esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Strategies for Upgrading the Dielectric Strength Prediction Protocol
#### 3.2.1 Strategy I: Reparameterization of Previous Protocol  
For PIM, the computational protocol developed in our previous study was adopted for both the equation structure and relevant parameters (0.0012, 0.288, and 0.401). As the parameters were optimized to reliably predict the dielectric strengths of the 137 PFC compounds in the original database, they do not cover the compounds newly added in this study. Therefore, to overcome the slight deterioration in accuracy, re-PIM was developed via the reparameterization of PIM to predict the dielectric strengths of the 168 PFC and non-PFC compounds (Figure 4; Table 1). As shown in Table 1, the reparameterization produced appreciable values for all the parameters of re-PIM, implying that all the factors contribute meaningfully to the predictions of dielectric strength (Table 1). However, compared to PIM, re-PIM provides an insignificant enhancement in the accuracy level of the predictions (Figure 4a,b). Specifically, re-PIM shows a prediction ability comparable to that of PIM, with a trend line of \(y=0.978x+0.087\) and a slightly improved accuracy (relative error = 18.94%; RMSD = 0.328). Improvements in the predication ability may be fundamentally limited by the use of the same equation-based protocol for both PIM and re-PIM.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 4 Analysis and Findings from Experiments
### Evaluation Overview  
Preprocessing transforms raw images into a standardized format, ensuring compatibility with the input requirements of the detection model. Due to varying font styles, the size and dimensions of characters differ across images [18].  
The proposed detection system performs dual tasks:  
* Localizing objects by predicting bounding boxes.
* Classifying them into appropriate categories.  
To evaluate the combined effectiveness of these tasks, we employ Mean Average Precision (mAP), a widely used metric in object detection that integrates both localization and classification performance.  
The Intersection over Union (IoU) metric assesses the overlap between the predicted bounding box and the ground-truth bounding box. It is defined as the ratio of the intersection area to the union area of the two boxes:  
\[\text{IoU}(P,Q)=\frac{\text{Area}(P\cap Q)}{\text{Area}(P\cup Q)} \tag{1}\]  
\begin{table}
\begin{tabular}{c  
A prediction is considered a true positive (TP) if its IoU with the ground-truth box exceeds a predefined threshold (0.5); otherwise, it is labeled a false positive (FP). Using this criterion, precision is computed as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
To obtain a comprehensive evaluation, we calculate AP at multiple IoU thresholds ranging from 0.5 to 0.95 in steps of 0.05. The mean Average Precision (mAP) over all \(C\) classes is then calculated:  
\[\text{mAP}=\frac{1}{C}\sum_{i=1}^{C}AP_{i} \tag{3}\]
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
###### Abstract  
Vehicle Ad-hoc network (VANET) can provide technical support and solutions for the construction of intelligent and efficient transportation systems, and the routing protocol directly affects the efficiency of VANET. The rapid movement of nodes and uneven density distribution affect the routing stability and data transmission efficiency in VANET. To improve the local optimality and routing loops of the path-aware greedy perimeter stateless routing protocol (PA-GPSR) in urban sparse networks, a weight-based path-aware greedy perimeter stateless routing protocol (W-PAGPSR) is proposed. The protocol is divided into two stages. Firstly, in the routing establishment stage, the node distance, reliable node density, cumulative communication duration, and node movement direction are integrated to indicate the communication reliability of the node, and the next hop node is selected using the weight greedy forwarding strategy to achieve reliable transmission of data packets. Secondly, in the routing maintenance stage, based on the data packet delivery angle and reliable node density, the next hop node is selected for forwarding using the weight perimeter forwarding strategy to achieve routing repair. The simulation results show that compared to the greedy peripheral stateless routing protocol (GPSR), for the maximum distance-minimum angle greedy peripheral stateless routing (MM-GPSR) and PA-GPSR protocols, the packet loss rate of the protocol is reduced by an average of 24.47%, 25.02%, and 14.12%, respectively; the average end-to-end delay is reduced by an average of 48.34%, 79.96%, and 21.45%, respectively; and the network throughput is increased by an average of 47.68%, 58.39%, and 20.33%, respectively. This protocol improves network throughput while reducing the average end-to-end delay and packet loss rate.  
