \(\Gamma_{ij}\) represents the scale factor, \(\frac{\beta^{t}_{ij}}{\beta_{\max}}\) represents the instantaneous link quality factor of the nodes and the sink node, and \(c^{t}_{opp_{th}}\) is the threshold of opportunistic dynamic connection. Then the total network management cost-function is deduced to minimize the evaluation, as follows:  
\[c^{t}_{tot}=c^{t}_{OC_{tot}}+c^{t}_{DC_{tot}}+c^{t}_{inff_{tot}}+c^{t}_{qqs_{ tot}}+c^{t}_{E_{tot}}+c^{t}_{DT_{tot}} \tag{3}\]  
\[Minimize\ u=\sum_{t=1}^{T}c^{t}_{tot} \tag{4}\]  
\[Subject\ to\ \sum_{t=1}^{T}c^{t}_{OC_{tot}}\geq c^{th}_{OC},c^{t}_{qqs_{tot}} \geq c^{th}_{qqs_{tot}}\,c^{t}_{inff_{tot}}\geq c^{th}_{inff} \tag{5}\]  
where \(c^{t}_{OC_{tot}}\) is the cost of connection, \(c^{t}_{DC_{tot}}\) is the cost of data transmission, \(c^{t}_{inff_{tot}}\) is the cost of interference management, \(c^{t}_{qqs_{tot}}\) is the cost of Qos management, \(c^{t}_{E_{tot}}\) is the cost of energy management, and \(c^{t}_{DT_{tot}}\) is the cost of dynamic topology management. \(c^{t}_{DT_{tot}},c^{t}_{DT_{tot}}\), and \(c^{th}_{inff}\) are the corresponding thresholds respectively. Lastly, the Lagrange operator is used to optimize the cost function of the network management. Based on the above theoretical analysis and simulation, the data show that the protocol is more adaptable in the dynamic WBAN environment, can quickly establish the connection path in the case of a link failure, reduce the cost of the network management, and improve the stability of the network.
###### Abstract  
Extrusion-based three-dimensional (3D) food printing (3 DFP) enhances the customization of 3D-printed foods by using multiple food pastes. Post-printing processes like baking are usually necessary and significantly impact the stability of the 3D-printed foods. This study aimed to produce multi-material 3D-printed foods using nixtamalized corn dough and chickpea paste (CP) in extrusion-based 3 DFP and to study the effect of post-printing processes (water oven cooking and steam cooking) and the type of material used (single- or multi-material) on the final appearance, weight, size, and texture of the 3D-printed foods. Multi-material 3D-printed foods were successfully produced using extrusion-based 3 DFP. Steam-cooked 3D-printed foods cooked uniformly and had a better appearance, as they did not develop surface cracks compared to water oven-cooked foods. Water-oven cooked foods experienced a greater weight loss of 35.6%, and higher height and length reduction of 1.5% and 8.4%, respectively. Steam-cooked multi-material 3D-printed foods were harder at 40% of strain, with force values of 66.9 and 46.3 N for water-oven cooked foods. Post-printing processes, as well as the presence of CP in the middle of the 3D-printed foods, influenced their final appearance, weight, size, and texture. This study offers interesting findings for the innovative design of chickpea- and corn-based multi-material 3D-printed foods.  
3D food printing; post-printing processes; corn-based dough; chickpea paste; texture Article