esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Encoder and Decoder  
The main components of the proposed network were the \(n\) encoders and decoders. The encoders were responsible for learning the interactions between the different questions and converting input question embeddings into intermediate representations, which can be seen as the question's knowledge requirement. Meanwhile, the decoder learns the relationship between the intermediate question representations and response embeddings, as well as converting the response embeddings into high-level representations, which can be seen as a learner's knowledge state.  
Figure 3: The combination of multiband attention matrices with different \(m\)s. Blue areas represent the effective attention weights that can be obtained. The greater the depth of blue, the less the degree of forgetting. Different bands captured the attention information within different time step ranges.  
As shown in Figure 1, both the encoder and decoder adopted a similar residual connection network structure. For the encoder, the input question embedding serves as the query, key, and value. It goes through a multiband attention layer, a layer-normalization layer, a feed-forward layer, and another layer-normalization layer to obtain the intermediate representations of the questions. For the decoder, it takes the response embedding as the query and key, and the output of the encoder is taken as the value. It goes through a masked multiband attention layer, a layer-normalization layer, a feed-forward layer, and another layer-normalization layer to obtain the high-level representations of the answers. Both the encoder and decoder are stacked with \(n\) layers, where the output of the previous layer serves as the input to the next layer.  
The output of the last decoder obtained above is passed through the fully connected layer with Sigmoid activation to predict the response of the learner.  
\[s_{t}=\text{Sigmoid}(w_{f}^{T}d_{t}+b_{f}), \tag{13}\]  
where \(s_{t}\) is a scalar representing the probability of the learner providing the correct response to question t, and \(w_{f}\) and \(b_{f}\) are the parameters of the fully connected layer.  
The main objective of the training was to minimize the negative log-likelihood of the observed sequence of learner responses under the model. The parameters were learned by minimizing the binary cross entropy (BCE) loss between \(s_{t}\) and \(r_{t}\).  
\[L_{BCE}=-\sum_{t}r_{t}log(s_{t})+(1-r_{t})log(1-s_{t}). \tag{14}\]  
As for regularization, we hope that the embedding features of all the questions related to the same knowledge concept were close to the embedding of the knowledge concept itself. In other words, the sum of the norms of all the features in the codebook, i.e.,  
\[L_{Reg}=\sum_{i\in B}\|x_{i}\|^{2}, \tag{15}\]  
where \(x_{i}\)s are all the embedding features in codebook \(B\), which should be as small as possible.  
The final loss function was a weighted sum of \(L_{BCE}\) and \(L_{Reg}\).  
\[L=\lambda L_{BCE}+(1-\lambda)L_{Reg}, \tag{16}\]  
where \(\lambda\) is the hyperparameter to govern the influence of question embedding variance.
## 4 Impact of Yaw Operation on Energy Capture Performance  
### Effects under Different Wind Shear Conditions  
Due to the influence of surface roughness, topography, temperature, and other unexpected factors, wind speed is different at different heights above the ground surface, which can cause wind shear. The wind speed profile can be expressed as  
\[v(z)=v_{hub}\left(\frac{z}{z_{hub}}\right)^{\alpha} \tag{5}\]  
where \(v(z)\) represents the wind speed at the height of interest \(z\); \(z_{hub}\) is the hub height of the wind turbine; \(\alpha\) is wind shear coefficient; \(v_{hub}\) is the wind speed measured at hub height.  
When performing CFD calculations in ANSYS Fluent, a wind speed profile will be applied to describe the wind speeds on the inlet plane of the flow field via a specifically defined user-defined function (UDF) program. Then, when \(z_{\text{hub}}=80\) m, the calculation results of the energy captured by the turbine rotor at different wind speeds, rotor speeds, and the azimuth angles of the blade are shown in Figure 6, where Figure 5(a)-d shows the results obtained when wind speed is 11.5 m/s, the rotor speed is 17 r/min, and the azimuth angle is 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), respectively. Figure 5(e) shows the results obtained when wind speed is 9 m/s, the rotor speed is 17 r/min, and the azimuth angle is 0\({}^{\circ}\). Figure 5(f) shows the results when wind speed is 11.5 m/s, the rotor speed is 15 r/min, and the azimuth angle is 0\({}^{\circ}\).  
From Figure 5(a)-d, it is found that the energy captured by the wind turbine rotor decreases with the increase of wind shear coefficient, which indicates the negative influence of wind shear on the power production of the wind turbine. To quantitatively describe such negative influence of wind shear, the power fluctuation factor \(\Lambda\) is defined, i.e.,  
\[\Lambda=\frac{P_{amax}-P_{amin}}{P_{amax}}\times 100\% \tag{6}\]  
Figure 5: Quadratic regression effect fitting.  
