esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
## 1 Introduction
### Contributions  
In the field of EvoRL [19; 21; 22], the surveys [23; 24] provide related topics. Ref. [23] categorizes over 45 EvoRL algorithms, primarily developed after 2017, focusing on the integration of EAs with reinforcement learning techniques, which emphasizes the mechanisms of combination rather than the experimental results. Ref. [24] further explores this domain, examining the intersection of EAs and reinforcement learning, and highlights its applications in complex environments. Although these surveys offer valuable insights for EvoRL, they could benefit from incorporating a broader range of perspectives and analytical approaches, ensuring a more diverse understanding of EvoRL's potential and challenges in future exploration. Specifically, reinforcement learning usually faces issues such as parameter sensitivity and reward sparsity, while EvoRL provides a new way to solve these problems by combining reinforcement learning with EAs, but it also has new challenges such as a low sampling efficiency and high algorithm complexity. Therefore, the core scientific questions of this review are how EvoRL is currently addressing the reinforcement learning challenge, as well as its limitations and potential directions for improvement.  
This review makes the following four contributions:  
* Multidimensional classification. We have taken a comprehensive look at the EvoRL field, categorizing different approaches and strategies in detail. This detailed classification covers not only EvoRL's multiple techniques and strategies, such as genetic algorithms and policy gradients, but also their application to complex problem-solving.
* Challenges in EvoRL. We provide an in-depth analysis of the challenges and limitations of current research, including issues encountered by reinforcement learning and EvoRL, and provide their corresponding solutions.
* Constructive open issues. This review presents a summary of emerging topics, including the scalability to high-dimensional spaces, adaptability to dynamic environments, adversarial robustness, ethics, and fairness in EvoRL.
* Promising future directions. We identify key research gaps and propose future directions for EvoRL, highlighting the need for meta-evolutionary strategies, self-adaptation and self-improvement mechanisms, model scalability, interpretability, and explainability, as well as the incorporation of EvoRL within Large Language Models (LLMs).
# Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops
## 3 Materials and Methods
### Validation  
The validation of the classification models allows evaluating the performance of the models obtained from Support Vector Machines (SVM) and CNN. The accuracy, precision, sensitivity, and specificity metrics obtained from a confusion matrix were used. Likewise, the receiver operating characteristic (ROC) curve was analyzed together with the calculation of the area under the ROC curve (AUC).
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 6 Conclusions  
The study proposes a new integrated index that considers the efficiency between productivity and safety performance using the DEA. As interest in safety performance increases in developed countries due to the ESG, considering PSEI is timely. Through developed integrated measures based on the results of DEA, the PSEI of industries was compared with the weight of input and output variables. By extracting significant variables of input and output, the industries' strengths and weaknesses can be found.  
In addition, three key findings are proposed. First, the patterns of efficient activities are defined based on the characteristics of industrial production systems. Strategies can then be formulated to customize support activities, tailored to the differences in patterns related to labor and facility management. Second, the deficiencies in input and output activities across industries are identified to benchmark the most efficient Decision-Making Units (DMUs). The DEA results can provide valuable reference information to improve productivity and reduce incidents. Based on the benchmarking scores, policymakers can prioritize strategies for government supervision and support. Finally, the trend analysis highlights the industries that have seen a decline in the PSEI over time. Policmakers can use this information to target industries that need improvement. If trends in technology or management practices are identified as contributing to a reduction in incident rates, effective methods for managing both productivity and occupational risks can be suggested.  
In this respect, we contribute by providing evidence for formulating strategies and policies to enhance both productivity and safety performance. By identifying patterns, benchmarking units, and trends of PSEI, it is expected that the policymakers can prioritize the industries that introduce the improvement plan. Also, safety is regarded as a key societal value for corporations, aligning with Corporate Social Responsibility (CSR). In Korea, safety performance is incorporated into the assessment criteria for bidding on construction projects. Therefore, the PSEI can serve as an integrated index that captures both productivity and safety within ESG activities.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT: Ministry of Science and ICT) (NRF-2020R1C1C1007302).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the author on request.  
The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Machine Learning Applications in Seismology
###### Abstract  
The comprehension of earthquakes and natural hazards, including volcanic eruptions and landslides, as well as explosions, through observational data is a pivotal activity within the field of seismology. The rapid advancements in seismogram technology have resulted in the accumulation of extensive seismic datasets, presenting significant opportunities for the exploration of seismicity patterns, the physical processes underlying earthquakes, and the elucidation of earthquake mechanisms [1; 2; 3; 4; 5; 6; 7; 8]. The availability of such large-scale seismic data can substantially enhance data-driven research endeavors in seismology [9; 10; 11; 12; 13]. These data-rich resources can be employed for a variety of analytical and modeling initiatives, thereby assisting seismologists in gaining insights into earthquake mechanisms, forecasting seismic hazards, and formulating strategies for disaster prevention and mitigation.  