VANET; routing protocol; path aware; greedy strategy; perimeter strategy +
Footnote †: journal: Sensors  
0203
# Reddit CrosspostNet--Studying Reddit Communities with Large-Scale Crosposst Graph Networks
## 4 Characterizing CrosspostNet
### Community Analysis
It should be noted that communities discovered using the Louvain method are quite similar to these reported in a recent study [14], where subreddit clusters were established based on their content, modeled with textual embeddings. In that study, the largest clusters also included pornography, politics, and memes, as the top largest communities. Therefore, it can be stated that results obtained on the basis of crosspost connections are consistent with these established for content similarity. Moreover, again, results obtained using two different approaches support the validity of each other. Embedding Similarity  
In addition to finding subreddits that are the biggest crosspost contributors or form node communities, it is possible to approach the graph network seeking subreddits that are similar in terms of their graph structure. While an existing edge is the most direct connection, it is also possible to look for subreddits, which have similar architectural positions in the whole network. Previous studies on social networks (e.g., [22]) discovered that node embeddings can retain a combination of network properties different from the standard metrics (such as degree, centrality, etc.). Therefore, the most prominent solution, mentioned in [22], Node2 Vec [23] has been applied. Node2 Vec is an algorithm that allows embedding graph network nodes and building vectors representing them. The algorithm is based on random walks in the graph. Here, for each node in CrosspostNet, a vector representing the node is generated using the Node2 Vec random walks strategy. Node2 Vec has different hyperparameters, such as p--controlling how deep should the algorithm go, q--controlling the chance of immediately revisiting a node, vector size, number of walks, and walk length. In performed experiments, different hyperparameters for CrosspostNet, using combinations of p (1, 2), q (1, 2), vector size (16, 32, 64), walk length (10, 100, 200), number of walks (100, 200) have been explored. None of them yielded, results that were much different in terms of vector similarity, which is the next step. Hence, results with p = 2, q = 2, vector size = 64 and walk length = 100 are reported. Having generated the vectors, the next step is to find the most similar ones to merge them into groups. Again, vector similarity can be measured with many algorithms. The cosine vector similarity was chosen, because it showed the best results in other vector similarity applications (e.g., [24]). The each-with-each similarity of vectors (representing subreddits) has been used. Next, the resulting pairs were sorted by similarity, and the top pairs were extracted. They are presented in Table 2. Note that Table 2 disregards nodes of degree less than 3 since their structural embedding is trivial and their similarity is obvious and not meaningful.
## 7 Discussion  
### Speech Command Phrase  
The results of the experiment show that speech recognition accuracy/success rate falls as noise levels rise. A regression model was developed from the empirical observation of nearly 3108 speech command utterances--which is 12 speech commands, repeated for each of seven noise levels, by 37 different experiment participants. The polynomial curve fitting characteristic generated for the custom CMU Sphinx ASR can be used to predict speech recognition performance for aerial robotic systems where the CMU Sphinx ASR engine is being used, as well as in the performance comparison with other ASR engines. In addition, it was not clear how the length of speech (the number of speech words in phrase) affects the recognition accuracy because, while there is evidence supporting single-word poor performance like 'hover', there is alternative evidence supporting single-word good performance for words like 'land' and'stop', in the experiment. However, multi-word speech commands may be more reliable and effective than single-word commands due to the possibility of introducing a syntax error checking stage in the recognition process to validate the control command. The composition of multi-word speech commands consists of keywords and modifiers. For example, in the SC7 command \"Go Forward Half Metre\", the primary keyword is 'Forward', the secondary keyword is 'Go', the primary modifier is 'Half', and the secondary modifier is the unit 'Metre'. In multi-word speech commands, the primary keyword and the primary modifier is the most significant, as the failure of these primary parameters would result in the command execution failure. Secondary modifiers aid system usability particularly for human operators, and could be used for error checking within the UAV system to ensure fidelity of command communication.
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 2 Related Work
### wav2 vec 2.0  
The basic framework of wav2 vec 2.0 is illustrated in Figure 3. This framework is designed for self-supervised learning of representations from raw audio data. The original speech audio \(X\) is encoded by a multilayer convolutional neural network, and then mask the generated late speech representations with the same method as masked language modeling \(Z=z_{1},...,z_{l}\). The masked latent speech representations are fed to a Transformer network to build contextualized representations \(C=c_{1},...,c_{l}\), and fed to a quantization network to generate quantized representations \(Q\), to calculate contrastive loss and optimize the model.  
Figure 3: The figure illustrates the wav2 vec 2.0 framework, which aims to learn both contextualized speech representations and an inventory of discretized speech units in a joint manner.  
Figure 2: Overview of wav2 vec: wav2 vec uses two layers of CNNs stacked on top of each other to extract audio features from the original audio input \(X\) and predict the next-step information.  
Its corresponding contrast loss is  
\[\mathcal{L}_{m}=-log\frac{exp(sim(c_{t},q_{t})/\kappa)}{\sum_{q_{i}\sim Q_{i}} exp(sim(c_{t},\bar{q})/\kappa)} \tag{3}\]  
where \(sim(a,b)=a^{T}b/||a||||b||\)is used to calculate the cosine similarity of contextualized representations and quantized representations, and \(kappa\) is a super parameter.
