esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Granulated Ti-Dopped Manganese Oxide Oxygen Carriers in Chemical Looping Processes
## 1 Introduction
Reduction-oxidation cycles were performed with the different samples, showing an improvement with respect to the pure manganese oxide in the reoxidation capacity in air and in the reoxidation temperature, which was increased up to 950 \({}^{\circ}\)C. After analyzing the composition of the samples, it was observed that the redox pair responsible for the release of oxygen was Mn\({}_{2}\)O\({}_{3}\)-Mn\({}_{3}\)O\({}_{4}\). The pyrophanite remained stable in the successive redox cycles of reduction-reoxidation and did not intervene in the release of oxygen. However, everything seems to indicate that its presence favored the reoxidation of Mn\({}_{3}\)O\({}_{4}\). Similar conclusions were obtained in a more recent study, where manganese oxides from different origins (both commercial and synthesized) were evaluated [26]. In the case of the Mn-Ti samples, besides the oxygen uncoupling capability of the manganese oxygen carriers, lattice oxygen in the manganese oxide can also be transferred to burn the main combustion gases in a conventional CLC process through the following redox reactions involving the redox pair Mn\({}_{3}\)O\({}_{4}\)/MnO:  
\[\text{Mn}_{3}\text{O}_{4}+\text{H}_{2}\to 3\text{MnO}+\text{H}_{2}\text{O}\] (r3)  
\[\text{Mn}_{3}\text{O}_{4}+\text{CO}\to 3\text{MnO}+\text{CO}_{2}\] (r4)  
\[\text{4 Mn}_{3}\text{O}_{4}+\text{CH}_{4}\to 12\text{MnO}+2\text{H}_{2} \text{O}+\text{CO}_{2}\] (r5)  
Considering all the above, the objective of the present work is to further investigate the effects of TiO\({}_{2}\) addition to manganese oxide in order to assess the viability of these materials for the CLOU/CLAS or TCES processes as well as for CLC. The novelty of this work in relation to previous studies lies in the method of preparation of the mixed Mn-Ti materials. In this work, the possibility of producing them by granulation of the corresponding commercial powders will be studied. In turn, the durability and the evolution of the reactivity of these materials under several redox cycles will be studied for the first time, as well as their fluidization properties for applications that require the use of fluidized bed reactors. This way, the assessment of their potential as oxygen carriers for different Chemical Looping processes can be done.
## 5 Conclusions  
Based on the analysis of the grain size and geochemical characteristics of the surface sediment in the Bohai Bay, this paper studied the provenance contribution of rivers, sediment migration and diffusion, and discussed their relationship with ocean dynamics. The main conclusions are as follows:  
1. The results of grain size trend analysis showed that coastal sediment in Bohai Bay is generally removed from the coast, which is consistent with the widely observed shore retreat.
2. Discriminant function (DF) index based on Ca/Al can indicate the sediment contribution of the Yellow River and Luanhe River to Bohai Bay. Materials from the Yellow River dominated southern, western and central Bohai Bay, while materials from the Luanhe River dominated northeast Bohai Bay. The DF analysis indicated that the dominance of the Yellow River source was greater than previously thought, and the influence boundary between the Yellow River and Luanhe River is estimated to be at \(38^{\circ}50^{\prime}\) N. The influence of the Haihe River source is limited to the west of \(118^{\circ}\) E.
3. With the diversion of the Yellow River channel and the reduction of material feed into the sea, sediment transport in Bohai Bay is now controlled mainly by marine dynamic processes. In both south and north Bohai Bay, sediment was transported into the Bay under the influence of constant wave directions, strongest wave directions and tidal residual currents. The central sea area and the underwater bank slope along the west coast of Bohai Bay have become the main sink areas of sediment. The coast has become an important source of sediment in Bohai Bay, and the source is higher up to include the coast itself.  
Conceptualization, B.Z.; methodology, L.Z. and P.W.; formal analysis, B.Z.; investigation, B.Z., P.Z. and J.Y.; writing--original draft preparation, B.Z. and L.Z.; writing--review and editing, L.Z., P.W. and Y.Y.; visualization, J.Y.; supervision, Y.Y.; data curation, X.L. and S.Y.; and project administration, X.L. and S.Y. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the Foundation of State Environmental Protection Key Laboratory of Marine Ecosystem Restoration (No. 202106) and the Doctoral Scientific Research Foundation of Liaoning Province (No. 2021-BS-300).  
Data Availability Statement: Data sharing not applicable.  
The authors would like to thank the editors and reviewers for constructive suggestions and comments that helped improve the quality of the article.  
The authors declare no conflict of interest.
