esa-sceva/satcom-chunk-collection huggingface dataset:
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 2 Characteristic Functions and Parameters of PMs versus GSs
### Linear FOV  
* For a GS, using Equation (1), the linear FOV (i.e., the maximum dimension that can be scanned on a certain target plane/lens) is obtained, with Equation (15), Table 1.
* For a PM, by combining Figures 2b and 5a, the linear FOV/scanning domain can be obtained as \[Dom\ h=h(\theta_{max})-h(\theta_{min})\]  
Using Equations (4) and (12) in Table 1, the two terms in the above relationship have rather complicated expressions  
\[\begin{split} h(\theta_{min,max})&=R\sqrt{2}-e-\frac {R-e\left(x\cdot\cos^{2}\alpha+\sin\alpha\cdot\sqrt{1-x^{2}\cos\alpha}\right) }{\left(\sqrt{1-x^{2}\cos\alpha}+x\cdot\sin\alpha\right)\,\cos\alpha}\\ &\quad-L\frac{\left(\sqrt{1-2x^{2}\cos^{2}\alpha}\right)\cos 2 \alpha+2x\cdot sin2\alpha-\cos\alpha\cdot\sqrt{1-x^{2}\cos\alpha}}{2\cos \alpha\cdot\left[x\sqrt{1-2x^{2}\cos^{2}\alpha}+\cos 2x\pm\left(2x^{2}\cos^{2}\alpha-1 \right)\sin\alpha\right]}\end{split}\]  
where the same notation \(x=e/R\) was used, but a simpler, more elegant expression is obtained using these relationships for  
\[Dom\ h=\frac{\sin 2\alpha}{\cos\theta_{min}\cdot\cos\theta_{max}}\left(2e+L \cdot\frac{\cos 2\alpha}{2\cdot\sin\theta_{min}\cdot\sin\theta_{max}}\right),\]  
therefore the final Equation (16) in Table 1 is deduced.  
Figure 5: Rotational angles of the PM, \(\theta_{\min}\) and \(\theta_{\max}\) for which the ray reaches the margins of a PM facet: (**a**) The up- and down-deflected rays; (**b**) the two surfaces of the scan angles \(\theta_{\min}\) and \(\theta_{\max}\) for the (most useful) \(x<1\) and \(\alpha<\pi/4\) intervals with regard to these two parameters.
## 3 Results and Discussion
### Photodegradation in Transparent Glass Containers  
The photodegradation experiments, under the same instrumental conditions reported above, were performed on the DHP standard solutions in transparent glass containers (borosilicate glass flasks), which cut the wavelengths below 300-320 nm. MCR processing of the spectral data recorded in this second set of experiments showed, in most cases, only a slight reduction in t\({}_{10}\) values, but the overall results were not significantly different from those recorded using the quartz containers. These results indicated that the main wavelengths responsible for the degradation process of DHPs were not included in the region cut by the transparent glass.
