esa-sceva/satcom-chunk-collection huggingface dataset:
# The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm
###### Abstract  
In the field of mobile robot path planning, traditional genetic algorithms face issues such as slow convergence, lack of dynamic adaptability, and uncertain mutation directionality. To address these issues, a dichotomy-based multi-step method was used during the population initialization phase to enhance both the quality and diversity of the population. The selection strategy was improved for tournament selection, which reduces the monopolistic dominance of exceptional individuals and preserves population diversity through multiple rounds of grouping competitions. The crossover strategy adopted an adaptive crossover point number, with parents chosen based on a dynamic threshold. The mutation strategy combines a two-layer encoding approach: the first layer uses random mutations to enhance diversity, while the second layer performs goal-oriented mutations, with the selection probability of each layer dynamically adjusted during iterations. Bezier curve optimization of the optimal path was conducted. Experimental results show that the improved genetic algorithm demonstrates significant advantages in terms of path length, smoothness, iteration speed, and computational efficiency.  
Keywords:path planning; genetic algorithm; population initialization; dichotomy and multi-step method; goal oriented
## 2 Site Study and Database
### Site Study  
An analysis of the locations for GNSS-R reflection specular point comparisons is provided in [32], which describes the core validation sites to use in assessing the soil moisture retrieval algorithm using SMAP. Thirty-four candidate sites for core validation were presented in [32], and these sites provided well-calibrated, in-situ soil moisture measurements within SMAP grid pixels (the structure of the pixels is called EASE grid, and it is presented in Section 3). Out of the 34 candidate sites, 18 sites satisfied all resolutions that SMAP provided in public, such as 36-km, 9-km, and 3-km for down-scaling. The data from the core validation sites were quality controlled by applying a site-specific spatial scaling function and comparing with SMAP-derived soil moisture to acquire precise soil moisture values. Therefore, by using the core validation sites, the derived result in this study can be guaranteed through the qualified references.  
Figure 1 represents the International Geosphere-Biosphere Programme (IGBP) land classification, which arranged the type of Earth's surface into 16 classes of land, including water. The IGBP is created by using Moderate Resolution Imaging Spectroradiometer (MODIS) data and it is included in SMAP metadata with equivalent resolution to soil moisture. Among the 16 classes of the land, the land cover classification of \"grasslands\" is used in the analysis process. As an initial study for calculating quantitative dielectric constant using GNSS-R, this research has only considered the reflection occurring on flat surfaces with grasslands. Some researchers have reported the results of the relationship between land classification and the qualities of the reflected signals from GNSS satellites [33; 34; 35]. In relation to this study, the quantitative effect of land classification to soil moisture will be the subject of a future study.
## 2 Principles and Methods
### Selection of Paramater Indicators  
According to the principles of the VMD algorithm, several initial parameters need to be set when decomposing the signal using VMD, such as the decomposition scale K, the penalty factor \(\alpha\), parameters related to the update of the center frequency (DC component), and the convergence tolerance \(\epsilon\). K is actually the k of the above equations, and when we extract k as a parameter for optimization, we refer to it as K. Therefore, it is necessary to analyze the impact of these parameter settings on the decomposition results of the VMD algorithm [27; 28].  
The above analysis indicates that the selection of the decomposition scale K and penalty factor \(\alpha\) in the VMD algorithm is crucial for the extraction of profiles and the reconstruction of signals in the presence of strong cultural noise [29]. To evaluate the denoising performance of typical strong cultural noise under different K values, this paper conducts a comprehensive analysis using parameters such as Normalized Cross-Correlation (NCC) and signal-to-noise ratio (SNR). The relevant parameter definitions are as follows:
## 4 Experimental Results
### Evaluation of Scale in Combination with Extrinsic Calibration  
We evaluate the accuracy of combining scaling with extrinsic calibration to better understand the impact of errors in the extrinsic calibration. First, we scale the point cloud and transform it into the _ego_-coordinate frame using our estimated extrinsic calibration. The results are then compared with LiDAR point clouds, transformed using the ground truth extrinsics from the OSDaR23 dataset.  
As in Section 4.1, we start by evaluating image-wise scaling and image-wise extrinsic calibration across all scenes that meet the preliminary requirements. The results are shown in the upper subplot of Figure 10b. Unlike the scale factor, extrinsic calibration is a fixed physical setup unaffected by dynamic camera parameters. Therefore, in the next experiment, we average the extrinsic calibration and apply it to the image-wise scaled point clouds. This represents an operational scenario where extrinsics are consistently averaged  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Average Error**} \\ \cline{2-4}  & **UniDepth** & **Depth Pro** & **MiDaS** \\ \hline Translation & 0.19 m & **0.16 m** & 0.41 m \\ Rotation & **5.17 deg** & 5.61 deg & 5.45 deg \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average deviations of extrinsic calibration parameters from ground truth.  