Recent progress in seismic data acquisition and processing, particularly through the application of machine learning techniques, has proven beneficial for seismologists in identifying signals or patterns that traditional methodologies may overlook [8; 14]. For instance, the automatic detection of seismic events via models such as PhaseNet [15] streamlines the processing of seismic data [16; 17; 18; 19; 20]. Additionally, the classification of seismic events utilizing convolutional neural networks (CNN) demonstrates greater efficiency compared to conventional feature-based methods [21; 22; 23]. Furthermore, machine learning approaches to earthquake prediction and early warning systems offer alternative strategies for mitigating earthquake hazards [4; 24; 25; 26]. In summary, machine learning methodologies significantly enhance the capabilities of seismologists in processing seismic data and uncovering the physical mechanisms associated with earthquakes [27].  
Over the past two years, we have compiled 15 articles for this Special Issue titled \"Machine Learning Applications in Seismology\". These contributions encompass topics such as seismic inversion, earthquake detection, ground motion simulation, focal mechanism analysis, and earthquake early warning and forecasting systems. The articles underscore the necessity of integrating machine learning into seismological research and provide illustrative examples of its application within the discipline.
## 3 Gas Desorption Laws of Coal with Different Water-Bearing States under Various Stress Paths
### Influencing Analysis of Various Stress Paths on Gas Desorption from Water-Bearing Coal Samples  
During the test, the influence of stress on the gas desorption displayed by water-bearing coal could be analyzed using the accumulated gas desorption. The isothermal adsorption characteristics of coal samples could be described using the Langmuir equation, namely:  
\[Q=\frac{abP}{1+bP} \tag{3}\]  
where \(Q\) is the gas adsorption capacity with the unit of mL/g; \(a\) is the maximum adsorption capacity with the unit of mL/g; \(b\) is the Langmuir constant with the unit of Pa\({}^{-1}\); and \(P\) is the pore pressure with the unit of Pa.  
Combined with the above formula, the accumulated gas desorption from the water-bearing coal body under various stress paths was analyzed by using a Langmuir-type relational expression of gas desorption with time [22]:  
\[Q_{t}=\frac{AB\sqrt{t}}{1+B\sqrt{t}} \tag{4}\]  
where \(Q\) is the accumulated gas desorption at time \(t\) with the unit of mL/g; \(t\) is desorption time with the unit of \(s\); \(A\) is the limiting gas desorption with the unit of mL/g; and \(B\) is the desorption rate constant.  
Under constant temperature, the accumulated gas desorption displayed by water-bearing coal under multiple stress paths was measured and fitted as per Formula (4) to acquire the limiting gas desorption under different test conditions. The water-bearing coal desorption curves and the change curves of the limiting gas desorption under multiple stress paths were drawn, as displayed in Figures 3-6.  
By analyzing Figures 3 and 4, it can be seen that (1) the coal samples differing in moisture content were subjected to different stress paths, which also led to the difference in the corresponding gas desorption process. Within 0-3 min in the initial test stage, the stress borne by the coal samples gradually increased, accompanied by the overlapping or crossing of desorption curves under each stress path, but under equivalent gas adsorption by coal samples, the total desorption displayed by coal samples grew with the stress within 60 min. (2) The coal samples with different moisture contents were under the action of multiple stress paths, and the total gas adsorption produced by each stress path was different, but  
It can be seen in Figures 5 and 6 that when the axial stress borne by the coal samples with two different moisture contents showed hierarchical growth (0, 4, 8, 12, and 16 MPa), the limiting desorption displayed by coal samples was fitted, and then the limiting desorption displayed by the coal sample with a moisture content of 1.15% was about 16.12-21.33 mL/g, with an increase of 5.21 mL/g. The limiting desorption of the coal sample with a moisture content of 3.24% was about 14.81-19.34 mL/g, increasing by 4.53 mL/g. The desorption displayed by coal samples was obviously promoted by the hierarchical growth of the axial stress borne by coal samples. However, the increase amplitude of the limiting desorption declined due to the increase in the moisture content of coal samples.
# Multi-Sensor System for Analysis of Maneuver Performance in Olympic Sailing
## 2 System Setup, Test, and Data Processing Method
### Data Analysis  
After the completion of the field test, data were selected and analyzed. Course changes as a result of a tack were identified manually, and a 10 s interval centered around the apex of the tack was defined as the representative interval capturing a full tack maneuver. For this analysis, the direction for VMG was found using the estimated onboard wind direction (EOWD). The EOWD was determined by finding the mean course of all starboard legs and the mean course of all port legs from the selected dataset, and lastly, by calculating the mean course between those two.  