## 3 Results
### Effect of TB Addition on Taste Attributes of Yogurt  
The E-tongue converts an electrical signal into a taste signal to distinguish the taste of yogurt, and it has a low threshold of sensation and avoids sensory evaluation subjectivity [29]. Figure 4 shows the response values for eight different taste attributes (sourness, saltiness, richness, umami, aftertaste-A, aftertaste-B, astringency, and bittermess). The signal values of saltiness, richness, umami, and aftertaste-B of the yogurt increased considerably after TB was added to the milk before the fermentation process, but the signals of bittermess and aftertaste-A only increased slightly. Compared with the common yogurt, the response value of astringency in optimal yogurt with TB addition (TB yogurt) was slightly lower. There was also a marked decrease in the level of sourness in TB yogurt. Thus, TB can greatly improve the taste profile of yogurt, which could be due to the abundance of flavonoids, organic acids, phenolic acids, and sugars in TB.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Source** & **Sum of Squares** & **Degrees of Freedom** & **Mean Square** & **F Value** & \(p\) **Value** & **Significance** \\ \hline Model & 714.51 & 9 & 79.39 & 13.47 & 0.0012 & significant \\ A & 24.50 & 1 & 24.50 & 4.16 & 0.0808 & \\ B & 21.13 & 1 & 21.13 & 3.58 & 0.1002 & \\ C & 153.12 & 1 & 153.12 & 25.98 & 0.0014 & \\ AB & 2.25 & 1 & 2.25 & 0.38 & 0.5562 & \\ AC & 0.25 & 1 & 0.25 & 0.04 & 0.8427 & \\ BC & 0.00 & 1 & 0.00 & 0.00 & 1.0000 & \\ A\({}^{2}\) & 95.00 & 1 & 95.00 & 16.12 & 0.0051 & \\ B\({}^{2}\) & 67.37 & 1 & 67.37 & 11.43 & 0.0117 & \\ C\({}^{2}\) & 304.21 & 1 & 304.21 & 51.62 & 0.0002 & \\ Residual & 41.25 & 7 & 5.89 & & & \\ Lack-of-fit & 31.25 & 3 & 10.42 & 4.17 & 0.1008 & not significant \\ Pure error & 10.00 & 4 & 2.50 & & & \\ Cor total & 755.76 & 16 & & & & \\ R\({}^{2}\) & 0.9454 & & Std. Dev. & 2.43 & & \\ Adj R\({}^{2}\) & 0.8752 & & Mean & 85.88 & & \\ Pred R\({}^{2}\) & 0.3177 & & C.V. \% & 2.83 & & \\ Adeq Precision & 10.2722 & & PRESS & 515.63 & & \\ \hline \end{tabular}
\end{table}
Table 2: ANOVA analysis for the fitted quadratic polynomial model.  
Figure 3: Response surface plots of variables on the sensory score of the yogurt containing TB. (**A**) Interactions between the amount of TB and the amount of sugar. (**B**) Interactions between the amount of TB and the fermentation time. (**C**) Interactions between the amount of sugar and the fermentation time. Red color means the higher sensory score, while purple color means the lower sensory score.
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Variation of Fault Impedance  
The nature of the fault transients depends on the fault impedance which might affect the performance of the protection scheme. Hence, the algorithm was tested for fault impedances of 0 \(\Omega\), 5 \(\Omega\), 10 \(\Omega\), 15 \(\Omega\), 20 \(\Omega\) and 25 \(\Omega\) for all the investigated faults. The fault index magnitude associated with all the phases during the incidence of an AG fault on node 671 is provided in Table 4. This is infers that the fault index has a higher value compared to the threshold (\(2\times 10^{8}\)) corresponding to phase-A and low corresponding to phases-B & C. Hence, the AG fault is effectively detected in the presence of fault impedance up to 25 \(\Omega\). The algorithm also works efficiently for all types of faults.  
Figure 10: Identification of phase-A to ground fault and incident on node 634 of a test system (**a**) Current waveform (**b**) WD-index (**c**) ALN-index (**d**) Fault index.
## 3 Results and Discussion  
### Preprocessing Comparative Analysis  
Feature selection can improve the prediction performance, and gain a better understanding of the data in machine learning. Feature selection by correlation is a commonly used feature selection  
Figure 3: Main steps of this work.  
method [24; 25]. In this study, a Spearman's rank correlation analysis between the TAs concentration in the soil and the preprocessed spectra was carried out.  