Figure 10: Evaluation of the extrinsic calibration. (**a**) Average deviations of extrinsic calibration parameters from ground truth. (**b**) Average Chamfer distances of image-wise scaled point clouds combined with image-wise (upper) and average (lower) extrinsic calibration.  
while scaling remains dynamic. The results of this approach are shown in the lower subplot of Figure 10b.  
Finally, we evaluate using an averaged scale and averaged extrinsic calibration across the entire dataset. The results are presented in Figure 11. The average Chamfer distances for all three experiments are additionally summarized in Table 3.
## 3 Results and Discussion  
### Fire Damage Assessment  
The goal of this study was to examine the spatial and temporal patterns of forest regeneration and then assess the driving factors relevant to the vegetation recovery process, especially for fire damage. The differences between the immediate post-fire and pre-fire NDVI values enabled us to identify the fire-damaged areas that were likely burned. Figure 2 shows a spatial pattern of fire damage that was computed from the pre- to post-fire difference in GIMMS NDVI. Red pixels represent large NDVI differences between pre- and post-fire, while blue pixels represent small NDVI differences. Fire damage is expected to influence the spatial configuration and arrangement of forest patches, contributing to and influencing ecological processes during post-fire recovery and succession [32]. Therefore, to track vegetation behavior that was affected by different fire damage extents, we classified the fire damage into five classes using the z-score (standard deviation) method [33].  
A z-score, or standard deviation, measures the dispersion of data. A reclassification procedure was used to divide the z-scores of the simple difference images (the standardized difference between pre- and post-fire NDVI values) into five categories (Table 2).  
Figures 3 and 4 illustrate the result of the fire damage classification using the z-score approach. The image is mapped in a blue-red color scale. Low z-scores are represented in blue, while a red color was assigned to high z-score pixels. The spatial pattern of the fire damage classes is clearly visible in the map with an evident differentiation between very high and slight patches.  
There were five different fire sources for this large fire event. Some burned areas joined together while others did not (Figure 4 and Table 3). Therefore, very high and high fire damage pixels were concentrated in a few disjunctive regions.  
Figure 3: The fire damage of the entire burned area divided into five classes: Very high (VH), High (H), Moderate (M), Low (L) and Slight (S).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Pre-Fire Year** & **Post-Fire Year** & **Standard** & **z-Score** & **Fire Damage** & **Pixels** & **Percentage** \\
**(1984–1986)** & **(1987)** & **Difference** & **Class** & **(\%)** & **(\%)** \\ \hline
**0.7708** & 0.6542 & \(-2\)\(\sim\)\(-1\) & 1 & Slight & 5 & 2.44\% \\
**0.7764** & 0.5352 & \(-1\)\(\sim\)0 & 2 & Low & 49 & 23.90\% \\
**0.7852** & 0.3868 & 0\(-1\) & 3 & Medium & 94 & 45.85\% \\
**0.7884** & 0.2239 & 1\(-2\) & 4 & High & 46 & 22.44\% \\
**0.8165** & 0.0524 & 2\(\sim\)999 & 5 & Very high & 11 & 5.37\% \\ \hline \end{tabular}
\end{table}
Table 2: Fire damage classification using the z-score method.  
Figure 2: Fire damage map of the study area for the Daxing’anling fire event with fire damage values obtained using Equation (1).
# Corrosion Monitoring Techniques in Subcritical and Supercritical Water Environments
## 3 Invasive Corrosion Monitoring Techniques  
The electrochemical online corrosion monitoring technique has the characteristics of high sensitivity, fast response speed, stable long-term service, a simple process, and low cost. It can accurately determine the corrosion rate and form of materials and achieve automatic feedback control at industrial sites. More importantly, based on alloys' electrochemical corrosion data, corrosion rate prediction, stress corrosion cracking evaluation, and other related determinations can be carried out. Electrochemical corrosion monitoring methods have broad application prospects in complex extreme sub-/supercritical water environments, such as higher temperatures, pressures, and high content of salts and dissolved oxygen; occurrence in cooled-water nuclear power plants; and advanced supercritical water treatment equipment.
## 5 Implementation and Evaluation
### Success Rate and Time Efficiency Compared with Other Work
#### 5.3.2 Time Efficiency Performance  
To validate that our approach can successfully balance the success rate with communication overhead, we compute the identification time of 5292 real IoT devices. We compare our approach with another approach based on protocol popularity. The idea of ranking protocol popularity is inspired by previous work [36]. We rank the probe methods as HTTP, HTTPS, RTSP, FIT, SSH, TELNET, SNMP, CWMP, and PPTP based on the number of responses.  