Data was collected after the field test to identify the angles corresponding to the mast and rudder. The rudder rotary sensor was sampled at \(-60^{\circ},-30^{\circ},0^{\circ},30^{\circ}\), and \(60^{\circ}\). For each static angle, potentiometer values were sampled and used to derive a proportional angle mapping. This was repeated for the mast, and data was sampled at \(-720^{\circ},-360^{\circ},0^{\circ},360^{\circ}\), and \(720^{\circ}\). The reference angle point was identified from a static section at the beginning of the field test where the rudder and mast were aligned straight in relation to the hull for 30 s. Potentiometers were selected over other angle measurement sensors, such as encoders, to maintain a low-cost overall system. The entry and exit courses were calculated as the mean course during the first and last second of the tack interval, respectively. The entry and exit speed over ground (SOG) were calculated as the mean speed of the first and last two seconds of the tack interval, respectively. The time until 80% of SOG was defined as the time interval from the start of the tack until the speed reached 80% of the entry SOG, using the lowest SOG in the interval as a threshold gate.  
Weather data was extracted from the SINTEF weather station in the relevant time interval. Wind speed is defined by the average speed of all wind measurements, and gust is the average of the highest value from each minute. The wind direction is the mean of the wind course data. Three outliers in the wind course data were filtered out: \(170^{\circ}\), \(171^{\circ}\), and \(173^{\circ}\). These were determined as numerical errors from the measurement buoy, considering all other data points were centered around 339.9 \(\pm\) 4.85\({}^{\circ}\).
## 1 Introduction
The major contributions of this work are as follows: (a) based on a detailed theoretical analysis PCFL and ACFL are proposed for three-dimensional underwater environments; (b) the proposed algorithms adopt the overlapping signal region of task anchors which can reduce uncertainty with fewer anchors than used in other approaches; (c) it is more efficient by using the adapted hierarchical structure and the projection model; (d) as the proportion factor of distance is proposed to weight the RGB values, PCFL and ACFL can provide more accurate RGB values; (e) specifically, the normalized nearness together with the calculation of the coordinates significantly improves the whole computational efficiency. The rest of this paper is arranged as follows: in Section 2, we survey some of the existing range-free localization techniques in UASNs. In Section 3, the PCFL and ACFL algorithms are put forward and their performance is verified, where we deduce the task-rings sampling method, compute the nearness degree threshold for filtering samples and locate the weighted mobile nodes. Simulations of the performance of PCFL and ACFL are evaluated in Section 4, and in the end conclusions are given in Section 5. The key notations used in this paper are summarized in Table 1. \begin{table}
\begin{tabular}{l l} \hline
**Notation** & **Explanation** \\ \hline \(R\) & Communication range \\ \(UN\) & A 3D UASNs \\ \(p\) & Number of anchors \\ \(q\) & Number of mobile nodes \\ \(n_{j}\)(\(j=1,2,...p+q\)) & Anchors or mobile nodes \\ \(A\) & Set of anchors \\ \(M\) & Set of mobile nodes \\ \(\alpha_{{}_{\!\!j}}\) & Smaller angle of acoustic signal from the anchors \(n_{j}\) received by the mobile node \(n_{i}\) \\ \(d\)(\(n_{i},n_{j}\)) & Euclidean distance between the node \(n_{i}\) and the node \(n_{j}\) \\ \(A_{i}^{\prime}\) & The set of task anchors corresponding to the mobile node \(n_{i}\) \\ \(p^{\prime}\) & Number of task anchors in \(A_{i}^{\prime}\) \\ \(B_{i}^{\prime}\) & The set of task projections for \(n_{j}\in A_{i}^{\prime}\) \\ \(RGB_{j}^{\prime\prime}\) & RGB sequences for the projections in \(B_{i}^{\prime}\) at time instant \(t\) \\ \(RGB_{j}^{\prime\prime}\) & RGB sequences for the anchors in \(A_{i}^{\prime}\) at time instant \(t\) \\ \(C_{i}^{\prime}\) & Task-ring for task projection \(n_{j}^{\prime}\in B_{i}^{\prime}\) \\ \(k_{g}^{\prime}\) & Depth difference between the task anchor \(n_{j}\) and the mobile node \(n_{i}\) \\ \(n_{j}^{\prime}\) & Projection of the task anchor \(n_{j}\) \\ \(p_{i}^{\prime}\) & Distance between the mobile node \(n_{i}\) and the task anchor’s projection \(n_{j}^{\prime}\) \\ \(S_{j}^{\prime}\) & The set of the sampling area \\ \(\lambda_{g}^{\prime}\) & Proportion factor of distances weights \\ \hline \end{tabular}
\end{table} 
Table 1: List of key notations.
