esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Transfer Learning with Classifier Tree  
This study used two main transfer learning methods [34]. One is feature-based transfer, in which the output of the coaching model is used as input to the student model, thereby improving the training effectiveness of the student model. The char-embeddings extracted by XLNet mentioned above are precisely the transfer learning method in this form [35]. Another type is parameter-based transfer [36], which means that some parameters in the coaching model are directly transferred to the student model, and these parameters can be fixed or updated later through post-propagation. This section uses the latter method to transfer the parameters across the classifier tree. Then, based on the parent classifier parameters, the training samples of the child classifier are further used for incremental training to accelerate the training convergence speed and classification accuracy of the child classifier model.  
In any classifier model in the tree, the parameters that can be used for migration are the HA and Bi-LSTM + HA (HAs). In other words, the parameters related to the extraction process of document-level embeddings are transferred. Because the parameters of FC are unsuitable for transferring due to the different target categories of each classifier, formally, set \(\Theta_{i}\in Y\) is the parameters set of the classifier \(i\) in the classifier tree \(Y=(\Theta,\prec)\). Therefore, the set of weights \(W^{1}{}_{\mid l\mid h}\in R^{(d/2)\times d}\) and \(W^{2}{}_{\mid l\mid h}\in R^{1\times(d/2)}\) and bias \(b^{1}{}_{\mid l\mid h}\in R^{(d/2)\times 1}\) and parameters of the Bi-LSTM network in Equations (11), (13)-(15) and (17), respectively, can be denoted as \(\mathfrak{E}_{i}\). For other classifier nodes \(j\) that satisfy the \(\Theta_{j}\prec\Theta_{i}\) in tree \(Y\), namely the child nodes of classifier \(i\), the parameter set \(\mathfrak{E}_{i}\) can be used as the initial value of the parameter set \(\mathfrak{E}_{j}\) for the training of the classifier \(j\).  
Theoretically, the effectiveness of this parameter-level transfer learning method is to improve the learning effect of parameters with a specific loss function and parameter update strategy. In the training phase, each classifier uses cross-entropy as a loss function [24] as follows:  
\[L=-\sum_{i=1}^{m}\Big{[}y^{(i)}log\tilde{y}^{(i)}+\Big{(}1-y^{(i)}\Big{)}log \Big{(}1-\tilde{y}^{(i)}\Big{)}\Big{]} \tag{18}\]  
Simultaneously, Adam [37] is used as the optimizer algorithm to optimize and update the parameters, which is given by  
\[\begin{split}& W+=-LearningRate\times\frac{m}{\sqrt{p}}\\ & m=b_{1}\times m+(1-b_{1})\times dx\\ & v=b_{2}\times v+(1-b_{1})\times(dx)^{2}\end{split} \tag{19}\]  
The transfer learning method proposed in this section can theoretically make the classifier model have an initial value closer to the optimal solution, thereby reducing the dependence of the child classifier on the amount of training sample data and improving the convergence efficiency of the training process.
# Dynamic Characteristic Master Curve and Parameters of Different Asphalt Mixtures
## 1 Introduction  
In the 1970s, the dynamic performance master curve was first used to study polymer damping materials and was used as the main content of the product specification of damping materials [1]. The master curve contains the basic dynamic parameters of viscoelastic materials, including dynamic modulus and phase angle, both of which express the material properties as a whole, and the phase angle can better express the relaxation characteristics of the material. Materials with the same dynamic modulus and different phase angles are different materials [2,3]. However, in the research and application of master curves, it isnot appropriate to pay more attention to dynamic modulus and ignore the study of phase angle master curves.  