The preprocessing of the soil spectral can effectively highlight the absorption and reflection bands [18; 40; 41]. We calculated the Spearman's rank correlation coefficients for the spectral bands after FD, SD, SNV, and MSC preprocessing (Figure 4). In Figure 4, the black line is the original spectrum Spearman's rank correlation coefficients, where it can be seen that the correlation is low, around \(-0.4\) to \(0.5\). After SNV, MSC, and SD preprocessing, some bands have been improved (red line), but the correlation is still low (\(0.4\) to \(0.5\)). After FD preprocessing, we find bands with correlation of higher than \(0.8\) appears near the \(700\) nm region TAs previous studies indicated [20; 23; 33], the VIS-NIR (650-700 nm) included important wavelengths for estimating TAs contents in soil. Therefore, we selected the three bands (Blue triangle in Figure 4) with the highest correlation coefficient, around \(700\) nm, and input this into the different machine learning models for calculation. Meanwhile, the three bands (blue triangle in Figure 4) with the highest correlation coefficients in other preprocessing methods are selected for comparison [42].  
We then used the model to predict the final result so that we could evaluate the model effect. The results are shown in Table 2. After using SNV, MSC, and SD preprocessing, three bands selected according to the correlation were input into the prediction models, and each model prediction set result was poor. However, the prediction effect of the model with high correlation (\(0.7\) or higher) after using the FD preprocessing is ideal. This shows that as the correlation coefficient increases, the effect of the  
Figure 4: Variation of the correlation coefficients after the different preprocessing. (**a**) SNV; (**b**) MSC; (**c**) SD; (**d**) FD.  
model increases. Using a higher correlation band can greatly improve the stability and predictability of the model [50]. Finally, FD was selected as the preprocessing method, and the three characteristic bands with the highest correlation (700, 703, and 706 nm) were selected for the modeling.
## 2 Data Collection and Quality Evaluation
### Performance of Onboard GNSS Receiver  
We evaluated the availability of TH-2 onboard GNSS data by analyzing the numbers of BDS2 and GPS observations. At present, the receivers can only track the first 14 BDS2 satellites, including five GEOs (C01-C05), six IGSOs (C06-C10 and C13), and three MEOs (C11, C12, and C14). Figure 1 shows the number of observations for GPS satellites. For GPS, the L1 observations numbered slightly more than those of L2, because the signal to noise ratio (SNR) of the L1 frequency was stronger than that of the L2 frequency [29]. Figure 2 shows the number of observations for BDS2 satellites. For BDS2, the number of IGSOs observations was slightly larger than that of GEOs and MEOs, which was the result of IGSOs' higher orbit altitude and better observation geometry. If both the dual-frequency code and carrier phase observations were present in an epoch, then that was an available epoch. The daily average numbers of available epochs were 2340 for the BDS2 MEO, 2226 for the BDS2 GEO, 2867 for the BDS2 IGSO, and 2376 for the GPS satellite.
# Adaptive Path Planning for UAV-Based Pollution Sampling
###### Abstract  
Unmanned Aerial Vehicles (UAVs) continue to gain popularity in applications such as military reconnaissance, environmental monitoring in remote locations, and package delivery. High-Altitude Long-Endurance (HALE) UAVs can remain airborne for extended periods, enabling air pollution measurements to be conducted across a wide range of altitudes, from a few hundred meters above ground level to the lower stratosphere. However, the challenges posed by dynamic environmental conditions and strict energy limitations necessitate the use of adaptive path planning algorithms that account for UAV and environmental models. To address these challenges, we propose a two-tier Adaptive Path Planner (APP), which comprises a Global Path Planner (GPP) and a Local Path Planner (LPP). The GPP, operating offline, generates obstacle-free, energy-efficient paths that adhere to the UAV's kinematic constraints, while the LPP dynamically recalculates alternative routes in real time when obstacles arise. The APP leverages a novel data-driven environmental model, integrating terrain, wind, airspace, and measurement maps. Extensive Model-in-the-Loop testing was conducted to evaluate various single-objective optimization algorithms for the GPP. Subsequently, the APP was successfully validated in simulation scenarios inspired by real-world pollution monitoring missions conducted in Poland and the Arctic. Additionally, the proposed approach was tested under real-world conditions, demonstrating significant application potential. A comparative analysis of the generated paths demonstrated that the APP effectively replaces human operators. Further testing confirmed the APP's capability for adaptive re-planning during mission execution.  
