esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In various applications of automatic underwater vehicles (AUVs), it is necessary to acquire the real-time location and speed information of the AUV. However, the complicated and fluctuating marine environment leads to measurement information loss. The accuracy of noise measurement is vital for accurate state estimation, but it is difficult for traditional algorithms to acquire time-varying noise measurements. Due to inaccurate process models and measurement noise, the filtering performance becomes poor or even diverges. To address the problems above, a switched variational estimation filtering (SVEF) algorithm, which combines the advantages of both Gaussian filtering and the variational estimation (VE) method, is proposed. In SVEF, VE is embedded into a linear or nonlinear filtering algorithm. Owing to the continuous discrete underwater navigation model, the state vector is estimated by the SVEF in the case of measurement information loss, and the accurate position and velocity of the AUV are determined. The experimental results prove that the SVEF achieves better positioning precision and is more robust than other conventional algorithms for AUV applications.  
underwater navigation; measurement information loss; switched variational estimation filtering; linear and nonlinear filtering +
Footnote †: journal: Applied  
14 May 2022  
Accepted: 27 June 2022  
Published: 30 June 2022
## 3 Soil Moisture Mapping Methodology
### Synthetic Database  
The parameterized WCM was used to generate a synthetic database of backscattering coefficients in the VV and VH (\(\sigma^{\circ}\)VV and \(\sigma^{\circ}\)VH) from a wide range of \(\theta\) (20\({}^{\circ}\) to 45\({}^{\circ}\)), \(mv\) (2 to 40 vol %), \(Hrms\) (0.5 to 3.8 cm), and NDVI (0 to 0.75) values. Each element of the database is composed of a combination of four input parameters \(\theta\), \(mv\), \(Hrms\), NDVI and a simulated \(\sigma^{\circ}\)VV and \(\sigma^{\circ}\)VH. The synthetic database comprises 84240 elements (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Min Value** & **Max Value** & **Element Numbers** \\ \hline \(mv\) (vol \%) & 2 & 40 & 20 \\ \(Hrms\) (cm) & 0.5 & 3.8 & 18 \\ NDVI & 0 & 0.75 & 9 \\ \(\theta\) (\({}^{\circ}\)) & 20 & 45 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum and maximum values of the input parameters of the WCM.  
To make the synthetic database of backscattering coefficients more realistic (i.e., similar to real SAR data), the error corresponding to Sentinel-1 radiometric accuracy was added to the simulated backscattering coefficients. This error is approximately 0.70 dB (3\(\sigma\)) for the VV polarization and 1.0 dB (3\(\sigma\)) for the VH polarization [52]. Thus, we considered two absolute zero-mean Gaussian random additive noises with a standard deviation of \(\pm\)0.7 and \(\pm\)1.0 dB to be added to the simulated backscattering coefficients in the VV and VH, respectively. Moreover, given that the relative error in the NDVI derived from the optical images is approximately 15% at the most [53; 54], the NDVI values in the synthetic database were noisy, with a zero-mean Gaussian additive relative noise of 15%.  
For each synthetic database backscattering coefficient, 10 random noises were used (zero-mean Gaussian noise with a standard deviation of 0.7 and 1.0 dB for the VV and VH, respectively). In addition, for each NDVI in the synthetic database, 10 random noises were also used. The synthetic database with noisy backscattering coefficients and NDVI is called the noisy synthetic database in this paper. It is composed of 8,424,000 elements. Finally, the noisy synthetic database was randomly divided into two equal sub-databases, one for the neural network training (noisy training database) and one for the neural network validation (noisy validation database).
