esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Advanced Oxidation/Reduction Techniques  
Advanced oxidation/reduction techniques have been used for the degradation of PFAs, with the advantages of high conversion efficiency and simple operation, and some techniques could achieve complete mineralization. However, these techniques generally put emphasis on long-chain PFAs, including PFOA and PFOS. Whether these techniques could remove short-chain PFAs was still lacking in studies. This section provides a systematic summary of the application of the degradation of short-chain PFAs about the techniques of electrochemical oxidation and photocatalytic degradation, etc.
# Similarity Estimation for Large-Scale Human Action Video Data on Spark
## 5 Discussion  
ELP shows competitive results on the UCF Sports action dataset. Our proposed approach for similarity measure of human action video data on top of Spark can be employed in different application scenarios such as estimating the similarity in exercise and dancing games [5]. For example, lots of users are performing exercise or dance activities following reference videos in the database, while cameras record all their actions. After they finish their performance, these recorded videos are delivered to the HDFS and compared with reference videos saved in the HDFS. In this case, the proposed method can measure similarities of users' actions and reference actions. Therefore, it can assist users to know how similar their actions are to reference actions when they do these kinds of human activities.
# High Precision Indoor Positioning System for Mobile Asset Management and Safety +
### Industrial Use Cases  
There are numerous applications of the proposed IPS solution, such as in industries, shopping malls, transportation hubs, etc. Some of the tasks achieved through IPS include asset tracking and indoor navigation, indoor positioning of cars in the showroom, workers' safety in warehouses, asset and personnel tracking and tunneling, the tracing of personnel in disasters, etc. This enables the massive market covering the IPS to be cost-effective and precise.
## 7 Conclusions  
This paper proposed an outage sequence optimization strategy for M&LVDNs based on user importance. An improved AHP-EW algorithm was used to rank the importance of LV users. Case studies verified that this strategy could effectively reduce the economic losses caused by power outages and improve the overall operational efficiency of the power system. Furthermore, the proposed model can effectively minimize system losses even when multiple faults simultaneously occur.  
In conclusion, the research findings of this paper have significant practical implications and application value for enhancing the reliability and safety of LVDNs. However, in this paper we have only considered medium-voltage line faults, ignoring the impact of low-voltage faults on users. This will continue to be studied in our future work.  
Conceptualization, W.L., J.W., H.B., Y.Y., M.X., Y.L., H.W. and W.H.; methodology, [PERSON], [PERSON], [PERSON] and C.L.; software, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON] and C.L.; investigation, [PERSON] and C.L.; resources, [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON], [PERSON], M.X., [PERSON], [PERSON], W.H. and C.L.; visualization, [PERSON] and W.H.; supervision, [PERSON], [PERSON], [PERSON], M.X. and H.W.; project administration, [PERSON]; funding acquisition, [PERSON] and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by China Southern Power Grid Co., Ltd. Science and Technology Project (030102 KK52220003).  
Not applicable.  
The data in our manuscripts is contained within the article from the authors'. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [PERSON] is employee of China Southern Power Grid Co., Ltd., who provided funding and teachnical support for the work.
## 3 Results  
### Satellite Altimetry Validation  
Correlations were calculated between altimetry data, for the grid box over five Caribbean locations (Figure 1) for which local tide gauge records exist, and the gauge records. As already noted, research quality tide gauge data for Jamaica are unavailable for the altimetry era. The five stations were chosen because (i) their data are freely accessible, (ii) they are in countries which share large-scale climate characteristics with Jamaica, and (iii) there was a period of overlap of the record with available altimetry data. Table 2 provides details for the tide gauge stations, the corresponding altimetry grid boxes, and the correlation values. Plots of the altimetry and gauge data for the respective periods of overlap are provided in the Appendix (Figure A1).  