Keywords:path planning; UAVs; pollution sampling; optimization; adaptivity; environment models +
Footnote †: journal: Applied sciences
## 2 Literature Review
### Sentiment Analysis  
Opinion mining or sentiment analysis (SA) is a popular text mining technique that is mainly used to identify user sensibility, affect, and subjective opinions in texts. In previous studies, topic modeling has been conducted on reviews of articles and articles in SNS, and information and social flows were analyzed [17; 18; 19]. In this context, one study has conducted a thorough analysis of text mining of reviews to assess the impact of product reviews on economic performance indicators such as sales volume [20]. Further, [PERSON] and [PERSON] [21] have analyzed the objectivity, subjectivity, and effect of user opinions via analyzing the opinions and sensibilities of Twitter users. In addition, some studies have focused on the past, present, and future trends of SA while others have proposed a new probability model that can overcome the problems of SA and capture a mixture of a subject and affect at the same time [22; 23]. Some studies have also proposed a sensible classifier that can determine the affirmative, negative, and neutral document properties by means of the corpus collection method [24; 25].  
The idea of user experience (UX) includes the concepts of usability and affective engineering and consists of all interactions between the user and the product [26; 27]. Currently, products include not only physical and visible products but also invisible services and algorithms. Therefore, it is necessary to include a UX element in the evaluation of a text mining service or algorithm. According to previous research [28; 29], the elements of UX can be classified as usability, affect, and user value (UV). These studies have also derived a quantification model that integrates these key elements into a single index. In particular, a total of 22 hierarchical dimensions, such as UX, and all elements and sub-elements were evaluated. As these studies on UX and affect are expected to be useful in the design of future products or services, they should be considered in this study.  
Based on our review of the literature, the topic modeling algorithm and SA are widely applied to identify the main topics and customer opinions from texts provided through various sources. In the meantime, the efficiency aspect of keyword extraction has been extensively dealt with by the developers, but there is a lack of consideration on how users experience the algorithm and accept the results. Also, very few studies compare the characteristics of subject word extraction as per evaluation of the perceived trust of individual algorithms; this is the aspect of text mining that we address in this study.
## 4 Performance Evaluations
### Experiment Results  
#### 4.2.1 Consensus Message Processing Performance According to the Total Number of Nodes  
Figure 5 presents the consensus message processing performance based on the total number of nodes in the blockchain network. As depicted in Figure 5, the message processing performance of the proposed PBFT method, the typical PBFT, and the DLM-PBFT was compared, taking into account the maximum number of child groups in scenarios where the total number of nodes varies from 120 to 300. The experimental results indicated that the proposed method exhibited the best consensus message processing performance in comparison to both the DLM-PBFT and the typical PBFT. Both the DLM-PBFT and the proposed PBFT method surpass the performance of the typical PBFT. These two methods ensured effective consensus, even as the number of nodes expanded.  
As illustrated in Figure 5, our proposed PBFT method demonstrated superior performance compared to DLM-PBFT. A key distinction between the DLM-PBFT and the proposed PBFT method is the absence of location-based clustering in DLM-PBFT. In the case of DLM-PBFT, the consensus time increases as the number of nodes in a cluster grows. This is because nodes with relatively longer communication delay times can be included when each group is formed. Thus, the location-based clustering feature of the proposed PBFT method gives it an advantage over DLM-PBFT in terms of the consensus processing time.  
In Figure 5, it is evident that DLM-PBFT's processing performance declined as the maximum number of child groups increased. However, when the maximum number of child groups was limited, such as 2 or 4, the performance difference was relatively minimal compared to other cases. As we explained, DLM-PBFT employs a group-based hierarchy structure, which offers some scalability as the number of nodes grows. With an increase in the maximum number of child groups, this hierarchical structure becomes less distinct and more akin to the typical PBFT structure. As a result, when the hierarchical structure can effectively utilize a smaller number of child groups, DLM-PBFT showed relatively good performance. But as this number grows, performance increasingly resembles that of PBFT.  
