esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 4 Experiments and Results  
In this section, the following four different methods for evolving robust or non-robust lowpass and highpass filters are presented:  
1. standard genetic programming (GP) without considering robustness requirements;
2. genetic programming with robustness-by-multi-simulation (GPRMS);
3. genetic programming with robustness-by-perturbed-evaluation (GPRPE);
4. hybrid GP/GA robust design method (GPGARMS).  
For all these methods, a fixed number of function evaluations is allocated to ensure fairness of comparison. Here, one function evaluation means one simulation of a design for measuring its performance, which is different from one fitness evaluation for an individual. In the multi-simulation robust design method GPRMS, multiple simulations (SPI = 10) are used to evaluate the fitness of a single design. For the lowpass and highpass filter design problem, the computation budget is 1,000,000 function evaluations. Note that, for methods that use multiple simulations to estimate the robustness fitness, each simulation is counted as one function evaluation. In addition, for the hybrid GPGARMS method, we allocate 500,000 function evaluations for GP evolution and the remaining 500,000 for GA evolution for robustness.  
We used only one parameter perturbation model in the evolutionary synthesis experiments--Gaussian noise \(N(\mu,\sigma)\) with a mean \(\mu\) of 0 and the standard deviation \(\sigma\) set to 20% of the parameter value. However, the evaluation of the evolved filters are subject to multiple perturbation magnitudes with extensive simulation.  
To assess the statistical significance of the performance discrepancy between these methods, for each target filter type and each synthesis method, 15 runs were conducted. The size of these experiments was determined by the computing resources available. However, since we found that the results are quite stable across multiple runs, the level of replication appears to be sufficient for the purposes of this paper.  
All experiments described below used the same embryo bond graph shown in Figure 1. The component values of source resistor \(R_{s}\) and load resistor \(R_{load}\) are both 1 \(\Omega\) for lowpass and highpass filter synthesis.  
In the following sections, we first describe separately the experimental configuration of each method, the best evolved bond graph models of each filter type, and the frequency response of the best solutions from each method. These results provide some general ideas regarding how robustness is evolved with respect to the parameter perturbations. Then, in Section 4.4, a statistical comparison of the performances of five algorithms for evolving robust filters are presented. These analyses will be used to support the hypothesis outlined in the introduction section.
## 2 The Equivalent Circuits of a Long-distance Submarine Cable
The blue, orange, purple and green lines indicate the lumped parameter models of the submarine cable, formed by cascaded 200, 100, 40 and 20 \(\pi\)-type lumped parameter equivalent circuits, respectively. Each \(\pi\)-type lumped parameter model represents 1 km, 2 km, 5 km or 10 km submarine cable segment. Taking the red line as a reference, it can be seen that the differences between the blue, orange and red lines are small, and the errors are less than 0.5%. The difference between the purple and red lines is larger, and the error is about 1.5%. The difference between the green and red lines is even larger, and the error is about 6%. It shows that the shorter the submarine cable segment represented by each \(\pi\)-type lumped parameter equivalent circuit, the smaller the difference between the lumped and distributed parameter models, which is consistent with the results of the above theoretical analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(f=1\) kHz & \(Z\) (Two-Port Network) & \(Z\) (Lumped Model) & \(Y\) (Two-Port Network) & \(Y\) (Lumped Model) \\ \hline \(l=1\) km & 1.00 + 2.51i & 1.00 + 2.51i & \(1.32\times 10^{-7}\) + 1.26i \(\times 10^{-3}\) & 1.26i \(\times 10^{-3}\) \\ \(l=2\) km & 1.99 + 5.02i & 2.00 + 5.03i & \(1.06\times 10^{-6}\) + 2.52i \(\times 10^{-3}\) & 2.51i \(\times 10^{-3}\) \\ \(l=3\) km & 2.97 + 7.51i & 3.00 + 7.54i & \(3.57\times 10^{-6}\) + 3.78i \(\times 10^{-3}\) & 3.77i \(\times 10^{-3}\) \\ \(l=4\) km & 3.93 + 9.98i & 4.00 + 10.05i & \(8.51\times 10^{-6}\) + 5.05i \(\times 10^{-3}\) & 5.03i \(\times 10^{-3}\) \\ \(l=5\) km & 4.87 + 12.43i & 5.00 + 12.57i & \(1.67\times 10^{-5}\) + 6.33i \(\times 10^{-3}\) & 6.28i \(\times 10^{-3}\) \\ \(l=6\) km & 5.77 + 14.84i & 6.00 + 15.08i & \(2.91\times 10^{-5}\) + 7.61i \(\times 10^{-3}\) & 7.54i \(\times 10^{-3}\) \\ \(l=7\) km & 6.64 + 17.21i & 7.00 + 17.59i & \(4.66\times 10^{-5}\) + 8.91i \(\times 10^{-3}\) & 8.80i \(\times 10^{-3}\) \\ \(l=8\) km & 7.47 + 19.54i & 8.00 + 20.11i & \(7.02\times 10^{-5}\) + 1.00i \(\times 10^{-2}\) & 1.00i \(\times 10^{-2}\) \\ \(l=9\) km & 8.25 + 21.81i & 9.00 + 22.62i & \(1.01\times 10^{-4}\) + 1.10i \(\times 10^{-2}\) & 1.10i \(\times 10^{-2}\) \\ \(l=10\) km & 8.97 + 24.03i & 10.00 + 25.13i & \(1.40\times 10^{-4}\) + 1.30i \(\times 10^{-2}\) & 1.30i \(\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of the two-port network and the lumped parameter model under different cable segment lengths. Figure 3: The simulation file for comparing different submarine cable models.