At present, the dynamic performance of the asphalt mixture is also characterized by the method of the master curve [4; 5; 6]. The actual state of the asphalt pavement under dynamic load is complex, which can be regarded as the superposition of multiple sine waves. The response of asphalt pavement materials to a single sine wave is the basic dynamic property. Because asphalt pavement material is a viscoelastic system, its dynamic modulus and phase angle parameters constitute the basis for understanding pavement load response [7]. Generally speaking, the mechanical response of asphalt pavement is closely related to driving speed; that is, the stiffness modulus is dependent on the loading time, and the shorter the stress pulse time is, the greater the modulus of the asphalt mixture is, which has the characteristics of high frequency and high elasticity [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. However, the current research pays more attention to the dynamic modulus of asphalt pavement and does not perform an in-depth study on the phase angle, which is an important parameter reflecting the viscoelastic structure. Based on laboratory tests, this paper studied the dynamic response characteristics of different asphalt mixtures and discussed the influence of the asphalt binder on the characteristic parameters of the dynamic modulus master curve and phase angle master curve of the asphalt mixture. The evaluation functions of phase angle master curve and mixture relaxation characteristics are expounded.
# Recommending Learning Objects with Arguments and Explanations
## 3 Conversational Educational Recommender System (C-ERS)
### Conversational Process  
As pointed out in Section 1, an extra benefit from our argumentation-based recommendation technique is the straightforward way to generate explanations from arguments. These explanations are justification texts that can be offered to the user to persuade them to try certain LOs and to explain why the system has proposed a specific LO. With each explanation, the user can interact with the system by selecting one of the three possible pre-defined responses (one to accept the explanation and hence the recommendation, one to ask for more justifications, and another one to reject the recommendation). Thus, the current version of C-ERS implements a constrained mode of user interaction that does not provide a natural language interface.  
Table 2 shows an example of the explanations that the system is able to generate. Note that the system do not provide explanations for all arguments that the system is able to generate from rules, but only for those rules that represent 'leafs' in the rule tree. These represent the deeper clauses that more general'recommendation' rules aggregate to support recommendations. This simple design decision allows us to reduce the complexity of the dialogue and to improve the system's expressiveness.  
In addition, the natural order to perform the backward chaining of rules and facts to derive arguments that support recommendations allows us to establish a conversational process between the C-ERS and the user. By this process, the system is able to elicit the actual preferences of the user and allows him/her to correct the system's wrong assumptions. Concretely, the following _priority orderings_ have been established for rules and their associated explanations (as a result of the possible combinations among the different types of rules):  
* **P1: First, show arguments that suit the student profile and preferences (CONTENT-BASED ARGUMENTS)**: C1.1.1 \(>\) C1.1.2 \(>\) C1.2 \(>\) C2.1.1 \(>\) C2.1.2 \(>\) C3.1 \(>\) C4.1 \(>\) K1 \(>\) O1 \(>\) G1 \(>\) G2* **P2: First, show arguments that suit the profile and preferences of similar users (COLLABORATIVE ARGUMENTS)**: \(\text{O1}>\text{C1.1.1}>\text{C1.1.2}>\text{C1.2}>\text{C2.1.1}>\text{C2.1.2}> \text{C3.1}>\text{C4.1}>\text{K1}>\text{G1}>\text{G2}\)
* **P3: First, show arguments that suit the usage history of the users (KNOWLEDGE-BASED ARGUMENTS)**: \(\text{K1}>\text{C1.1.1}>\text{C1.1.2}>\text{C1.2}>\text{C2.1.1}>\text{C2.1.2}> \text{C3.1}>\text{C4.1}>\text{O1}>\text{G1}>\text{G2}\)
* **P4: First, show arguments that justify the format of the object (GENERAL ARGUMENTS)**: \(\text{G1}>\text{G2}>\text{C1.1.1}>\text{C1.1.2}>\text{C1.2}>\text{C2.1.1}>\text{C2.1.2}> \text{C3.1}>\text{C4.1}>\text{K1}>\text{O1}\)  
Figure 1 illustrates the sequence of explanations that the system shows, depending on the rules that have been triggered to generate arguments for the recommendations and the decisions made by the user in each step of the dialogue.  
Figure 1: Steps of the conversational dialogue between the C-ERS and the user.