## 2 Sequential Coupling Model
### CFD Model
#### 2.3.2 Turbulence Model  
The governing equations are closed by introducing the SST \(k\)-\(\omega\) turbulence model [26]. The SST \(k\)-\(\omega\) model integrates advantages of both \(k\)-\(\varepsilon\) turbulence model and \(k\)-\(\omega\) turbulence model, and it controls value of dissipation source term in a specific dissipation equation with the blending function \(F_{1}\). As shown in Equation (3), coefficient \(\varphi\) can be changed with the variation of \(F_{1}\) function  
\[\varphi=F_{1}\varphi_{1}+(1-F_{1})\varphi_{2} \tag{3}\]  
where \(\varphi\), \(\varphi_{1}\), and \(\varphi_{2}\) are coefficients of the SST \(k\)-\(\omega\) turbulence model, \(k\)-\(\omega\) turbulence model and \(k\)-\(\varepsilon\) turbulence model, respectively. When \(F_{1}=0\), turbulence in the region far away from the wall is solved by the \(k\)-\(\varepsilon\) turbulence model. On contrary, the \(k\)-\(\omega\) turbulence model under low Reynolds number is  
Figure 1: Overall framework of the coupling model.  
applied in the region close to the wall when \(F_{1}=1\). As a result, the SST \(k\)-\(\omega\) turbulence model are believed to be able to provide a relatively high prediction accuracy of the performance curves of pump with acceptable computational cost [27; 28].
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 4 Performance Evaluation and Lessons Learnt
### Results and Discussion
#### 4.2.2 Vibration Dataset Classification  
The Vibration dataset contains three channels. The data was collected under scenarios, such as excessive vibration, falling human, and falling of object, within the shop floor. This served as the basis for data labeling. Based on simulation result, the CNN algorithm achieved 99.45% accuracy rate for all three channels. Other ML results were LR (88.42%), SVM (57.03%), NB (78.87%), and KNN (94.20%). So, CNN outperformed other ML algorithms for Vibration dataset.
## 1 Introduction
Besides 'raw' satellite data, also different, often project-based, internet portals like GlobSnow, CryoLand, Google Earth Engine, or EUMETSAT H-SAF are providing already processed satellite-based snow parameter products. However, in general, all remote sensing products are often not available in high-temporal resolution or may lack the required spatial resolution. This is especially the case for optical images, e.g., from MODIS, which face potential cloud cover issues [16; 17; 18]. Active microwave products are often restricted due to foreshortening or layover effects, in particular in mountain regions and passive microwave products are very coarse regarding their spatial resolution. Recent approaches tend to applymore and more multi-sensor techniques to overcome some of these limits, which is, e.g., currently a big aim of the NASA SnowEx campaign [19]. Additionally, the combination of EO and hydrological model approaches helps to increase the temporal and spatial availability of snow or run-off information as e.g., presented by [PERSON] et al. [20] and [PERSON] et al. [21]. In the current study, we present a comprehensive overview of a combined approach on using in situ measurements, EO, and hydrological modelling to derive continuous information on snow parameters and run-off. The applied methods and sensors of the in situ component as well as hydrological services were designed, developed and demonstrated in the framework of the business applications demonstration project SnowSense (2015-2018), which was co-funded by the European Space Agency (ESA). The SnowSense service mainly targets snow hydrological applications and is based on three pillars, including (i) a newly developed SnowSense in situ snow monitoring stations based on GNSS signals, (ii) EO products of the snow cover extent and information if and where the snow is dry or wet, and (iii) an integrated physically-based hydrological model. The in situ and EO information are used to assimilate the input and the parameters of the applied hydrological model PROMET (Processes of Mass and Energy Transfer) [22] to calculate SWE, snowmelt onset, and river run-off in catchments as spatial layers. Those data layers contain the relevant information for flood forecasts and hydropower plant management, particularly for so far non- or sparsely equipped catchments in remote areas. Within the project demonstration phase, we validated the GNSS in situ snow stations and the first run-off results of the combined approach were already provided as an operational service for a commercial hydropower plant company and the administration of the island of Newfoundland, Canada, being our first demo users.
## 3 Experimental Results and Analysis
### Layout of the CPFRA  
For verification, one broadband circularly polarized FRA prototype of 144 elements was designed based on the above architecture of the CPFRA for the satellite communication application in the millimeter-wave band. The FRA consists of 144 elements with an aperture area of 40.6 \(\times\) 40.6\(\pi\) mm\({}^{2}\) (12.3\(\pi\lambda\)\({}_{0}\)\({}^{2}\)). A linearly polarized pyramid horn with an aperture of 14 \(\times\) 20 mm\({}^{2}\) and a height of 17 mm is adopted as the feeding source, which is installed on the center. In order to take into account a better aperture efficiency, the distance between the primary reflector surface and the sub-reflector surface is 32.5 mm, which corresponds to a focal length (\(F\)) of 65 mm. The simulated \(-\)10 dB beam-width of the horn radiation pattern is 64\({}^{\circ}\). Once the layout of the CPFRA is ready, the phase compensation of each element on the primary reflector surface can be determined accordingly.