# Towards Mirror-Less Graphene-Based Perfect Absorbers
###### Abstract  
Owing to its exceptional electronic and optical properties, graphene has attracted extensive attention among researchers in the development of high-performance optoelectronic devices. However, the light absorption of pure graphene is very poor, limiting its development in practical application. In this review, as a solution for this issue, various types of graphene-based perfect absorbers are addressed in terms of their operation principles and design requirements. Their recent progress and potential applications such as photodetectors and modulators are also discussed. In particular, we emphasize the importance of mirror-less (in particular, one-port mimicking) perfect absorber design due to simplified fabrication processes or enhanced tolerance for fabrication error.  
perfect absorption; graphene; tunable absorption; mirror-less +
Footnote †: journal: Applied  
0000-0002-4000-3999-2299][PERSON]  
0000-0002-3999-2299-2299-2999-2999-2999-  
consisting of 25 pairs of alternating AlAs and AlGaAs layers, can be grown via molecular beam epitaxy (MBE) on an n-doped GaAs substrate. Owing to the low difference between the refractive indexes of AlAs and AlGaAs, the use of so many deposition processes is inevitable. Compared to conventional GPAs utilizing these external mirrors [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44], mirrorless GPAs are desirable due to their simpler fabrication process, although their somewhat strict design requirements are inevitable [45; 46; 47; 48; 49]. In the case of the former, substantial progress has been made with respect to their development, but for the latter, development remains immature. We review the basic theory behind the two main types of GPAs in Sections 2 and 3, and then we discuss the performance of relevant potential applications in Section 4. Finally, the conclusions and future perspectives are provided in Section 5.
## 2 Materials and Methods
### Materials  
The mineral powders used in this study were montmorillonite, kaolinite, and quartz. These three kinds of mineral powders were bought from Guangzhou Jialiang Mineral Products Co., Ltd. (Guangzhou, China); Guangzhou Yifeng Chemical Technology Co., Ltd. (Guangzhou, China); and Inner Mongolia Ningcheng Tianyu Chemical Co., Ltd. (Chifeng, China), respectively. The grain size distributions of these three kinds of mineral powders were measured by the Mastersizer 2000 laser particle analyzer. The grain size distributions of the three mineral powders are shown in Figure 1. The maximum particle sizes of the montmorillonite, kaolinite, and quartz were 0.01, 0.04, and 0.55 mm, respectively. The clay (\(<\)0.005 mm), silt (0.005-0.075 mm), and sand (\(>\)0.075 mm) contents of the three kinds of mineral powders are shown in Table 1. Particles finer than 0.005 mm in montmorillonite, kaolinite, and quartz represented 70.23%, 84.10%, and 1.63% of the samples, respectively. Additionally, 15.9%, 29.77%, and 4.15% of the particles in the montmorillonite, kaolinite, and quartz were between 0.005 and 0.0075 mm. Only the quartz had particles bigger than 0.075 mm; 94.22% of the particles in the quartz were bigger than 0.075 mm.  