## 4 Experiments and Discussion
### Results of Prediction Models and Discussion
The results showed that the proposed DCNN-based prediction model was able to predict the wafer failure pattern types more accurately than the other ML- and DL-based models, possessing the best average recall, F1-score, and AUC, with values of 0.9919, 0.9766, and 0.999, respectively. For each wafer failure patterns, the proposed DCNN model achieved the highest precision, F1-score, and AUC results, with values of 0.8511, 0.8081, 0.9726, respectively, for the 'Local' failure pattern types. Moreover, the proposed prediction model also achieved the highest recall and F1-score, with values of 0.9837 and 0.9641, 0.9419 and 0.9419, and 0.9901 and 0.9852, for the 'Center', 'Donut', and 'Edge-Ring' failure pattern types, respectively, as well as the highest recall and AUC results, with values of 1 and 1, for the 'None' failure pattern type. In addition, the VGG19 and ResNet50-based prediction models also achieved the best performance for the 'None' failure pattern type, with the highest values for precision, recall, F1-score, and AUC (1, 1, 1, and 1, respectively). In addition, the ResNet50 model obtained the highest precision and AUC results (0.9899 and 0.9999, respectively) for the 'Edge-Ring' failure pattern type. Additionally, the ResNet101 and EfficientNetB0 models received the best recall and AUC results (1 and 1, respectively) for the 'None' failure pattern type, which was also the same as that obtained by the proposed DCNN model. Furthermore, the GBDT model exhibited the highest precision, recall, F1-score, and AUC results (0.989, 1, 0.9945, and 0.9999, respectively) for 'Random', and the best precision and AUC results (0.9664 and 0.9988, respectively) for the 'Center' failure pattern types. The RF model achieved the best performance for the 'Edge-Local' failure pattern type, with the highest recall, F1-score, and AUC results (0.9802, 0.9754, and 0.9997, respectively), and the 'Random' and 'Scratch' failure pattern types, with the highest values of recall and AUC (1 and 0.9999, respectively), and precision and AUC (0.9639 and 0.9974, respectively).
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 4 Results
### Use Case Example  
This section presents an implementation of a RAN NDT to train an RL-based capacity sharing solution for RAN slicing based on the architecture proposed in Figure 1.  
The RL-based capacity sharing solution has the role of an _App_, as shown in Figure 1. Specifically, the considered capacity sharing solution is the Deep Q-Network Multi-Agent-Reinforcement Learning (DQN-MARL) capacity sharing approach proposed in our work in [19]. The solution allows dynamically distributing the available capacity in a RAN infrastructure composed of \(N\) gNBs among \(K\) tenants, each of them provided with a RAN slice. Each gNB \(n\) has a total capacity of \(c_{n}\) (b/s). The solution targets the efficient use of the available capacity in the gNBs and, at the same time, the satisfaction of the SLA of the tenants. The SLA established for the \(k\)-th tenant is defined in terms of: (a) the Scenario Aggregated Guaranteed Bit Rate, \(SAGBR_{k}\), which is the aggregated capacity to be provided across all gNBs to tenant \(k\) if requested, and (b) the Maximum Cell Bit Rate, \(MCBR_{k,n}\), which is the maximum bit rate that can be provided to tenant \(k\) in cell \(n\). The DQN-MARL capacity sharing solution considers that each tenant is associated to a different RL agent. Each agent tunes the resource quota (i.e., the fraction of capacity) assigned to the tenant's slice in the different gNBs in time steps of duration \(\Delta t\), in the time scales in the order of minutes.  
To learn the policy that tunes the resource quota for each tenant, at each time step, the RL agent obtains the state of the tenant in the different gNBs of the environment, which is defined as a tuple with different metrics, including the resource usage and resource quota of the tenant, the resource quota not assigned to any tenant, the resources not used in the gNB and the SLA parameters of the tenant. According to the obtained state, the RL agent decides the actions to perform in each gNB, which can be to increase the resource quota in \(\Delta\), to decrease it in \(\Delta\) or to keep it unaltered. To assess to what extent the applied resource quota in each gNB was suitable, a reward is provided to each RL agent in the following time step. The reward definition promotes the satisfaction of the SLA parameters and the minimization of overprovisioning situations. For further details on definitions of the state action and reward, the reader is referred to [19]. During the training of the solution, the RL agents interact with a RAN NDT instance, from which the state and reward signals are obtained and the actions, selected by an \(\varepsilon\)-Greedy strategy, are applied in the different gNBs. Based on this interaction, the policy that tunes the resource quotas is updated until it reaches convergence.