## 4 Conclusions  
In the present study, we synthesized five different derivatives of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2\mathrm{x}}\)O\({}_{4}\) (\(0.01\leq\mathrm{x}\leq 0.05\)) NPs hydrothermally. The structure and surface morphology of Mn\({}_{0.5}\)Zn\({}_{0.5}\)SmEuFe\({}_{1.8-2\mathrm{x}}\)O\({}_{4}\) spinel hydrothermal NPs were characterized by the XRD, SEM, TEM, and EDX methods, respectively. We examined the impact of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2\mathrm{x}}\)O\({}_{4}\) (\(0.01\leq\mathrm{x}\leq 0.05\)) NPs on both normal (HEK-293) and \  
and cancerous (HCT-116) cells. We found that after 48 h of treatment, Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (\(0.01\leq\mathrm{x}\leq 0.05\)) NPs showed a significant decline in the cancer cell population. The IC\({}_{50}\) value of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (\(0.01\leq\mathrm{x}\leq 0.05\)) NPs ranged between 0.75 to 2.25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. When tested on normal and healthy cells (HEK-293), we found that the treatment of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (\(0.01\leq\mathrm{x}\leq 0.05\)) NPs did not produce any effects on normal cells, which suggests that Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (\(0.01\leq\mathrm{x}\leq 0.05\)) NPs selectively targeted cancerous cells. The anti-bacterial properties of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (\(0.01\leq\mathrm{x}\leq 0.05\)) NPs were also evaluated by MIC and MBC assays. We conclude that Mn\({}_{0.5}\)Zn\({}_{0.5}\)Sm\({}_{x}\)Eu\({}_{x}\)Fe\({}_{1.8-2}\)O\({}_{4}\) (\(0.01\leq\mathrm{x}\leq 0.05\)) NPs produced via the hydrothermal route possess potential anti-cancer and anti-bacterial abilities.  
All authors equally contributed to this study.  
Authors thank to Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University.  
The authors declare no conflicts of interest.
## 1 Introduction
### Geopolymer Concrete and Fiber-Reinforced Composites
Fly ash is one of the basic raw materials used in the production of geopolymer materials. Fly ash is divided into three types according to ASTM 618-03. Table 1 shows the chemical composition requirements for each class. Class C fly ash is mainly from the combustion of brown coal or bituminous coal, while class F includes products from the combustion of black coal or anthracic coal. Class N includes unprocessed or calcined natural pozzolans i.e., certain diatomaceous earths, shales, volcanic ash, or pumice. For the production of geopolymer materials in the world, ash grades C and F are most commonly used as raw material [31; 32]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Class N** & **Class F** & **Class C** \\ \hline SiO2 + Al2O3 + Fe2O3 (min. \%) & 70.0 & 70.0 & 50.0 \\ \hline SO3 (max. \%) & 4.0 & 5.0 & 5.0 \\ \hline Humidity (max. \%) & 3.0 & 3.0 & 3.0 \\ \hline Loss on ignition (max. \%) & 10.0 & 6.0 & 6.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical composition requirements for fly ash classifications (based on [31; 32]).
## 3 Preliminaries  
### Network Model  
Suppose that there are \(n\) sensor nodes randomly deployed in the \(m_{1}\times m_{2}\) rectangle monitoring area. In order to simplify the problem analysis, some assumptions about the DSN scenario are given as follows.  
1. Each sensor node could collect data and send them to BS.
2. The position information of sensor nodes could be obtained by BS.
3. All directional sensor nodes had the same initial energy, sensing radius, angle of view (AoV) and communication ability.
4. Each sensor node could be cluster head or member node.
5. Each sensor node could reduce data transmission by data fusion.
6. All directional sensor nodes could guarantee the network connectivity.
# Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
## 3 Problem Modeling
Graph Load Balancing Routing Algorithm Based on Link Weight Characterization and Its Implementation  
The MC problem is a mixed-integer linear programming (MILP) problem, which is difficult to solve efficiently. To solve the MC problem effectively, a link weight calculation rule is first designed to dynamically convert the load status and link delay into link weights. Based on this link weight, the efficient solution of the MC problem can be transformed into a shortest path problem. Furthermore, a joint scheduling algorithm for time-sensitive and non-time-sensitive services is proposed, explaining the link weight update rules. This ensures that non-time-sensitive services are evenly distributed across the satellite network while guaranteeing the QoS of time-sensitive services. The algorithm dynamically adjusts link weights in real-time to adapt to changes in the satellite network topology. Given the predictable nature of satellite orbital movements, the algorithm can anticipate changes in network topology and promptly recalculate optimal routes, ensuring continuity in path selection and balanced load distribution. Through this design, the algorithm demonstrates enhanced adaptability and robustness within the frequently changing topological and traffic conditions of satellite networks, significantly improving overall network performance.