Figure 1: The grain size distributions of quartz, kaolinite, and montmorillonite. From left to right, the red curve, black curve, and blue curve indicate grain size distributions of kaolinite, montmorillonite, and curve, respectively. The particle size of quartz mineral powder is the largest among the three kinds of mineral powder.  
Table 1 presents the basic properties of the quartz, kaolinite, and montmorillonite used in this study. The Atterberg limits were measured by the method presented in ASTM D4318. The plastic limit, liquid limit, and plasticity index of the kaolinite were 32.00%, 68.99%, and 36.99%, respectively. The plastic limit, liquid limit, and plasticity index of the montmorillonite were 61.34%, 178.65%, and 117.31%, respectively. According to ASTM D2487, the kaolinite and montmorillonite used in this study can be classified as high plastic clay (CH). The chemical compositions of minerals were determined by the X-ray fluorescence method. The main chemical composition of quartz used in this study was SiO\({}_{2}\), and its proportion was 98.85%. The montmorillonite used in this study was mainly composed of SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), CaO, and MgO. The proportions of these chemical compositions were 62.76%, 17.13%, 8.46%, 5.80%, and 4.49%, respectively. The main chemical compositions of kaolinite were SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), K\({}_{2}\)O, and Fe\({}_{2}\)O\({}_{3}\), and the proportions of these chemical compositions were 57.82%, 35.24%, 3.89%, and 1.86%, respectively. According to ASTM D7263, the specific gravity values of quartz, kaolinite, and montmorillonite were determined to be 2.68, 2.76, and 2.61, respectively.  
The distilled water used in this study had a pH of 6.7 and the electrical conductivity of the distilled water was below 1 ms/m.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Feature Selection  
Feature selection focused on the data acquired by accelerometers A1, A2, and A3, while the remaining accelerometers (A4, A5, and A6) were used to validate the robustness of the selected subset. The selection process is illustrated in Figure 5.  
A wrapper-based approach was employed using the random forest (RF) classifier, as this type of method evaluates the performance of feature subsets within the learning model itself, improving selection quality. Additionally, RF allows for estimating each CI relative importance by calculating its mean influence (MI) on the model's performance.  
Figure 4: Vibration signal obtained from the accelerometers.  
Figure 3: Methodology for feature selection and classification analysis.  
The selection process consisted of the following phases:  
* Phase 1: Preparation of the RF classification model (mathematically detailed in Section 2.2.4) was carried out by optimising its hyperparameters, using the 64 computed condition indicators (CIs). These CIs were iteratively introduced into the classification model, with the training dataset (from accelerometers A1-A3) being resampled using the bootstrap method until maximum accuracy was achieved.  
The machine learning wrapper method was applied once the RF classifier's hyperparameters were optimised. This consisted of testing and evaluating different combinations of CIs to determine which yielded the best performance within the RF model, based on the average importance of each CI. The top 10 most relevant CIs were selected for each accelerometer, and a descending weight from 10 to 1 was assigned according to their ranking.
* Phase 2: The weights of each CI were summed by failure type across the three accelerometers to determine their relative influence of each kind of failure.
* Phase 3: Finally, the weights accumulated for each failure type were aggregated. The CIs with the highest total weights that appeared across all failure types were selected. This resulted in a final subset of the 7 most relevant CIs used as input for classification models. The monitoring method is ready for implementation with the selected condition indicators and the optimised classification model.
# Avoiding Obstacles via Missile Real-Time Inference by Reinforcement Learning
## 3 Architecture Design and Training  
Figure 7 shows the architecture of the artificial neural network for scenarios A and B, where the left one is the actor network and the right one is the critic network. Each network is composed of nine hidden layers and each layer contains hundreds of neurons, as shown in Figure 7. All layers use hyperbolic activation functions. The actor network has 14 and 24 states as inputs for scenarios A and B, respectively, and has 1 and 2 outputs as actions for scenarios A and B, respectively. Actions, which are the missile maneuver acceleration, are limited to the feasible range. Actions are then normalized and fed into the critic network to evaluate the policy along with the states. The critic networks is updated with the loss function of Mean Square Error (MSE), and the policy is updated via TD3 PG [23]. TD3 PG stands for Twin Delayed Deep Deterministic Policy Gradient and is one of the most advanced algorithms of RL. TD3 PG was developed to ease the limitation of Deep Deterministic Policy Gradient (DDPG) [24; 25], which sometimes overestimated the value of the state-action.  