## 5 Results and Analysis
### Hyperparameters Analysis  
Figure 10 presents the results of three independent experiments designed to assess the impact of various hyperparameters on the model's performance, specifically focusing on the model dimension \(d_{model}\), the number of layers \(N_{ed}\) in both the encoder and decoder, and the number of heads \(N_{h}\). The experimental setup remains consistent with that described in Section 4.2, ensuring that any observed variations in performance can be attributed to changes in these hyperparameters. The analysis of the results reveals that the metrics MAE, RMSE, and ACC exhibit similar trends across different prediction horizons. MAE values range from 0.25 to 0.31, gradually increasing as the prediction horizon extends. RMSE values follow a similar pattern, ranging from 0.46 to 0.58. Conversely, ACC values range from 81% to 85%, showing a slight decrease with longer prediction horizons. These consistent trends across varying hyperparameter settings suggest that the CeT model is relatively insensitive to changes in \(d_{model}\), \(N_{ed}\), and \(N_{h}\). This insensitivity is advantageous as it indicates that the model can maintain stable performance without extensive hyperparameter tuning.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Scenario** & **Metrics** & **noSTE** & **noTE** & **noSE** & **CeT** \\ \hline \multirow{3}{*}{Scenario 1} & MAE & 0.281 & 0.262 & 0.278 & **0.247** \\  & RMSE & 0.663 & 0.597 & 0.647 & **0.565** \\  & ACC & 81.13 & 83.24 & 81.64 & **84.81** \\ \hline \multirow{3}{*}{Scenario 2} & MAE & 0.318 & 0.283 & 0.303 & **0.272** \\  & RMSE & 0.598 & 0.534 & 0.569 & **0.512** \\  & ACC & 80.49 & 82.82 & 81.56 & **84.36** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study for temporal and spatial embeddings in Scenario 1 and Scenario 2. The best model is shown in bold font.  
Figure 9: Ablation study for spatial and temporal attention in Scenario 1 and Scenario 2.
# Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military
## 3 BiLSTM-Self-Attention-CRF Model
### Embedding Layer  
The standard method of existing state-of-the-art models for English NER treats it as a word-by-word sequence labeling task [18]. English words can be quickly and clearly separated by explicit word separators, such as blank spaces. However, in the Chinese language, there is no natural separator between words, which results in word boundaries being ambiguous. The inaccuracy of Chinese word segmentation will affect the performance of subsequent entity recognition tasks. In order to avoid the potential problem of error accumulation caused by Chinese word segmentation, we adopt a character-based method to recognize Chinese MNEs, referring to the work of [PERSON] et al. [19]. The authors in References [20; 21] also prove that the character-based method performs better than the word-based method in the Chinese NER task.  
Figure 1: The architecture of BiLSTM-self_attention-CRF model. The input sentences, \((x_{1},x_{2},\ldots,x_{n})\), are converted into a sequence of character vectors, \((\mathbf{e_{1}},\mathbf{e_{2}},\ldots,\mathbf{e_{n}})\). Character embedding is regarded as inputs to BiLSTM encoder layer. BiLSTM learn to how to obtain contextual features for identifying MNES. The output of BiLSTM encoder layer will be fed into self-attention layer before decoding. Finally, we employ CRF model to yield the predicted label sequence.  
Given a military document A including a sequence of sentences, (\(s_{1}\), \(s_{2}\),..., \(s_{m}\)), we set up \(x=(x_{1},x_{2},\ldots,x_{n})\) as the input sentence, where n represents the number of characters in the input sentence. We first need to map input sentences to vector sequence so that the neural network model can process the raw data simply. We use the distributed character-based representation proposed by [PERSON] et al. (2015), which maps sparse and high-dimensional one-hot codes to dense and low-dimensional character vectors through unsupervised training. In the first step, we use the skip-gram model of Word2 vec to pre-train large-scale unlabeled Chinese corpus and get the character vector dictionary. Then, we map each Chinese character \(x_{i}\) in the input sentence to a vector \(\mathbf{e_{i}}\) by querying the dictionary. By this way, input sentences are converted to character-based vector sequences and then these vector sequences will be entered to the BiLSTM encoding layer.