# An Emotion-Based Rating System for Books Using Sentiment Analysis and Machine Learning in the Cloud
## 4 Results and Discussion
With a specificity of 96, 95, 95, 95, 96, 94, 94, 95, 96, and 95 percent for each dataset, CNN-LSTM surpassed all other sentiment analysis techniques in the comparison. _Geometric-mean (G-mean)_ A single number that strikes a balance between sensitivity and specificity makes up this metric [35]. \[G-mean=\sqrt{(specificity\times sensitivity)} \tag{32}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database Name** & \multicolumn{5}{c}{**Sensitivity**} \\ \cline{2-7}  & **SVM** & **CNN** & **ANN** & **LSTM** & **BI-LSTM** & **CNN-LSTM** \\ \hline Stanford—Treebank & 85 & 87 & 85 & 84 & 86 & 94 \\ SemEval Tasks & 86 & 88 & 85 & 85 & 85 & 92 \\ Thai fairy tales & 87 & 86 & 89 & 82 & 85 & 93 \\ SS-Tweet & 87 & 85 & 87 & 83 & 86 & 91 \\ EmoBank & 89 & 84 & 86 & 84 & 87 & 92 \\ IS EAR & 87 & 87 & 84 & 85 & 84 & 93 \\ Alm gold & 87 & 89 & 89 & 82 & 85 & 91 \\ EmoTex & 85 & 87 & 87 & 81 & 86 & 93 \\ Text Affect & 86 & 89 & 88 & 86 & 87 & 94 \\ Neverauskaya & 84 & 86 & 89 & 84 & 85 & 92 \\ Aman’s & 85 & 87 & 86 & 85 & 84 & 93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance analysis for BOW + IF-IDF with CNN-LSTM based on sensitivity. Figure 13: Performance analysis for BOW + IF-IDF with CNN-LSTM based on sensitivity. (“Stanford Sentiment...” means “Stanford Sentiment Treebank”). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database Name** & \multicolumn{5}{c}{**Specificity**} \\ \cline{2-7}  & **SVM** & **CNN** & **ANN** & **LSTM** & **BI-LSTM** & **CNN-LSTM** \\ \hline Stanford—Treebank & 82 & 86 & 85 & 85 & 87 & 96 \\ SemEval Tasks & 85 & 87 & 84 & 84 & 85 & 95 \\ Thai fairy tales & 84 & 85 & 84 & 86 & 86 & 96 \\ SS-Tweet & 86 & 84 & 85 & 82 & 87 & 95 \\ EmoBank & 85 & 86 & 84 & 84 & 88 & 96 \\ IS EAR & 82 & 86 & 86 & 85 & 85 & 94 \\ Alm gold & 83 & 85 & 83 & 86 & 84 & 96 \\ EmoTex & 84 & 84 & 84 & 84 & 86 & 94 \\ Text Affect & 85 & 85 & 85 & 82 & 87 & 95 \\ Neviarouskaya & 84 & 82 & 86 & 83 & 85 & 96 \\ Aman’s & 86 & 87 & 85 & 86 & 86 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance analysis for BOW + IF-IDF with CNN-LSTM based on specificity. Table 10 displays the overall G-mean performance of various sentiment analysis methods used for multiple datasets. In terms of G-mean, which was 0.916, 0.952, 0.962, 0.957, 0.956, 0.959, 0.958, 0.954, 0.956, 0.957, and 0.949 for each dataset, CNN-LSTM performed better than any other sentiment analysis techniques.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 4 Calculation Procedure of Main Circuit Parameters
### Basic Factors
#### 4.1.2 Control Modes  
The LCC at the rectifier side adopts the constant current control and the CCC at the inverter side adopts the constant voltage control. The on-load converter transformers adopt the angle control mode.  
The tap control is one of the main control modes to adjust the DC voltage during steady-state operation. There are usually two control modes. One is the voltage control mode, in which the DC voltage or the AC voltage at the valve side is constant. Another is the angle control mode, which keeps the firing angle or the extinction angle within the given range. These two control modes both have applications in practical engineering. The latter requires a wider range of tap changers but can maintain a high power factor under various operating conditions. The angle control mode is the preferred operation control mode for HVDC transmission systems [27] and is adopted in this paper.
## 3 Vision System for Therapeutic Adaptation: Analysis of Functional Requirements
### Functional Requirements of the Suggestion Module  
The suggestion module is an essential component of the system, designed to analyze the patient's progress, interpret the collected data, and generate personalized recommendations to dynamically adapt the therapy parameters, and unlike traditional methods, which require manual interaction with the therapist to change the treatment settings, this module uses machine learning algorithms and statistical analysis to provide suggestions in real time, improving the effectiveness and personalization of rehabilitation.  