The environments for both scenarios have the following termination conditions:  
1. Collision: is activated when the agent hits the object that should not be hit;
2. Escape: is activated when the agent is going outside of the environment;
3. Excess altitude: is activated when the agent exceeds the altitude limit set in advance and is only for scenario B;
4. Time over: is activated when the episode takes more time than it is supposed to;
5. Out of sight: is activated when the target is outside of the field of view of the seeker of the agent;
6. Hit: is activated when the agent is close enough to the target.  
In the training session, the environment of each episode is randomly generated under the given constraints. This randomness makes the guidance law robust by letting the agent experience a varying environment. Further training details for each scenario will be described below.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 3 Methodology
### Final Prediction  
Dense Prediction Layer:  
A fully connected layer maps the fused vector to the final output:  
\[\hat{y}=W_{f}\cdot\text{fusion}+b_{f} \tag{13}\]  
where \(\hat{y}\) is the predicted, normalized response time.  
TSA GRU Fusion Model Pseudocode: Algorithm 2 describes the TSA-GRU pseudocode.  
```
1:# 1. Embedding Transformation
2:\(x_{\text{embed}}\leftarrow\text{LinearEmbedding}(\text{input\_sequence})\)\(\triangleright\) (batch, seq_len, hidden_dim)
3:# 2. Apply Temporal Sparse Attention (TSA)
4:tsa_features \(\leftarrow\) TSA_Module(\(x_{\text{embed}}\),num_heads, hidden_dim)
5:# 3. GRU Sequential Encoding
6:gru_out \(\leftarrow\) GRU_Layer(tsa_features)\(\triangleright\) (batch, seq_len, hidden_dim)
7:tsa_pooled \(\leftarrow\) GlobalAveragePooling(tsa_features)\(\triangleright\) (batch, hidden_dim)
8:gru_pooled \(\leftarrow\) GlobalAveragePooling(gru_out)\(\triangleright\) (batch, hidden_dim)
9:# 5. Fusion of TSA and GRU Features
10:fusion \(\leftarrow\) Concatenate(tsa_pooled, gru_pooled)\(\triangleright\) (batch, \(2\times\) hidden_dim)
11:# 6. Final Prediction
12:prediction \(\leftarrow\) Dense(fusion, \(W_{f},b_{f}\))\(\triangleright\) (batch, \(1\))
13:return prediction
```  
**Algorithm 2** Build_TSA_GRU_Fusion_Model(input_sequence)
The challenges in an AUV-aided UWSN are illustrated below:  
* Energy saving in underwater sensors is challenging because the nodes are equipped with an amount of battery within which it can perform sensing and transmission;
* Delay in data transmission increases due to the involvement of multiple intermediate hops and waiting time. Transmitting the data through multiple sensors in a relay pattern, and also the sensor waiting time before the AUV's arrival for data collection, causes high delays;
* Dynamic topology in the network due to the movement of sensors in relation to the ocean current changes over time. In this paper, a 3D-UWSN architecture was designed with the objective of minimizing energy consumption and transmission delay. In order to achieve this objective, the processes of clustering, scheduling, AUV movement and inter-cluster routing were designed. The energy was saved by optimal CH selection and assigning sleep slots. Then, the data gathering delay was reduced by appropriate movement of AUV and inter-cluster routing. The architecture was composed of two AUVs to serve two depths of sensor node.
## 4 SLR Methodology
### Conducting the SLR
#### 4.2.3 Data Analysis  
Following the completion of feature extraction from the selected articles and the questionnaire survey by the authors of the articles, the available data were analysed from multiple viewpoints, as presented in Table 3. From the metadata, sources were assessed to obtain an idea of the venues in which the works on XAI are published. Furthermore, the author-defined keywords and the abstracts were analysed by utilising natural language processing (NLP) techniques to assess the relevance of the articles to the XAI domain. Afterwards, the selected articles were clustered based on application domains and tasks to determine future research possibilities.  