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 4 Discussion
This is mainly because there is an implicit correlation between the features belonging to the same object at different spatial locations, and capturing this correlation to enhance features belonging to the same object can improve the accuracy of the pest detection task. The beneficial effect of spatial location information on the vision task is also explained in the work of [PERSON] et al. [43]. The coordinate attention also uses channel attention to enhance the feature extraction process. The channel attention is often used in pest detection tasks and can produce a stable gain effect. Furthermore, this study compares the proposed YOLOLite-CSG with existing regular-size and lightweight object detection methods, and the results of the experiment are shown in Table 5. The regular-size object detection methods usually adopt a complex network structure to enhance the detection effect [44]. This greatly increases the number of parameters and computations of these methods. However, the experiment results show that our method not only surpasses the regular-size object detection methods evaluated in this study in terms of accuracy, but is also more efficient in terms of parameters and computations. This is mainly attributed to our simplification of the network structure and the lightweight sandglass blocks used. The network structure simplification is more common in crop pest detection tasks [45], but often does not yield better results in terms of accuracy. Our method obtains better results in terms of accuracy, number of parameters, and computation, which provides a greater advantage for practical applications. This advantage is even more evident in the comparison with the lightweight object detection methods. Compared with the lightweight object detection methods evaluated in this study, our method achieves up to a 10.6% improvement in detection accuracy. Moreover, our method has a lower number of parameters and a comparable number of computations. Such excellent results were not obtained in previous studies [46]. However, there are some shortcomings in this study. Due to the limitations of this device, we did not perform the actual effect evaluation in UAVs or sticky traps. In subsequent studies, we should focus more on the practical effectiveness of crop pest detection methods in agricultural environments and further optimize our methods.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 3 Energy-Aware Reed-Solomon Scheme
### Overview of Node Operation with EA-RS  
A node invokes EA-RS at the start of every data-transmission period, denoted as \(p_{\text{tx}}\), and repeats the following operations:  
1. Energy prediction (explained in Section 3.2) * Prediction of harvested energy: Predict the energy harvested in the next period \(p_{\text{tx}}\) using the given solar-energy harvesting model [18] * Prediction of consumed energy: Predict the energy consumed in the next period \(p_{\text{tx}}\) with a varying parity length:
2. Determination of the parity length (explained in Section 3.3) * Determination of the parity length for the node itself: Calculate the appropriate parity length for \(p_{\text{tx}}\), considering its energy prediction * Finalization of the parity length: Finalize the appropriate parity length for \(p_{\text{tx}}\), considering the parity length of the target node
3. Data processing (explained in Section 3.4) * Sensing the data: Gather the data that the application requires * Encoding the data: Encode the sensory data to the RS block with the determined parity length * Transferring the data: Send the encoded RS block to the RF module at the physical layer for transferring Figure 3 describes these operations in detail. Figure 3 describes the functional relationship between the operations described above, and Figure 3 shows the order in which the operations are performed in each period. By performing these operations, EA-RS can increase the data reliability of each node as much as possible within the available energy budget. Note that because the proposed method is based on predicting the energy consumptions of nodes, it can be applied more practically in time-based periodic applications, such as environmental monitoring applications, rather than event-based applications, which make it difficult to predict energy consumption.  
Figure 3: Overview of energy-aware (EA)-RS.
# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 4 Ontology Overview
### Representation of Weather Conditions of the Prescribed Burning Process  
Planning and conducting a prescribed burning process is a challenge because of uncertainties in weather, fire behavior, and other factors [41]. A representation of the appropriate weather conditions in order to execute this process in a specific location is valuable. The involved concepts for the prescribed burning process are visualized in Figure 4. The class _Location_ is used to present the location where the prescribed burning can be conducted. Suitable values for weather condition parameters (via class _WeatherConditionParameter_) including temperature (via class _Temperature_), humidity (via class _Humidity_), wind speed (via class _WindSpeed_) and precipitation (via class _Precipitation_) can enable the mission (via class _Mission_) for prescribed burning (via class _PrescribedBurning_).