Figure 7 shows how the identification success rate changes with the maximum number of probe methods for the two approaches. Figure 7a-c show how the success rate changes when identifying brand, model, and firmware version, respectively. Our scheduling policy achieves a higher identification success rate with fewer probe methods. Especially for firmware version identification, our approach reaches a success rate of 83.71% with a combination of no more than five probes. By contrast, the approach based on protocol popularity requires a combination of nine probes to achieve a similar success rate.  
In addition, we calculate the time to identify 5292 real IoT devices. Our approach requires 10.89 min to achieve success rates of 96.89%, 93.43%, and 83.71% for the brand, model, and firmware version, respectively, while the popularity-based approach requires 24.73 min. Our approach can reduce the identification time by 55.96% when identifying large-scale IoT devices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Work**} & \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Features**} & \multicolumn{4}{c}{**Success Rate of Identification**} \\ \cline{3-6}  & & & **Type** & **Brand** & **Model** & **Version** \\ \hline
[36] & Protocol Banner Fingerprints & Protocol Banners & over 90\% & NA & NA & NA \\ \hline Our Work & Probe Scheduling & Multi-layer features & 93.43\% & 96.89\% & 93.43\% & 83.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Success rate compared with other work.
## 5 Discussion  
### Comparative Analysis in the Quantity of CPs  
In order to further explore the capability of the proposed methods for monitoring ground displacement in mining areas, a comparative analysis was conducted for the results in four key subsidence zones obtained by the three methods shown in Figure 6. The quantity of CPs in four key subsidence zones were statistically counted, as shown in Figure 7 and Table 2. It can be seen that the quantity of CPs obtained by the proposed method is significantly more than the conventional time-series InSAR method. Furthermore, combined with the land use and feature classification dataset [34; 35], the points distribution in the four zone was analyzed. According to the dataset of the study area, the land use within the study area is mainly divided into three categories, crops, water bodies, and buildings, as shown in Figure 8. The displacement distribution results obtained by the PS-InSAR technique in four zones are mainly concentrated in the building distribution area, while the mining area is mainly dominated by low vegetation (crops) [36], making the image low coherence and, therefore, the quantity of points selected by the PS-InSAR technique is low. SBAS-InSAR technique maintains SAR image coherence with short spatial and temporal baselines [37], covers a range of non-building areas in addition to the building areas where the PS points are distributed, with an increased point density compared to  
Figure 6: Comparative analysis of ground cumulative displacement at characteristic points in different methods: (**a**) T1; (**b**) T2; (**c**) T3; and (**d**) T4, respectively.
## 3 Model Building and Algorithm Design  
### Problem Formulation  
This paper selects the ore pressure data of C Coal Mine as the research dataset and uses the logistic regression method to predict the mine pressure hazard. The prediction model is shown in Figure 1.  
The purpose of this paper is to predict whether there is danger in the mine pressure, help managers predict and judge whether the danger is caused by mine pressure exceeding the threshold value, and make appropriate decisions in time. In order to manage users more conveniently, in terms of the result orientation, the output results only need to be the presence of danger and the absence of danger, so as to provide decision-making basis for managers. It is clear that the problem is a binary classification problem. Therefore, the mine pressure hazard is divided into \"existence danger\" and \"non-existence danger\". Let \(y\) represent whether the mine pressure is dangerous; then, \(y=0\) means that the mine pressure does not exceed the standard and there is no danger, and \(y=1\) means that the mine pressure exceeds the standard and there is danger. There are many factors that affect mine pressure hazard, and together they form the evaluation indicator system that affects the mine pressure hazard. Let the evaluation indicator system \(X=[x_{1}x_{2}x_{3}\ldots x_{i}]\); \(x_{i}\) for \(i=1,2,3\ldots n\) represent the i-th evaluation indicator selected in the preprocessed data. The logistic regression prediction model is used to study the degree of influence of each evaluation indicator on the mine pressure hazard and the prediction results. The input of the prediction model is the evaluation indicator system \(X\), that is, the preprocessed mine pressure data, and the output is the prediction result \(y\), that is, whether the mine pressure is dangerous. Finally, the logistic regression prediction model driven by the sample data of the mine pressure is established.  
Figure 1: The mine pressure hazard prediction model.
# The Analysis of Service Convergence in an Optical Access Network +
## 4 Results and Discussion
### Cost Analysis  
While the performance metrics of the UFMC and GFDM demonstrate their technical feasibility, the cost implications for deploying these technologies in optical networks require further consideration. Key factors include the cost of upgrading infrastructure, purchasing advanced modulation and transmission equipment, and ongoing maintenance expenses, all of which must be considered in the overall analysis. For instance, the integration of additional filtering mechanisms in the UFMC could result in higher initial costs but lower operational expenses due to its efficient spectrum utilization. Conversely, GFDM may present lower upfront costs but higher operational expenses due to the complexity of signal processing.