Correlations were significant for four of the five stations and exceeded +0.7, ranging from 0.74 in the southeast Caribbean (Dominica) to 0.89 in the north Caribbean (Puerto Rico). The correlation magnitudes are consistent with similar analyses establishing satellite altimetry as viable for use in the Caribbean [16; 22]. [PERSON] (2018) [24] suggests that sea) level fluctuation trends may be shared between Jamaica and Puerto Rico, which, in this study, showed the highest correlation between station data and altimetry. For one station (Haiti), the correlation was low (0.35) but still significant. The variation in values across the four stations may be due to a number of factors, including the distance between the altimetry grid points and the tide gauge station, or local considerations such as wave reflection, which may reduce the ability of satellite altimetry to capture small changes in sea level very close to the coast.  
Satellite altimetry data for the grid box closest to Kingston, Jamaica (17.875\({}^{\circ}\) N, 76.652\({}^{\circ}\) W) were extracted and used to investigate sea level change off the south coast of Jamaica.
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 2 Background  
### Notation  
* \(\mathcal{I}\) denotes an inertial frame of reference.
* \(I_{n\times n}\in\mathbb{R}^{n\times n}\) denotes the identity matrix of size \(n\).
* \({}^{\mathcal{A}}p_{\mathcal{B}}\in\mathbb{R}^{3}\) is the position of the origin of the frame \(\mathcal{B}\) with respect to the frame \(\mathcal{A}\).
* \({}^{\mathcal{A}}R_{\mathcal{B}}\in SO(3)\) represents the rotation matrix of the frames \(\mathcal{B}\) with respect to \(\mathcal{A}\).
* \({}^{\mathcal{A}}\omega_{\mathcal{B}}\in\mathbb{R}^{3}\) is the angular velocity of the frame \(\mathcal{B}\) with respect to \(\mathcal{A}\), expressed in \(\mathcal{A}\).
* The operator \(\operatorname{tr}(\cdot):\mathbb{R}^{3\times 3}\to\mathbb{R}\) denotes the _trace_ of a matrix, such that given \(A\in\mathbb{R}^{3\times 3}\), it is defined as \(\operatorname{tr}(A):=A_{1,1}+A_{2,2}+A_{3,3}\).
* The operator \(\operatorname{sk}(\cdot):\mathbb{R}^{3\times 3}\to so(3)\) denotes _skew-symmetric_ operation of a matrix, such that given \(A\in\mathbb{R}^{3\times 3}\), it is defined as \(\operatorname{sk}(A):=(A-A^{\top})/2\).
* The operator \(S(\cdot):\mathbb{R}^{3}\to so(3)\) denotes _skew-symmetric_ vector operation, such that given two vectors \(v,u\in\mathbb{R}^{3}\), it is defined as \(v\times u=S(v)u\).
* The _vee_ operator \(\cdot^{\vee}:so(3)\to\mathbb{R}^{3}\) denotes the inverse of the _skew-symmetric_ vector operator, such that given a matrix \(A\in so(3)\) and a vector \(u\in\mathbb{R}^{3}\), it is defined as \(Au=A^{\vee}\times u\).
* The operator \(\left\lVert\cdot\right\rVert_{2}\) indicates the L2-norm of a vector, such that given a vector \(v\in\mathbb{R}^{3}\), it is defined as \(\left\lVert v\right\rVert_{2}=\sqrt{v_{1}^{2}+v_{2}^{2}+v_{3}^{2}}\).
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 4 Bleach of Photodarkened Fiber
### Thermal-Bleaching  
Temperature is used to describe the degree of thermal movement of micro particles, which has a significant impact on the stability of the color center and is an important factor in the thermal annealing effect of the color center. It is generally believed that the higher the temperature, the more serious the annealing of the color center [22; 84; 85]. In 2009, [PERSON] et al. [21] studied TB of PD-induced loss in a 10 \(\upmu\)m core diameter, LMA Yb-doped silica fiber. Pristine and photodarkened samples are subjected to thermalization. With the increase of temperature, PD loss begins to recover at \(\sim\)350 \({}^{\circ}\)C and completely recovers when the temperature reached \(\sim\)625 \({}^{\circ}\)C. However, before recovery, the photodarkened fiber exhibits a further heat-induced increase of absorption loss. This phenomenon is attributed to the permanent increase of color centers and a temperature-dependent broadening of the absorption spectrum [23]. However, some studies have shown that the decrease of PD-induced loss in a specific band with the increase of temperature may be the result of the transition from one color center to another, not necessarily the annealing of the color center [86; 87].
# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
###### Abstract  
The rapid expansion of software applications has led to an increase in the frequency of bugs, which are typically reported through user-submitted bug reports. Developers prioritize these reports based on severity and project schedules. However, the manual process of assigning bug priorities is time-consuming and prone to inconsistencies. To address these limitations, this study presents a Priority-Sensitive LSTM-Attention mechanism for automating bug priority prediction. The proposed approach extracts features such as product and component details from bug repositories and preprocesses the data to ensure consistency. Priority-based feature selection is applied to align the input data with the task of bug prioritization. These features are processed through a Long Short-Term Memory (LSTM) network to capture sequential dependencies, and the outputs are further refined using an Attention mechanism to focus on the most relevant information for prediction. The effectiveness of the proposed model was evaluated using datasets from the Eclipse and Mozilla open-source projects. Compared to baseline models such as Naive Bayes, Random Forest, Decision Tree, SVM, CNN, LSTM, and CNN-LSTM, the proposed model achieved a superior performance. It recorded an accuracy of 93.00% for Eclipse and 84.11% for Mozilla, representing improvements of 31.11% and 40.39%, respectively, over the baseline models. Statistical verification confirmed that these performance gains were significant. This study distinguishes itself by integrating priority-based feature selection with a hybrid LSTM-Attention architecture, which enhances prediction accuracy and robustness compared to existing methods. The results demonstrate the potential of this approach to streamline bug prioritization, improve project management efficiency, and assist developers in resolving high-priority issues.  
bug priority prediction; LSTM-attention mechanism; priority-based feature selection; software maintenance; automated bug prioritization Article
## 3 Results
### Water Quality Monitoring  
A significant variation in Chl-a and TSM was observed over the one-year study period in Lake Tana. The Chl-a concentration varied between seasons with the highest values at the end of the wet season (August-October 2020). The minimum Chl-a concentration was recorded in February 2020 (4.45 mg m\({}^{-3}\)) during the dry season (Figure 4b). In that period, the highest concentration was observed at the northeastern and eastern part of the lake where the mouths of the three major and other small tributary rivers are located (Figure 1). Concentrations increased progressively along the entire east shore and the southwestern part during the rainy season, reaching values over 40 mg m\({}^{-3}\) by the end of August (Figure 4h). TSM concentrations also varied in space and time during the study year (Figure 5), showing a spatial pattern similar to that of Chl-a. However, the lowest mean values were observed in May (30.1 g m\({}^{-3}\), Figure 5e) and the highest during the late wet season, in September (62.5 g m\({}^{-3}\), Figure 5i). Generally, higher TSM values were encountered near the northeastern and eastern shores of the lake.  
Figure 4: (**a–**l) Chlorophyll-a concentration (Chl-a, mg m\({}^{-3}\)) in Lake Tana from January 2020 to December 2020 estimated from Sentinel-2 satellites after the C2 RCC processor.
# Use of Vine Shoot Waste for Manufacturing Innovative Reinforced Cement Composites
## 3 Results and Discussion
### Fourier-Transform Infrared (FT-IR) Spectroscopy  
The FT-IR spectra of raw materials (CEM and VSW), CP and CP10 VSW samples are presented in Figure 9. The FT-IR spectra (Figure 9) of CEM show the presence of silicates, as suggested by the bands at \(\sim\)930 cm\({}^{-1}\) attributed to Si-O asymmetric stretching vibration, at \(\sim\)522 cm\({}^{-1}\) attributed to Si-O out-of-plane bending vibration and by the shoulder at \(\sim\)461 cm\({}^{-1}\) attributed to Si-O in-plane bending vibration. As well as the specific bands for silicates, there were specific bands detected for the S-O stretching (\(\sim\)1154 cm\({}^{-1}\)) and bending (\(\sim\)604 cm\({}^{-1}\)) vibration in the SO\({}_{4}\)\({}^{2-}\) group; the asymmetric stretching (\(\sim\)1434 cm\({}^{-1}\)) and the out-of-plane bending (\(\sim\)884 cm\({}^{-1}\)) vibration of the CO\({}_{3}\)\({}^{2-}\) group as well as the bands specific for -OH group stretching (\(\sim\)3660 cm\({}^{-1}\)) and in water (\(\sim\)3420 and \(\sim\)1622 cm\({}^{-1}\)) [29]. The FT-IR spectra of the CP confirms the formation of cement hydration products: calcium silicate hydrate gel (CSH) and CH [46]. Following the hydration, the blue shift of specific bands for silicates from 929 cm\({}^{-1}\) to 974 cm\({}^{-1}\) was noticed, confirming the polymerization of silicates and the formation of the calcium silicate hydrate gel [46,47]. The Si-O bond peak also suffers a change in profile after the hydration. The increase of the bands specific to carbonates and to water indicates the carbonation process  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **O** & **Ca** & **Si** & **Al** & **Na** & **Ca/Si** & **Ca/(Si + Al)** \\ \hline CP & 53.30 & 35.05 & 8.51 & 1.73 & 1.43 & **4.17** & **6.42** \\ CP10 VSW & 49.90 & 44.07 & 4.70 & 1.05 & 0.29 & **9.37** & **32.83** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Element concentrations (%) in CP and CP10 VSW samples on the surface.  