Before analysing the selected articles, clustering was performed in accordance with the primary tasks and input data mentioned in Section 2 and the method deployed to perform the primary task. Additionally, the proposed methods of explainability were clustered  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Viewpoint** & **Feature** & **Description** \\ \hline Metadata & Source & Name of the conference/journal where the article was published. \\ Keywords & Keywords & Prominent words from the abstract and keywords sections that represents the concept of the article. The targeted domain for which the study was performed. \\  & Domain & The targeted domain for which the study was performed. \\  & Application & Specific application that was developed or enhanced. \\ \hline Primary task & Data & The form of data that was used to develop a model, e.g., images, texts. \\ Model & Model & AI/ML model that was used for performing the primary task of classification/regression. \\  & Performance & The performance of the models for the defined tasks. \\ \hline Explainability & Stage & The stage of generating explanation—during the training of a model (ante hoc) or after the training ends (post hoc). \\  & Scope & Whether the explanation is on the whole model, on a specific inference instance, i.e., global, local or both. The level for which explanation is generated, i.e., feature, decision or both. \\ \hline Explanation & Method & The procedure of generating explanations. \\ Type & Type & The form of explanations generated for the models or the outcomes. \\ \hline Evaluation & Approach & The technique of evaluating the explanation and the method of generating explanation. \\ Metrics & Metrics & The criteria of measuring the quality of the explanations. \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of prominent features extracted from the selected articles.  
based on scopes and stages. Finally, the evaluation methods were investigated. All the clustering and investigations performed in this review work were intended to summarise the methods of generating explanations along with the evaluation metrics and to present guidelines for the researchers devoted to exploiting the domain of XAI.
## 3 Experimental Results and Analysis
### Ablation Experiments
#### 3.3.2 Tracking Performance  
To verify the impact of the proposed improved CenterNet detection network and the dual data association module on the tracking algorithm, we experimentally compared the proposed AMDDATrack algorithm with the current mainstream tracking algorithms; the results are shown in Table 5. It can be seen that the improved detection network performance and retrieval of low-scoring detection boxes lead to a significant improvement in tracking algorithm accuracy with FairMOT as the baseline.  
As shown in Table 5, with the conf_thres set to 0.6, the tracking accuracy of the AMDDATrack algorithm proposed in this paper is 0.6 percentage points higher on the MOT17 dataset than the FairMOT multi-pedestrian single-stage tracking algorithm; in particular, the IDs index is significantly reduced, indicating that the AMDDATrack algorithm has improved robustness when dealing with severe occlusion and can significantly reduce the number of pedestrian trajectory interruptions and ID switches.
## Appendix A Implementation Specific Details of the Main Radar Blocks
### Antenna Details  
Both transmitter and receiver antennas are identical corrugated conical horns, also recycled from the CBI radio telescope [30]. The horns' antennae have an aperture of 8.1 cm and a 15\({}^{\circ}\) semi flare angle. Because these types of antennas have very little losses, their gain can be approximated to their directivity. The directivity for 35 GHz is estimated in 22.7 dB via simulations performed with ANSYS HFSS. The far-field region distance is estimated in 1.53 m, according to the wavelength and the antenna aperture size.
###### Abstract  
It is of great significance to study short-term water-use efficiency (WUE\({}_{\mathrm{s}}\)) at different canopy heights for accurately evaluating the adaptability of cold-temperate larch (_Larix gmelini_) forest to climate change. The stable isotope method combining data of gradient meteorology, photosynthetic properties and leaf structure were used to assess the influence of different canopy heights on short-term water-use efficiency (WUEs) in larch forests in the northern Da Hinggan Mountains. The results show that: (1) The rank of leaf WUEs at different canopy heights was upper canopy > middle canopy > lower canopy. The leaf WUEs in upper canopy was significantly higher than those in the middle and lower canopy (\(p\) < 0.01), and no significant difference was found between the middle and lower canopy (\(p\) > 0.05). (2) The environmental factors, the photosynthetic characteristics, the specific leaf weight (LMA) and stomatal density (SD) had significant impact (\(p\) < 0.05) on leaf WUE\({}_{\mathrm{s}}\) at different canopy heights of larch forest. (3) The results of the weighted random forest analysis show that the main factor affecting WUEs in larch forests at different canopy heights was vapor pressure deficit (VPD), followed by relative humidity (RH) and net photosynthetic rate (Pn), while LMA and SD made relatively small contributions. This indicates that the variation of leaf WUEs at different canopy heights is mainly due to environmental factors. Our results highlight that the difference of environmental factors at different canopy heights should be considered in the future study of leaf WUE. Our results contribute to a better understanding of water utilization strategies and carbohydrate relations in the boreal forest ecosystems, which is of great significance for improving the sustainable management measures and strategies of boreal forest resources.  
cold-temperate zone; larch forest; canopy heights gradient; WUE\({}_{\mathrm{s}}\); influencing factors 20221245126633900/su140951263900/12/22