# Maintaining the Sense of Agency in Semi-Autonomous Robot Conferencing
## 5 Experiment
### Measures
#### 5.5.2 Questionnaire  
To estimate the following operator feelings, the subjects were asked to complete a questionnaire. Their answers were rated on a nine-point Likert scale, as follows: 1 = strongly disagree, 5 = neutral, 9 = strongly agree.  
Figure 2: Example of the movement of view point at the semi condition, and the method to calculate the gaze rate.  
**Sense of agency:** In reference to previous studies, we set the following two items.  
* I felt as if I were controlling the head of the robot by myself.
* I felt as if the movement of the robot head were my own.  
**Social telepresence:** Because determining the feelings of being in the same room is useful for measuring the social telepresence of the remote conversation partner [8; 12; 13; 29], we used the same representation to measure the social telepresence and set the following two items.  
* I felt as if I were listening to the partners in the same room.
* I felt as if I were speaking to the partners in the same room.  
**Comfort level:** In addition to the above feelings, we measured how much the subjects felt comfortable during the robot conferencing. We expected that the subject might feel comfortable, as the autonomous behavior substitutes the subject's teleoperation, and set the following four items.  
* I felt nervous while listening to the partners.
* I felt nervous while speaking to the partners.
* I was able to listen to the partners comfortably.
* I was able to speak to the partners comfortably.  
As other indicators of comfort level, we recorded how often the subject remembered the partner's answer. We considered that substituting the teleoperation with autonomous behavior might enable the operator to focus on the conversation. The method used to conduct this memory test will be described in the procedure section.  
**Intention:** When the operator did not notice the intervention of autonomous behavior during teleoperation, they may feel that the behavior is their intentions. Furthermore, when autonomous behavior was convenient for the subjects, they may hardly notice it. To confirm these predictions, we added the following two items.  
* I felt that the robot head was moving according to my intentions.
* I felt that the robot head was looking in the appropriate direction, depending on the situation.  
The questionnaire including these two items was completed after showing a recorded video of the conversation of the subject (the view angle is shown by the bottom right picture of Figure 1) was shown to them. This was done so that the subject could evaluate these feelings objectively.
## 4 Optimization
### Comparison of Performance Curve  
Figure 19 and Table 6 show a comparison of the performance of the original and optimized models. In Figure 19a, at the design point (DP), it can be seen that the efficiency of the optimized model improved 1.98% compared to that of the original model. Under the condition of an expansion ratio of 1.34 or higher, it was confirmed that the efficiency of the optimization model was improved compared to that of the original model (Table 6). The extra mechanical losses which occur at the volute and the rotor back disk were considered. Figure 19b shows the temperature trends at the expander outlet relative to the expansion ratio. The outlet temperature of the optimized model was 0.32% lower than that of the original model at the design point. Figure 19c shows the relationship between the expansion ratio and the expander output power. At the design point, the power of the optimized model was approximately 2% higher than that of original model.  
Figure 19: Performance comparisons between the original model and optimized model: (**a**) expansion ratio vs. efficiency; (**b**) expansion ratio vs. expander outlet temperature; (**c**) expansion ratio vs. expander power.
## 2 Materials and Methods
### Evaluation  
The proposed framework is compared with three other conventional algorithms:  
* FBCSP (without feature selection): this method has 17 filter banks, ranging from 4-40 Hz, a 4 Hz bandwidth, 2 Hz overlapping, and the number of spatial filters \(m\) is three.
* DFBCSP--Fisher: This method has 17 filter banks, ranging from 4-40 Hz, a 4 Hz bandwidth, 2 Hz overlapping and the number of spatial filters \(m\) is three Fisher's ratio is used for band selection.
* DFBCSP--mRmR: This method has 17 filter banks, ranging from 4-40 Hz, a 4 Hz bandwidth, 2 Hz overlapping, and the number of spatial filters \(m\) is three. mRmR is used for band selection, which could have a maximum of six filter banks.