Figure 8: Surface mapping of (**a**) CP and (**b**) CP10 VSW samples at high magnification.  
that appears during hydration. The FT-IR spectra of CP10 VSW are similar to that of CP, calcium silicate hydrate gel (CSH) and CH are present, except for a small peak that appears at \(\sim\)2940 cm\({}^{-1}\) and is specific to -CH stretching vibration in the VSW. The FT-IR spectra of VSW show the absorption peaks specific to the cellulosic components of the vegetal fibers: -OH groups at \(\sim\)3330 cm\({}^{-1}\), -CH groups in saturated alkane chains at \(\sim\)1370, \(\sim\)2923 and \(\sim\)2855 cm\({}^{-1}\), C=O group at \(\sim\)1740 cm\({}^{-1}\), C-O groups at \(\sim\)1042 cm\({}^{-1}\) and Si-O groups at \(\sim\)594 cm\({}^{-1}\)[47].
## 2 Formulation for Modeling
### Design of Modified TF to Suppress Overestimation in High-Frequencies
In this regard, we applied the principle of the Wiener filter to the present the Kalman-filter technique, as described in the following. Reference [20] explains some filters for original-image-restoration from blurred images. First, the Inverse filter is introduced. Assuming that the blurring function D, which is a sort of transfer function, is known, the inverse filter can be directly used in the frequency domain to recover the original image. The observation equation can be expressed in the frequency domain, as;  
\[\mathrm{G(u,v)}\ =\ \mathrm{F(u,v)}\ \mathrm{D(u,v)} \tag{21}\]  
where  
G: observed image  
F: original image  
D: blurring function  
The original image F(u,v) can be restored by filtering the observation G(u,v). The inverse filter \(\frac{1}{\mathrm{D(u,v)}}\) provides a perfect estimate of the original image in the case of no noise as follows:  
\[\mathrm{\hat{F}=G(u,v)\frac{1}{\mathrm{D(u,v)}}} \tag{22}\]  
where  
\(\mathrm{\hat{F}}\): estimated image  
However, if there is sensor noise N(u,v) as Equation (23), the result will be filtered noise i.e., the sond term in Equation (24) and the desired image. The term is called \"Inverse Filtered Noise\" [20]. The inverse filter typically has very high gain at certain frequencies, where D(u,v) is small. The amplified noise at those frequencies will dominate the result. Figure 11 is the schematic diagram of noise amplification. In the bottom right of the figure, \(\mathrm{\hat{F}}\) the estimated image is amplified (red solid line) in the high frequency region by the inverse filter.
## 3 The Optimized Kinematic Modeling Method
### Limitations of Kinematic Modeling  
The forward kinematic of the mobile manipulator robot is mainly used to solve the position and posture of the mobile manipulator. In this article, the mobile manipulator is composed of two parts: the mobile platform and the manipulator system. Therefore, kinematic modeling is established based on the basic parameters of each link and the essential parameters of the mobile platform. However, many shortcomings have been observed in the kinematic modeling results of the mobile manipulator robot based on other studies [41; 42].