# An Empirical Evaluation of Document Embeddings and Similarity Metrics for Scientific Articles
## 4 Embeddings and Measures
### Embeddings
**ELMo**, the acronym of Embeddings from Language Models, was introduced by [PERSON] et al. [26]. It uses a new type of networks, Recurrent Neural Network (RNN), i.e., a neural network that has internal connection with itself, allowing the use of an internal state (memory). Although we did several experiments with it, some others did not finish in acceptable time, as explained later. The last method we consider is named Bidirectional Encoder Representations from Transformer, or **BERT**, due to [PERSON] et al. [2]. In this case, BERT uses another type of architecture, the transformer [27]. A transformer involves an encoder and a decoder, containing both an attention layer, being this layer the one that allows dispensing with the recurrence of RNNs. Because there is no memory, they need to consume an entire set of tokens. Both the encoder and the decoder have three Multi-Head Attention layers. Unfortunately, BERT does not work with texts containing more than 512 tokens, which is an arbitrary selection carried out by the model's authors. This is not enough for scientific articles, since they commonly have from 1 k+ to almost 10 k different tokens (see Section 7.1 for concrete numbers). Thus, using some technique such as splitting the text into multiple parts might be unfair, and the results difficult to interpret, if compared directly to the other techniques. Other possibilities might be the evolutions of BERT, such as RoBERTa [28] or ALBERT [29], which outperform BERT training during more time and/or using more data, or the recent Transformer-based models which can work with more than 512 tokens. Namely, XL-Transformer, which has no input length limit [30], or its evolution, XLNet, also with no input limit and outperforming BERT in many tasks [31]. There have also been published document-specific transformer-based models such as Longformer [32] (which, in the published version, supports up to 4096 tokens, still smaller than required). Unfortunately, these systems have higher training/inference computation requirements. For example, the Longformer model pretrains starting on the RoBERTa released checkpoint [32]. As a result, we pulled BERT and other aforementioned models from the analysis since they are extremely hard to train if we want remarkable results, and they have unjustified ecological impact [33]. e.g., [PERSON] et al. [6] estimated the emitted \(CO_{2}\) for the training of the BERT-base model published by Google in 652 kg, with 79 hours of training in a 64 Nvidia Tesla V100 GPUs. Another important point in this decision is the prediction time of big bunches of large documents such as a thesis, due to the number of computations required by these big models. With this paper, we show that simpler and less complex models, that can be executed on a regular desktop, we achieve remarkable results. Therefore, we will focus on the already-established models in the literature.
## 4 Automated Online Traffic Collection, Sample Construction, and Correlation
### System Overview  
The classification of encrypted traffic is similar to other supervised learning classification problems in machine learning, including training and applying. The labeled datasets are used in the training phase. It is worth noting that the sample collection should be deployed on the end because almost all the traffic is encrypted in the communication link for ISPs. Even the deep packet inspection devices cannot determine the application of TLS traffic since the plaintext parameter, like SNI, is changeable. On the contrary, the data are generated on ends and servers, and the user behaviors are sourced from the applications on the end. The data are not encrypted before becoming the traffic. Therefore, in the end system, we can make sure what the traffic is and collect the traffic simultaneously.  
To guarantee the integrity, purity, authenticity, and credibility of the dataset, we propose the A3 Cautomated encrypted traffic labeled sample collection, construction, and correlation architecture, as shown in Figure 3. The A3C includes two procedures: the online acquisition procedure and the offline validation procedure, in which there are five modules. The asynchronous scheme can significantly reduce the overhead of collecting devices because the sample acquisition is time-sensitive, but validation is not. Validation requires a much higher calculation cost because of the high complexity computation, especially in encryption detection. Each module in Figure 3 is described in detail later.  
Although we have deconstructed the entire lifecycle of automated encrypted traffic labeled sample acquisition and designed the A3C system architecture, many issues still need further study. The first is the operating system platform. The collection and construction  
Figure 3: A3C system architecture.  
of encrypted traffic samples cannot be separated from the end system. If encrypted traffic sample data with broader coverage and various environments are required, it is necessary to make A3C capable of supporting various mainstream OS platforms.  
The second problem is the interference between background traffic and other applications. The current traffic sample collection scheme needs to limit the application's network access, including a particular pre-operation cost and OS background traffic interference. In an OS, the running unit of the application is the process. If we want to separate the traffic belonging to different applications, we need to separate the traffic at the process-level first.  
The third is the indeterminate coupling between user behavior and application traffic. That is, the traffic generated by the application is either spontaneous or user-generated, which needs to be considered.