## 2 Materials and Methods
### Statistical Analysis  
The dataset encompassing the responses from a total of 1389 participants was evaluated by employing a blend of descriptive and inferential statistical techniques. The cohort was divided into two distinct groups for the analysis: Group A, comprising 473 healthy subjects with a family history of cancer, and Group B, consisting of 916 participants diagnosed with breast cancer. Continuous variables were summarized using the mean values and standard deviations, while categorical variables were quantified through the frequencies and percentage distributions. The Mann-Whitney test was used to assess the differences in responses between the two groups. Additionally, for hormonal dynamics variables, statistical differences according to the Mann-Whitney test were also calculated by dividing the women from both groups into those younger or older than 45 years of age. The age of 45 was chosen as a cutoff to distinguish between women of childbearing age and those who have presumably surpassed the age of having children, aiming to further refine the analysis based on potential differences in the risk factors or genetic predisposition associated with age. The Mann-Whitney non-parametric test was used instead of a parametric one because both the Shapiro-Wilk and Kolmogorov-Smirnov tests showed significant \(p\)-values, indicating that the data were not normally distributed.  
The statistical analyses were executed using the MATLAB software, 2023b version, adhering to a significance threshold of \(p<0.05\) to validate the reliability and significance of the findings.
## 4 Discussion
### Outlook  
Any comparison of field trial data on dust suppressants is hampered by the fact that different studies pursue individual experimental designs and measurement methods. Hence, there is a clear need for standardized simulated dust suppression tests that provide information that can be used to predict dust emissions from soils. Such a method would be used to determine probable dust dispersion and develop cumulative dust emission and suppression data to support mine permit application requirements. Those methods could also be tools to generate data used to design and implement best management practices and treatment processes needed by mining operations to meet compliance requirements.  
In the mining industry context, a standard testing methodology should be feasible in remote and relatively inaccessible areas (e.g., tailings sites), easy to implement and allow for comparative sampling of various dust control products with minor financial and personnel requirements. Accordingly, experimental setups with field boundary layer wind tunnels, usually used to quantify dust emissions from soils, are not expedient because they are difficult to transport, labor-intensive and pose substantial methodological challenges [58; 59].  
By contrast, methodologies adopting soil plots eroded with commercial electric blowers do pose suitable alternatives, even though measured dust emissions do not reflect dust emissions caused by natural wind erosion since electric blowers do not simulate the atmospheric flow. Those experiments can establish the effectiveness of dust suppressants over different test intervals at locations where they are intended to be applied, indicative of possible commercial-scale application and practice [59]. Moreover, such field trials are easy to conduct and enable cost-effective and low-maintenance testing of various soil treatments with replicates. Future steps that need to be taken to establish a standardized methodology are (i) harmonization of experimental parameters (e.g., applied wind speed, exposure time to simulated wind erosion) and sample preparation, and (ii) definition of requirements on electric blowers (e.g., outlet diameter), dust monitors and test site. It is also recommended to compare measurements of the proposed methodology with the portable in situ wind erosion lab (PI-SWERL) concept. The PI-SWERL is a cost-effective method to assess the potential of soil surfaces to generate dust emissions and correlates with traditional wind tunnel tests [60].
## 2 Target Area and Data  
### Target Area  
Geotechnical data of the soft soil along the southern coast of Korea were collected to develop compression index prediction models using multiple regression analysis and machine learning techniques. Korea's south coast has soft ground located in the Yeongsan, Seomijn, and Nakdong River water systems. The soft ground in the target area is characterized by the deposition of a sand layer on top of a clay layer resulting from flowing water. The thickness of the soft ground is mostly 10-20 m [1]. Table 1 shows the 4868 data points collected from these areas. Figure 1 shows a map of the region. The red portion in Figure 1 represents the targeted area  
\begin{table}
\begin{tabular}{c c} \hline
**Water System** & **Number of Data Points** \\ \hline Yeongsan River & 1759 \\ \hline Seomijn River & 1778 \\ \hline Nakdong River & 1331 \\ \hline Total & 4868 \\ \hline \end{tabular}
\end{table}
Table 1: Number of data points by water system in the target area.
# Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units
## 5 Experimental Verification
### Experimental Procedures  
The following procedures were utilized during the experiment:  
1. We checked to ensure that all components were normal and then started the water pump to add water to the slurry mixing tank.
2. We started the recirculating pump, turned on the densitometer, and supplied cement to the slurry mixing tank via gravity.
3. We confirmed that the density of the cement slurry rose slowly after the cement entered the slurry mixing tank. When the density approached the set density, we adjusted the proportional valve to control the water flow rate.
4. We continued the mixing process, transferred the densitometer data to the PLC, and adjusted the proportional valve through the slurry mixing autonomous control system to finally adjust the slurry density to the design value.
5. We observed the controller output in real time during the experimental process. Thereafter, we checked the valve openness indicator dial and recorded the valve openness.