* Proposed method (DFBCSP--FmRmR): t\(\backslash\)This method has 17 filter banks, ranging from 4-40 Hz, a 4 Hz bandwidth, 2 Hz overlapping, and the number of spatial filters \(m\) is three. Both Fisher's ratio and mRmR are used for band selection, which can be carried out without manual screening. The selected bands are varied after each loop and can be up to six filter banks.  
The results are compared in terms of accuracy and F1-score. While accuracy is a metric indicating the proportion of properly recognized instances, F1-score is primarily concerned with the disagreement between the true positive rate and the positive predictive value. Consequently, this study employs both metrics for a more objective evaluation. The models are evaluated on both the self-acquired dataset and the public BCI competition dataset. The results are visualized through bar charts, confusion matrixes, tables, and ROC curves.
## 3 Results
### Estuarine Habitat Mapping
#### 3.1.2 WorldView-3 vs. RapidEye  
Results from both WV and RE are promising for their ability to accurately map wetland habitats (Figures 5 and 6). Given that some habitat patches within the study area are <25 m\({}^{2}\) (the area of a RE pixel) it was assumed that RE would have trouble distinguishing these features. This was particularly evident in the over prediction of scrub-shrub in RE imagery and can be seen in the training sample spectral signatures where there is more noise and mixing in the classes evidenced by the dip in the Supratidal Sand class on the red edge band, suggesting some mixing with slightly vegetated pixels. These partially mixed pixels were overpowered by the scrub-shrub class and were actually more often herbaceous, emergent wetland, or sand. Results show classifier confusion between scrub-shrub and herbaceous that could not be differentiated by RE, but could with WV, likely due to increased spatial resolution rather than spectral differentiation (Table S1). Other than this difficulty differentiating the herbaceous class on RE, RE and WV had similar results across the other classes. Both had difficulty separating emergent wetland from intertidal sand and scrub-shrub and high accuracy for most other classes.
## 3 Methods
### Char-Grams Models
The best accuracy is obtained with seven-char-grams for RF and SMO requires less tweets that the best accuracy for the six-char-grams. As we increase the length of the char-grams to seven, we can observe how the accuracy of the model decreases gradually reaching to an 83.30% for the RF classifier and to 85.50% for SMO; both with the corpus-3. The results for the eight-char-grams achieve a best accuracy for the RF classifier with 85.00% with the corpus-1 and SMO achieves an 89.80% with the corpus-3. The usage of char-grams instead of word-grams have reduced the sparsity of the data. However, there are much more redundant features in the corpora. As we are applying supervised learning for prediction, this extra features does not lowers the accuracy. However, they increase the storage and speed concerns. In addition, the differences between RF and SMO with smaller corpus size are significant. With unigrams, bigrams and trigrams, SMO and RF achieve similar results (See Table 3). However, char-grams performed worse with less corpus-size. This was due to the same features being able to appear both in positive and negative tweets and SMO was not capable of performing a good separation between then. The accuracy among the size of the corpus for the RF model is shown in Figure 8 and for the SMO model in Figure 9. We can observe the accuracy of five, six and seven char-grams achieve a peak of the accuracy for the corpus-3. As the number of tweets increases, the accuracy of each length of char-grams keeps the same distance, with the best accuracy of five-char-grams and decreases a mean of 2.5% each time we increase in 1 the length of the char-grams sequences. The results for the combinations of [5; 6], [6; 7] and [7; 8] char-grams are shown in Table 6. These results indicate that, as long has we increase the length of the char-grams, the performance of the classifiers decreases slightly. In concrete, the accuracy of RF goes down from 90.50%, with the combinations of [5; 6] char-grams, to 89.80% with the combination of [6; 7] char-grams and, finally, until 88.00%. Comparing SMO, we observed a similar loss of accuracy; from 89.30% with the combination of [5; 6] char-grams, to 88.50% with the combination of [6; 7] until 88.30% with the combination of [7; 8] char-grams. We can also observe that there was less accuracy with the fixed char-grams individually, where RF achieved also an accuracy of 90.80% with five-char-grams and SMO 90.00% (see Table 5). Figure 8: Comparison of the accuracy between five-, six-, seven- and eight-char-grams for the RF classifier.