As a result of the experiment, the proposed PBFT method showed an improvement in processing performance ranging from about 107% to about 128% compared to PBFT. And the proposed PBFT method showed an improvement in processing performance ranging from about 11% to about 99% compared to DLM-PBFT.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Regression Strategy and Optimization
#### 3.3.2 Optimization  
Training is performed using the Adam optimizer, with an initial learning rate of 0.0005 and a batch size of 64. To avoid overfitting, early stopping is applied with a patience of 20 epochs, and training is capped at 200 epochs.
## 2 Materials and Methods
### Experiment Design and Configuration  
#### 2.6.1 Experimental Dataset  
As described in the previous subsection, we received the data in two stages. Because only 46 images were collected in the initial stage, we randomly split the 46 images into training and test sets with a ratio of 8:1 and generated three synthesized datasets by using the image mixing data augmentation method described in Section 2.2. In the experiments of this study, the training, validation, and test sets consist of 5000, 500, and 100 mixed images, respectively. Note that, during generation of the training and validation sets, the six images reserved for the test were untouched. However, for the test set, in order to generate a wide range of HVs, the synthetization process will randomly select one to ten images from the 40 training samples combined with the samples from the test set to generate a test image with a size of 400 \(\times\) 400. The distribution of HVs for these datasets is shown in Figure 8. As can be seen from these distributions, the distribution of the HVs can be divided into 8 intervals from 350 to 430. Therefore, the number of bins was set to 8 in the proposed model based on multi-task learning. The 59 new images received later were considered as an additional unseen test set to evaluate the generalization of the developed model. In our evaluation, we did not combine these images with any other image collected in the first stage.
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
###### Abstract
[11] finely tuned the model on the basis of the BERT pre-training task for different subtasks in the CKBQA process and obtained good results on the open-domain Chinese knowledge-based question-answering task. However, the aforementioned methods suffer from two disadvantages: firstly, while previous language knowledge can be incorporated into hand-crafted templates, template design can consume a large amount of computational time. At the same time, manual templates tend to have large granularity and are prone to exceptions, which limits the model of ability for generalization. Secondly, the performances of conventional methods are low on the Chinese dataset. Compared with the NER task for English, NER for Chinese is more difficult because Chinese sentences cannot be spatially segmented as naturally as English, and the presence of a large number of indistinguishable entities, as well as the presentation differences between Chinese questions and basic knowledge, which makes it difficult for general models to learn text features adequately. For Chinese NER, the common practice is to use the Chinese Word Segmentation (CWS) tool for segmentation before applying word order tokens. However, the noise present in the CWS tool itself can significantly affect the performance of NER models. For the purpose of this work, we focus on the CKBQA task via the designed CWS and BERT models in order to address the difficulties such as the insufficient association between entities and predicates. To achieve the final answer selection, we link the score of entities and predicates, determining the query path and retrieve a definitive answer. The key contributions of this paper are as follows:  
1. An NER transfer learning model is presented. By fusing CWS in the NER task, the accuracy of candidate entities is improved without introducing too much noise. We also use a pre-trained BERT model combined with CNN and Softmax models for entity disambiguation and predicate mapping in which richer contextual information can be learned. The model makes full use of the problem encoding and candidate information features extracted by the BERT model and has strong generalization for application in multi-domain knowledge bases. 2. The results of the experiments suggested that we have developed a satisfactory question answering system for the Chinese dataset. The current method achieves better on the NLPCC-ICCPOL 2016 KBQA dataset. It can generate more accurate and relevant answers due to the transfer learning and BERT's powerful feature extraction capability. The remainder of this work is organized as follows: Section 2 describes the background of the question-answering system, KBQA, and NER. Section 3 introduces the related technologies. The model, results, and discussion proposed by us will be presented in Sections 4-6. Section 7 provides a conclusion.