Further observation of Figure 6a-d discloses that when the azimuth angle of the blade is different, the obtained power-yaw angle curves are more or less different from each other. This suggests that wind shear can cause fluctuation to the power produced by the wind turbine, which will challenge the power quality of the wind farm if no measures are taken.
## 1 Introduction
The main contributions of LBMR are multipath routing with load balancing, robustness, and reliability. First, load balancing is done by selecting the best upper-layer node with the least traffic load. Second, robustness is achieved because the synergy of LRC and LEA provides multiple upper-layer next-hops to the gateway for each lower-layer nodes, and RM can detect link failures for quick link switching. Since LBMR eliminates bottlenecks by load balancing design and provides multipath routing, LBMR provides a much more reliable routing service than current Zigbee AODV-related solutions. Simulation results show LBMR provides better load balancing and achieves better route maintenance with more reliable packet delivery comparing with AODV and AOMDV. The rest of this paper is organized as follows: Section 2 illustrates how Zigbee-LBMR assists wireless medical monitoring. Section 3 presents LBMR. Section 4 demonstrates our simulation results. Finally, Section 5 concludes this paper.
## 6 Limitations  
This investigation into the discourse of mental health on Reddit offers substantial insights but is accompanied by several limitations. Predominantly, the data originates from user-generated content on Reddit, a platform whose demographics may not encompass the full diversity of the global population. Such demographic constraints potentially introduce biases, suggesting that our findings, while revealing trends within Reddit, may not extend seamlessly to wider mental health dynamics across more varied populations.  
The study's quantitative focus on post volumes, analyzed using heatmap visualization, captures engagement patterns but falls short of interpreting the qualitative depth of user interactions. This approach does not encompass the nuanced discourse quality, tone, or context emerging within online communities, thereby presenting a limited view of mental health discussions' complexity.  
A notable challenge is the categorization of complex mental health narratives, especially within the broad \"trauma and stress\" category. The diverse range of issues encapsulated in this category resulted in ambiguities in distinguishing these narratives from those related to \"personality\". Future studies should consider breaking down \"trauma and stress\" into finer categories, to enhance clarity and reduce annotation overlaps.  
Moreover, the limited scope of our annotated sub dataset, with only 800 posts from a larger corpus, restricts a thorough analysis of the multifaceted mental health discourse on Reddit. Expanding annotation efforts in future research would enhance the robustness and generalizability of findings, offering a more detailed understanding and aiding in the development of more sophisticated machine learning models for automated analysis.  
Another consideration is that our dataset may predominantly reflect transient emotional states, particularly due to the pandemic's impact. The expressions of novelty, loneliness, and isolation on social media during this time may not consistently represent long-term mental health conditions, but rather immediate reactions to an unprecedented global event. Thus, while this study provides a valuable analysis of mental health discourse, it is crucial to consider that some observed trends might be episodic responses to the pandemic rather than indicative of a lasting change in mental health status.  
In light of these limitations, future research should adopt a multifaceted approach, combining both quantitative and qualitative methods and utilizing a broader range of data sources. Such an approach is essential for a more holistic understanding of mental health discussions in the digital age, especially in the context of global crises like the COVID-19 pandemic. This comprehensive perspective will be crucial in informing effective interventions and policy development in mental health care.
## 2 Materials and Methods
### Multivariate Data Analysis  
Principal component analysis (PCA) is a commonly utilized unsupervised statistical method employed for the multivariate analysis of environmental monitoring data [48, 49]. The primary function of PCA is to diminish the complexity of the input data matrix (transfer or accumulate factors of elements in this study) by generating a limited set of new variables termed principal components (PCs) or latent factors. PCs emerge as linear combinations of the initial variables (transfer or accumulate factors of elements in this study). The original autoscaled data matrix is deconstructed into two constituent matrices: the factor loadings matrix, which depicts the contribution of each original variable to the newly formed principal components, and the factor score matrix, which furnishes the updated coordinates of each object (sampling point) within the novel principal component space.  
Partial least squares-discriminant analysis (PLS-DA), conversely, is not a widely adopted supervised technique in the realm of environmental assessment studies [50, 51]. PLS-DA represents a specific variant of partial least square modeling that encompasses the construction of PLS components followed by discriminant analysis within the extracted PLS component space. In this study, the differentiation of distinct sample categories (sand and clay groups) was accomplished through the utilization of the PLS1 algorithm. This algorithm as employed to model the associations between an independent block of variables (i.e., element concentrations in soil samples) and a dependent categorical dummy variable denoting the membership of each sample in either the sand or clay class. The PLS-DA generates various statistics pertaining to the independent variables (element soil concentrations) and the performance of the model. The variable importance on projection (VIP) serves as a metric for gauging the significance of the independent variables in the predictive model. Variables with VIP scores exceeding 1 hold substantial discriminatory power in the classification model. The obtained regression vectors portray the concentration profiles of known sample classes, while the area under the curve (AUC) stands out as the primary parameter for evaluating the predictive models. An AUC of 1 signifies a flawless predictive model.  