# A Survey of Machine Learning Methods for Time Series Prediction
## 6 M5 and M6 Forecasting Competitions
### Takeaways from M5 and M6 Forecasting Competitions  
One of the most notable takeaways from the M5 competition was the dominance of LightGBM. Its ease of use allowed even relatively inexperienced participants to excel, as evidenced by the first-place finisher in the M5 Accuracy Competition, an undergraduate student. Most top-performing teams relied on ensembles of LightGBM models, leveraging its efficiency in handling large datasets. The results of the M5 competition also emphasizedthe importance of combining multiple models. Most top teams utilized ensembles, often containing hundreds of individual models, to enhance the accuracy of their predictions. Across both the M5 and M6 competitions, cross-validation (CV) and hyperparameter tuning emerged as indispensable components of successful forecasting methodologies. All top-performing teams employed thorough CV to optimize their models.  
One critical takeaway from the M6 competition was the importance of the data quality, feature engineering, and the inclusion of exogenous variables. Unlike the M5 competition, where all participants worked with a standardized dataset, M6 participants were responsible for sourcing their own data. The complexity of this competition, including the unique challenges in the financial domain, also resulted in the number of participating teams to decrease to less than 4% of the M5 competition's turnout (despite a substantial increase in incentives). The results of the competition showed no clear consensus on the best-performing methodology and suggest that the importance of the data quality and strong feature engineering, including the exogenous variables chosen, often outweighs the importance in the choice of the prediction model itself.
## 1 Introduction, Motivation, and Related Research
The research results state that agent technology requires less effort and time than IEC-61499 FB to implement three configurations of the RAS, as IEC-61499 FB lacks of inter-platform communication flexibility. An Intelligent control of an airport Baggage Handling System (BHS) has been studied in [7] as an RMS case-study. It provides an extended customized Multi-Agent System (MAS) solution, by modeling every component of the BHS as an IEC-61499 FB. Basically it is dividing the whole conveyor transportation system into many segments. Every conveyor segment is presented as a customized FB. Moreover it models every bag over the BHS as another customized autonomous FB. All the instances of the conveyor segment FB and the bag FB are composing together a MAS to provide a distributed decision making process. The topics of [8; 9] are focusing their attention on the human-robot cooperation in assembly manufacturing. The focus of [8] is to provide a communication and integration architecture to support the planning of an assembly case-scenario. The assembly case-scenario is involving the loading and the assembly of the rear axle of a passenger vehicle. The research provides a layered conceptual architecture with no detailed specifications for the responsibilities and the functionalities of every layer. Furthermore no real implementation has been shown by the research to prove the viability of the architecture. The study in [9] tries to schedule the assembly tasks among two robots in cooperation with one worker. The criteria that have been used to schedule the cooperation were the average resources utilization and the mean flow time. Both criterion aim to reduce the operational resource dwell time. The research uses the Robot Operating System (ROS) middleware to achieve its concept. Even ROS could be a good environment for the academic studies, it cannot really fit in a real industrial application. This because ROS is not a real time solution, plus it has the disadvantage of a single master node. This means if the master node fell down, the whole architecture will be obsolete. This is in contradiction with a MAS solution such as JAVA Agent Development Environment (JADE), which provides an agent management system for every platform. This means if one platform fell down, it will not affect the other distributed platforms. This details will be discussed extensively in Section 3.2. In the next section of this paper we will formulate the research problem. In Section 3 some key technologies and methodologies will be highlighted. Those key technologies and methodologies will help to construct the solution conceptual model in Section 4. The implementation of this solution model will be explained in details in Section 5. Section 6 will show a case study where a variable number of workers should cooperate with a variable number of IRs to produce any quantity of customized products. Finally Section 7 will discuss and summarize the research to wrap it up with the conclusion and the challenges in the future work.