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 4 The Proposed Algorithm
### Attention Mechanism  
#### 4.1.1 Traditional Attention  
Attention mechanisms have been applied in many tasks and have achieved remarkable results, such as speech recognition [14; 15; 16], natural language processing [17], and machine translation [18]. An attention mechanism [13; 14] can adaptively learn the relationship of the inputs at several time steps and predict the current time step by using a weight vector. For a sequence with state \(h_{j}\) at each step, a super vector \(c_{t}\) can be computed by,  
\[c_{t}=\sum\
olimits_{j=1}^{T}\alpha_{tj}h_{j}, \tag{1}\]  
where \(T\) is the number of steps of the input sequence and \(\alpha_{tj}\) denotes the weight vector, computed at step \(t\) for each state \(h_{j}\). The weight vectors, \(\alpha_{tj}\), are then computed by using an intermediate vector, \(e_{tj}\),  
\[e_{tj}=\varphi\big{(}s_{t-1},h_{j}\big{)}, \tag{2}\]where \(s_{t-1}\) is the output at step \(t-1\) and \(\varphi(\cdot)\) is a learned function, which performs as an important scalar determined by \(h_{j}\) and \(s_{t-1}\). Then the value of the weight vectors, \(\alpha_{tj}\), are computed by,  
\[\alpha_{tj}=\frac{\exp\!\left(e_{tj}\right)}{\sum_{k=1}^{T}\exp\!\left(e_{tk} \right)}. \tag{3}\]  
This attention mechanism is able to recursively generate weight vectors given the state of the current step. The frames that contribute the most to the final task tend to have higher weights in the entire super vector, while the frames that contribute less to the final task tend to have lower weights. According to Equations (1) to (3), an utterance is encoded to a super vector \(c\), which acts as an input to a back-end genuine/spoof classifier.
## 4 Anomaly Detection
### Threshold Calculation  
With computed the joint probability of the observations and the most probable states for each Skill \(s\), we can concatenate them to compute the threshold of specific state \(z=k\) that is \(\rho(z_{k})\). Each state consists of its maximum \(\mathcal{L}^{max}_{z_{k}}\) and minimum \(\mathcal{L}^{min}_{z_{k}}\). We cluster the log-likelihoods with the same state, which represents the clustering data by HMM belonging to the same statistical distribution.  
\[\begin{split}\mathcal{L}^{max}_{z_{k}}&=\underset{ i\in\{1,\ldots,N\}}{max}\mathcal{L}^{n}_{i}\mathbf{1}(z_{k}==k),\\ \mathcal{L}^{min}_{z_{k}}&=\underset{i\in\{1,\ldots, N\}}{min}\mathcal{L}^{n}_{i}\mathbf{1}(z_{k}==k),\\ \mathcal{L}^{range}_{z_{k}}&=\mathcal{L}^{max}_{z_{k }}-\mathcal{L}^{min}_{z_{k}},\end{split} \tag{28}\]  
where the notation \(\mathbf{1}(\eta)\) to represent an indicator random variable is \(1\) if the event \(\eta\) occurs and \(0\), otherwise. Using Equation (28), we can set the anomaly detection threshold of each active state \(z_{k}\) at any moment according to:  
\[\rho(z_{k})=\mathcal{L}^{min}_{z_{k}}-\frac{\mathcal{L}^{range}_{z_{k}}}{2}. \tag{29}\]  
In testing implementation, we pre-processed the incoming multimodal data in the same scheme as the training process and then get the most possible state \(z_{test}\) by recursively computing the filtered marginals and the corresponding log-likelihood \(\mathcal{L}_{test}\) using Equations (18) and (19) respectively.  
\[z_{test}=\underset{1,\ldots,K}{arg\ max}\ \{p(z_{t}=1|y_{0:t}),\ldots,p(z_{t}=K|y_{0:t} )\}. \tag{30}\]  
Here, this approach detects an anomaly when the log-likelihood is lower than the state-dependent threshold \(\rho(z_{test})\). This approach also extends to online detection. Given the trained sHDP-VAR-HMM and streaming sensor data, the detector can recursively compute the \(z_{t}\) and \(\mathcal{L}_{t}\) at time \(t\), and then perform the comparison; if \(\mathcal{L}_{t}<\rho(z_{test})\), then anomaly, otherwise nominal.