6. We performed the experiment twice, using one controller for slurry mixing density control each time, and compared and analyzed the experimental results.
## 2 Materials and Methods
### Experimental Setting and Testing Conditions  
An experimental study with repeated cross-over design was adopted. Thus, both the LVP inputs and its transmission outputs to the human body were recorded simultaneously across fifteen trials generated by the combination of three \(f_{\text{set}}\) (20, 25, and 30 Hz), and five static postures [25; 36]. The postures were toe-standing (TS), with knees relaxed and heels raised; erect (ER), with knees relaxed; high squat (HS), with knee flexion at 30\({}^{\circ}\); deep squat (DS), with knee flexion at 90\({}^{\circ}\); and lunge (LU), one leg balanced with knee flexion at 30\({}^{\circ}\). These postures were chosen because they are the most common in the literature [21; 23; 24; 25; 30]. All the postures were performed without holding help, however, as participants had experience with the postures used, they were able to adopt the postures safely. Nevertheless, the three researchers were careful in ensuring the safety and correct posture of the participants. As is customary in transmission studies, the participants were barefoot [16; 17; 20; 21], and they were instructed to locate their feet on the platform marks separated by 28 cm [35] and to maintain an even distribution of plantar pressure [12; 14]. The participants visited the laboratory on one occasion, and the experimental protocol began with 5 min of familiarisation with the vibration stimulus and postures (1 min \(\times\) posture at 30 Hz), which served as a warm-up [7; 13; 14; 20]. After the warm-up, 5 min of rest was used for the WIMUs' attachment, which were well-fixed with elastic bands to the body sites. Then, fifteen experimental bouts were recorded and counterbalanced, which were organised in three sets of five bouts. Each bout of WBV exposure lasted 30 s (s) followed by 30 s of rest between bouts, and 3 min of rest between sets to avoid fatigue in the participants. To determine LVP feasibility, both its performance and its transmission were explored through assessed vibrations applied by the platform across three frequencies (20, 25, and 30 Hz) and five postures (TS, ER, HS, DS, and LU). On the one side, the performance of the LVP was characterised through its inputs of \(f_{\text{in}}\), \(P_{\text{in}}\), \(D_{\text{in}}\), and \(a_{\text{in}}\); on the other side, the transmissions to the human body were characterised through the outputs of \(T\), \(P_{\text{out}}\), and \(a_{\text{out}}\) at AN, KN, HI, L3, and HE.
# Dynamic Performance Optimization of Circular Sawing Machine Gearbox
## 5 Conclusions  
To optimize the rigidity and dynamic mechanical properties of the sawing machine, and to improve its processing quality and stability, an optimization design method that combined topology optimization, sensitivity analysis, and response surface optimization was proposed. The main conclusions are as follows:  
1. Simulating the modal parameters of the gearbox via the addition of mass points, it was determined that the calculated modality has a good correlation with the operational modal analysis.
2. By optimizing the design of the gearbox via topology optimization, sensitivity analysis, and response surface optimization, the quality of this component was reduced by 19% compared to the pre-optimized gearbox, and the natural frequencies of each step were significantly increased, thereby reducing the possibility of resonance.
3. The results for the sawing comparison experiments revealed that, under the same working conditions, the amplitude of the optimized gearbox was significantly reduced, the sawing process was more stable, and the dynamic performance of the gearbox was significantly improved.  
In summary, the main scientific contribution of this investigation is the application of the combined techniques of topology optimization, structural sensitivity, and response surface optimization to a complex structural design, as an effective lightweight design method. Subsequent research work will focus on multi-component reconstruction optimization and multi-factor information fusion algorithms.  
Figure 21: 3D surface topography before and after optimization (70 r/min): (**a**) Before optimization, (**b**) After optimization.  
Figure 22: 3D surface topography before and after optimization (90 r/min): (**a**) Before optimization, (**b**) After optimization.  
**Author Contributions:** Methodology, D.T.; formal analysis, Y.W., D.W. ([PERSON]), C.M. and D.T.; investigation, P.N., Q.L., Y.R. and H.W.; resources, D.W. ([PERSON]) and C.M.; data curation, D.W. ([PERSON]) and [PERSON] ([PERSON]); writing--original draft preparation, Y.W., P.N., Y.R. and H.W.; funding acquisition, Q.L.  
**Funding:** The authors are grateful for financial support from the following research grants: (1) the Natural Science Foundation of China (grant nos. 51405440, 51775501); (2) the Zhejiang Provincial Science Foundation (grant no. LR16E050001); (3) the Commonwealth Technology Project of Science and Technology Department of Zhejiang Province (grant no. 2016C31039); (4) the Growth Plan of Young Teachers in Shandong.  
**Conflicts of Interest:** The authors declare no conflict of interest.