# Optimal Strokes of Low Reynolds Number Linked-Sphere Swimmers
## 3 Linked-Sphere LRN Swimmers
### Non-Dimensionalization of the System  
Let \(A\), \(L\) be the characteristic sphere radius and link-arm length. The reflection method holds in the regime \(\varepsilon:=A/L\ll 1\). Shape deformations of the linked-sphere models (see Sections 3.3-3.5) come from the expanding/contracting of the link-arms (\(\dot{l}\)), or the radial expanding/contracting of the spheres (\(\dot{a}\)). For the shape deformation scales, we require that \(\dot{a}/\dot{l}\sim O(1)\), and \(|\delta a|,|\delta l|\ll A\), where \(\delta a\) and \(\delta l\) denote the shape deformations in \(a\) and \(l\), respectively.  
We non-dimensionalize the radii and rod lengths by \(A\) and \(L\):  
\[a^{*}(t)=\frac{a(t)}{A},\quad l^{*}(t)=\frac{l(t)}{L}\]  
We non-dimensionalize other length scales by \(A\) as well, and time by \(T\)-the swimming stroke period, thus we obtain:  
\[\mathbf{x}^{*}=\frac{\mathbf{x}}{A},\,\
abla^{*}=A\
abla,\,t^{*} =\frac{t}{T},\,\mathbf{u}^{*}=\frac{T}{A}\mathbf{u},\,\mathbf{U}^{*}=\frac{T} {A}\mathbf{U}\] \[\dot{a}^{*}=\frac{da^{*}}{dt^{*}}=\frac{T}{A}\dot{a},\qquad l^{*} =\frac{dl^{*}}{dt^{*}}=\frac{T}{A}\dot{l}\]  
We assume that the deformations \(\delta a,\delta l\) in \(a\) and \(l\) are of the same order, both of which are scaled by \(A\), thus  
\[\dot{a}^{*}=\lim_{\delta t\to 0}\frac{\delta a}{\delta t}=\lim_{\delta t \to 0}\frac{T}{A}\frac{\delta a^{*}}{\delta t^{*}}=\frac{T}{A}\dot{a},\qquad l^{*} =\lim_{\delta t\to 0}\frac{\delta l}{\delta t}=\lim_{\delta t \to 0}\frac{T}{A}\frac{\delta l^{*}}{\delta t^{*}}=\frac{T}{A}\dot{l}\]  
and we have the approximation:  
\[l^{*}(t^{*}+\delta t^{*})=\frac{l(t+\delta t)}{L}\sim\frac{l(t)+\dot{l}(t) \delta t}{L}=l^{*}(t^{*})+\dot{e}l^{*}(t^{*})\delta t^{*}\]The drag force \(\mathbf{f}\) exerted on a sphere of radius \(a\) in a quiescent fluid is related to the sphere velocity \(\mathbf{U}\) via Equation (4), which leads to the following scaling for forces:  
\[\mathbf{f}^{*}=\frac{T}{6\pi\mu A^{2}}\mathbf{f}=a^{*}\mathbf{U}^{*}\]  
Therefore Equations (6)-(8) become:  
\[\mathbf{U}_{i}^{*} \sim \frac{\mathbf{f}_{i}^{*}}{a_{i}^{*}}+\delta\mathbf{U}_{i}^{*} \{j\}+\delta\mathbf{U}_{i}^{*}\{j\}+O(\epsilon^{4})\] \[\delta\mathbf{U}_{i}^{t*}\{j\} = \frac{3\epsilon}{4l^{*}}\Big{[}\Big{(}1+\epsilon^{2}\frac{a_{i}^ {*2}+a_{j}^{*2}}{3l^{*2}}\Big{)}\mathbf{I}+\Big{(}1-\epsilon^{2}\frac{a_{i}^{ *2}+a_{j}^{*2}}{l^{*2}}\Big{)}\mathbf{i}^{*}\mathbf{f}_{j}^{*}\] \[\delta\mathbf{U}_{i}^{*}\{j\} = \epsilon^{2}a_{j}^{*}\Big{(}\frac{a_{j}^{*}}{l^{*}}\Big{)}^{2} \mathbf{i}^{*}\]  
Finally, we non-dimensionalize the power \(P\) as  
\[P^{*}=\frac{1}{6\pi\mu}\frac{T^{2}}{A^{3}}P\]  
so that while \(\mathrm{Power}=\mathrm{Force}\cdot\mathrm{Velocity}\) in dimensional form, after non-dimensionalization we still have \(P^{*}=\mathbf{f}^{*}\cdot\mathbf{U}^{*}\). Thus the non-dimensional version of Equation (9) is:  
\[P^{*}=\mathbf{f}^{*}\cdot\mathbf{U}^{*}+\frac{8}{3}a^{*}a^{*2}\]