All multivariate analysis calculations were performed under MATLAB R2023b using PLS Toolbox 9.2.1 (Eigenvector Research Inc., Manson, WA, USA).
## 5 Discussion  
### Boundary Convergence Performance Analysis of the IB Model  
The premise of the boundary parameter improvement scheme is that the model is expected to have higher convergence performance at the boundary. A model verification analysis was carried out on the 1222 (1) working face of the Zhujidong Coal Mine in Huainan. (The detailed geological and mining conditions of the working face are detailed in reference [36]). A total of 110 monitoring points was selected from the working face from 19 March 2017 to 18 September 2018 for analysis. The PIM parameters of the observation station were obtained using a multi-population genetic algorithm: \(q=0.86\), \(\tan\beta=1.9\), \(b=0.39\), \(\theta=89.0\), \(\mathrm{S_{1}}=21.0\), \(S_{2}=16.0\), \(S_{3}=24.0\), and \(S_{4}=21.0\). The estimated parameters of the improved model were: \(q=0.83\), \(P=0.64\), \(b=0.43\), \(\theta=87.0\), \(S_{1}=10.8\), \(S_{2}=-2.8\), \(S_{3}=16.2\), \(S_{4}=-12.5\), \(\tan\beta_{1}=0.85\), and \(\tan\beta_{2}=2.5\). The final measured subsidence value was compared to the predicted value, as shown in Figure 13.  
As can be seen from Figure 13, the IB prediction model made up for the shortcomings of the PIM, in which the boundary converges too quickly. The fitting error between the measured and predicted values of the IB prediction model was 25.4 mm, and the fitting error between the predicted and measured subsidence values of the PIM was 32.6 mm. In comparison, the overall fitting accuracy of the IB prediction model improved by 28.3%. For the edge area of the mining subsidence basin, the fitting errors were 27.6 and 29.7 mm, respectively. The accuracy of the IB prediction model in the edge area of the subsidence basin improved by 7.6%. It can be seen that the IB prediction model fits the process of mining subsidence surface deformation well.  
Figure 12: Comparison of the measured subsidence values and InSAR-DIB-predicted subsidence values. From (**a**–**c**) are the monitoring points MS29–MS48; in (**d**) are the monitoring points MS01–MS62.
# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 2 Research Objectives and Contributions  
The major goal of this paper is to propose a deep convolution neural network (CNN) which reduces the computational load (model size) in order to reduce the latency by running multiple modules on the same Jetson. Since [PERSON] et al. [13] have already validated ENet implementation on the embedded platform in real-time, this paper focuses on comparing the performance of ENet and the proposed method on a server with the following specification: 128 GB RAM, Intel Xeon E5 processor, and two GPUs - NVIDIA Tesla K40c and Telsa K20c.  
The ultimate goal is to develop a robotics platform that navigates on construction sites. The hardware development (the authors' previous work [13]) and algorithm development (this paper) were tailored to navigation on construction sites. This paper proposes a segmentation method to be efficient while providing accurate semantic segmentation.  
The main contributions of this paper are (1) creating a new pixel-level annotated dataset for real-time and mobile semantic segmentation in construction environments to deal with the limited number of training dataset and (2) proposing an efficient semantic segmentation method with a smaller model size and faster inference speed for future development of autonomous robots on construction sites. Although the focus of this study is on reducing the model size to enable running multiple modules on the same Jetson TX1, the inference time is also decreased, which increases the maximum input frame rate of the segmentation process for real-time performance.
## 2 Background
### Opposition-Based Learning  
Opposition-based learning (OBL) [65] is a heuristic technique used in optimization algorithms to enhance the search process and improve the quality of solutions. It is inspired by the concept of opposition, which involves considering the opposite or contrasting characteristics of a given solution or search-space point. OBL introduces the notion of \"opposition\" to generate new candidate solutions by incorporating contrasting information. The general working principle of opposition-based learning involves the following steps [66]:  
1. **Initialization:** A population of candidate solutions is randomly generated or initialized within the search space.
2. **Evaluation:** Each candidate solution is evaluated using an objective function to determine its fitness or quality.
3. **Opposition generation:** Opposite solutions or individuals are generated for each candidate solution by incorporating contrasting information. This can be achieved in various ways, such as flipping binary values, negating numerical values, or applying specific transformation functions.