## 5 Computational Experiments
### Experiments in Dynamic Environments  
#### 5.4.1 Experimental Setup  
In this section, a comparison of the proposed method and conventional methods is made in a problem environment where tasks are given from time to time. As a conventional method, the approach by Q-learning [49] of [PERSON] et al. (2020) was used, which can be applied to dynamic problem environments. The number of AGVs was fixed at 10, and given a task from time to time, the total transfer period required to complete a defined number of tasks and the computation time were compared.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Model-Based Intent Recognition Methods
#### 2.1.1 Template Matching  
Template matching utilizes predefined behavioral templates to match observed actions against known patterns [18]. Template matching is a widely used technique for intent recognition, leveraging predefined templates to match observed behaviors and infer intentions. It has been applied in various domains, such as human activity recognition, facial expression analysis, head motion detection, and air target intention recognition, demonstrating high efficiency and generalization, especially in scenarios with limited training samples.  
In the study of [PERSON] et al., the signal recorded by the three-axis accelerometer was automatically extracted and the user and axial-independent template was matched with the actual observed active signal [19]. The study further combines template matching with feature extraction techniques, such as feature extraction based on dynamic time warping, for human action recognition and complex activity classification. By simplifying the characteristics of the observed signal, template matching technology can effectively process high-dimensional data and improve computational efficiency, which makes it a significant advantage in wearable device data analysis [20; 21]. In complex scenes such as facial expression and head posture recognition, template matching is successfully applied to human-computer interaction system and auxiliary equipment design by detecting the user's face in real time and matching the head motion feature template [22]. This study presents a case-based inference system for classifying opponent configurations in adversarial air combat environments, demonstrating improved accuracy and resilience compared to standalone learned behavior models, even under noisy conditions and high model errors [23].  
The strengths of template matching lie in its simplicity, robustness to noise, and effectiveness in small-sample learning problems. However, its limitations include reliance on predefined templates, high computational costs for large-scale or high-dimensional data, and limited scalability for unseen behaviors or dynamic environments.
## 4 Conclusions  
This study was conducted to evaluate the potential of _Lemma minor_, _Salvinia minima_, _Ipomoea aquatica_ and _Centella asiatica_ as phytoremediation agents in removing TSS, NH\({}_{3}\)-N and COD from sewage wastewater samples. These plants were chosen because of their high tolerance to various pollutants and also because of their rapid growth rate. The results indicated that after the 8 days of treatment, the sewage wastewater treated by _Lemma minor Salvinia minima_, _Ipomoea aquatica_ and _Centella asiatica_ showed a reduction in TSS content at 50.8%, 77.6%, 85.6% and 67.6%, respectively. For the NH\({}_{3}\)-N removal, the four treatment samples showed a reduction of approximately 80.4%, 89.9%, 97.3% and 79.1%, respectively. The value of COD in sewage wastewater treated by _Salvinia minima_ reduced about 82% compared to _Lemma minor_, which reduced only 75% of COD content. _Ipomoea aquatica_ able to remove up to 44.8% of COD and _Centella asiatica_ removed 36.46% of COD. The one-way ANOVA test showed a significant different on sewage wastewater quality before and after the treatment by all plants. Based on the reduction percentage of TSS and NH\({}_{3}\)-N, sewage wastewater treatment with _Ipomoea aquatica_ showed a more efficient reduction, while _Salvinia minima_ was more efficient in reducing the COD content. Hence, phytoremediation treatment method was proven to be able to remove pollutants and enhance the water quality with a low capital cost. This method is also safe to be implemented as it is environmentally friendly.  
Formal analysis, N.H.H. and P.A.S.F.; writing--original draft preparation, N.I.H.A.A. and M.M.H.; writing--review and editing, M.M.H.; supervision, M.M.H.; project administration, M.M.H.; funding acquisition, M.M.H. and N.I.H.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Universiti Kebangsaan Malaysia, grant number DIP-2019-001.  
**Acknowledgments:** [PERSON] was financed by the UKM research grant (MI-2020-005).  
**Conflicts of Interest:** The authors declare no conflict of interest.