4. **Fitness evaluation for opposite solutions:** The fitness of the opposite solutions is evaluated using the same objective function.
5. **Update and selection:** The original candidate solutions and their opposite counterparts are compared based on their fitness values. The better solution between each pair (original and opposite) is selected and considered for the next iteration or generation.
6. **Repeat:** Steps 2-5 are iteratively repeated until a termination condition is met, such as reaching a maximum number of iterations or achieving a desired level of convergence.  
The use of opposition-based learning aims to promote exploration in the search space by considering contrasting information and potentially discovering new regions that may not be explored by traditional optimization techniques. By incorporating opposite solutions, OBL attempts to enhance the diversity and convergence properties of the optimization algorithm. Opposition-based learning has been applied in various optimization algorithms, including evolutionary algorithms, particle swarm optimization, and simulated annealing, among others. It has shown promising results in improving solution quality, convergence speed, and robustness in solving complex optimization problems [66]. The advantages and disadvantages of opposition-based learning can vary depending on the specific implementation and problem domain. Some commonly cited advantages and disadvantages are described below [66].
## 2 Measurement Mechanism
### Design of the 1-D Artifact  
To identify the geometric errors of machine tools, a novel 1-D artifact was designed with measurement targets fixed on it as Figure 2a illustrates. As Figure 2b shows, the target is composed of a plane mirror with a diameter of 10 mm and a revolutionary paraboloid with a diameter of 5 mm. The equation of the revolutionary paraboloid satisfies:  
\[\frac{x^{2}}{0.1547}+\frac{y^{2}}{0.1547}=2z\;(\text{unit}:\;\text{m}). \tag{1}\]  
Figure 1: The 6 degrees-of-freedom errors of the \(X\)-axis.  
The distance of the two targets is approximately 50 mm, and nine targets were selected for a measured linear range of 400 mm. The angle sensor can obtain 2-D angle information by measuring the plane mirror and obtain 2-D micro displacement information by measuring the revolutionary paraboloid.  
[PERSON] elaborated the mechanism by which an angle sensor identifies 2-D micro displacement information by measuring a revolutionary paraboloid in his paper [15]. Figure 3 illustrates the principle of measuring the optical path. Based on the principle of optical auto-collimation, the spot displacement is linearly related to the angle of the measuring surface in the small angle range [16]. On the other hand, the displacement of the reflection point on the revolutionary paraboloid is also linearly related with the tangent plane angle at the reflection point. Thus, the spot displacement is proportional to the displacement of the reflection point.
## 5 Experimental Results
### Parameter Selection Strategy  
#### 5.2.1 Clustering Parameters  
In the experiment, we found that the optimization of DBSCAN clustering and preprocessing strategies for noise processing and imbalanced data significantly affected the detection performance results of the model. The experiment used an adaptive algorithm to adjust the neighborhood radius (Eps) of DBSCAN from 1-11. As shown in Figure 6, it can be found that the number of noise points dropped from 3386 to 1118, while the number of clusters dropped sharply from 291 to 172, and the misclassification rate gradually decreased from 6.45% to 0.67%. This phenomenon shows that although a larger Eps value can effectively suppress noise interference, it may also lead to the loss of fine-grained attack features in the traffic channel. Specifically, when Eps is in the range of 4-7, the misclassification rate is stable between 0.98-2.14%, and the number of clusters is maintained between 244-219. Within this range, a balance is achieved between retaining the local density characteristics of malicious traffic and suppressing random noise. Therefore, Eps is set to four in the experiment as the optimal choice. In the preprocessing stage, we incorporate noise points  
Figure 5: t-SNE Visualization of traffic data distribution.  
into independent clusters through a re-labeling strategy to avoid information loss caused by data discard.  
And through research, it is found that some unknown low-rate continuous-attack malicious traffic may be misjudged as noise because of the overlap of feature density with normal traffic. For example, when Eps is six, the average flow of the normal flow aggregation channel is 3456 pkt/channel with a standard deviation of 1124 pkt/channel, while the attack flow aggregation channel is 4567 pkt/channel with a standard deviation of 1456 pkt/channel. There is a significant difference in the distribution of the two, but low-variance attack traffic may still be filtered due to insufficient local density. In addition, to deal with the problem of imbalanced datasets, we use dynamic balancing of normal and attack samples to alleviate the category imbalance in traffic data, and it shows good robustness in experiments. In the time dimension, the adaptive algorithm effectively captures the dynamic evolution characteristics of attack traffic by combining sliding window technology. For DDoS attacks that last for several minutes, the algorithm can identify changes in traffic patterns at different stages; for short-term port scanning attacks, the continuity of its complete behavior sequence is maintained. It provides a richer feature representation for subsequent real-time detection.