The suggestion module receives the data processed by the vision module through ROS2 topics, which transmit information on the patient's biomechanical parameters. If the module detects an improvement in the patient's movements, it suggests progressively reducing the motor assistance and increasing the difficulty level of the exercises to keep the therapy stimulating, while if the patient shows difficulty or a regression, the module can suggest increasing motor support, reducing exercise difficulty, or sending an alert to the therapist to evaluate any changes to the therapy.  
To make the system more adaptive, the suggestion module will use machine learning algorithms, including the following.  
* Reinforcement Learning: The system progressively learns from the data collected during rehabilitation sessions, optimizing decision thresholds to improve the precision of recommendations.
* Dynamic threshold analysis: The system automatically adjusts the levels of difficulty and motor assistance, avoiding sudden changes and ensuring gradual adaptation to therapy.  
However, the suggestion module does not autonomously set the therapeutic parameters, but simply provides suggestions, which the therapist can evaluate; this ensures that each decision remains under human control, ensuring a balance between automation and medical supervision.  
The suggestion module has been tested through simulations in the ROS2 environment, using synthetic data to verify the consistency of the recommendations generated. The results have shown that the system is capable of accurately analyzing patient biomechanical data and generates consistent suggestions, interfaces in real time with the vision module, receives data without significant latencies, and provides reliable recommendations, with an error margin of less than 5% compared to predefined parameters.
## 4 Simulation and Analysis
### Simulation Setup  
The simulation is divided into two parts. One is in a stationary WSN, and the other is in a dynamic WSN. In the stationary WSN, the position of each sensor node, including sink nodes, is fixed, meaning that sensors cannot move, and the WSN is closed, which means that no new sensors will join the network. On the other hand, in the dynamic WSN, each sensor node, involving sink nodes, has a pre-defined probability to move, and the WSN is open, which means that at each time slot, with a pre-determined probability, a new sensor may join the network. Performance is measured by the four quantitative metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage. The meanings of the four metrics can be easily deduced from their names. For packet delivery, it is supposed that a packet is transmitted and then received in one time slot. The simulation is operated in a simulated 1000 m \(\times\) 1000 m area, and \(200\) sensor nodes and five sink nodes are deployed randomly in this area. The communication radius, that is the furthest delivery radius, of each sensor is 60 m, and the sensing coverage radius of each sensor is 40 m. The initial energy of each sensor is set to 5 J. The average size of a packet is \(50\) bytes, and the actual size of a packet is based on the normal distribution with variance equal to \(10\). At each time slot, each node generates a packet based on a pre-defined probability, and the expiry time of a packet is based on exponential distributions. If a packet cannot reach a sink before its expiry time, the delivery of this packet is considered as a failure. Undelivered packets are also accounted for in the average delivery latency computation. The values and meanings of those parameters mentioned in the previous section, are listed in Table 1. These values were chosen experimentally to provide the best results. Each simulation was executed \(200\)-times, and the average results are displayed in the following figures. We used JAVA to build the simulation platform, where two two-dimensional arrays were used to represent the two layers of a sensor network and another two-dimensional array was defined to indicate the distance between two neighbouring sensors. 'Sensor' was programmed as a class, and each sensor is an object of this class.
## 2 Theoretical Background
### Types of Concept Drifts Incorporated in DRSs
This may occur due to many factors such as the vicinity, new experience, quality of service or user's dynamic preferences [36; 37]. For example, contrary to the usual check-in behavior, the same user may tend to visit a new restaurant where he had a pleasant experience during his last visit. I. _Dynamic social relation_. Dynamic social relationship refers to the relationships, comprises of friends, family, companion or colleagues among users whose influence can change over time [36; 38; 39; 40]. According to [39], users' dynamic social relationships contain many factors, which have a huge impact on delivering satisfactory recommendations. In many cases, social relationships are not based on common interests. For example, in a social network-based RS, a user may follow another user because they are colleagues at a time and followed a user who is most famous in a certain field at another time. J. _Dynamic user intent_. Dynamic influences of users' intentions and demands are other important drifting factors that undermine the accuracy of recommendation [41; 42]. In most cases, user intentions exhibit sequential patterns and tend to drift from time to time. In user's check-in scenarios, for instance, which follows a dynamic user preference along with temporal patterns of weekly, monthly, and seasonal changes, it will be challenging to anticipate a user's next destination due to the complexity in capturing periodic patterns [43]. The DRS systems excerpt their essential benefits by incorporating the methods for handling concept drift in data stream environments [44; 45; 43]. According to [46], there are two key approaches commonly used to handle concept drifts in any stream environments, namely, active and passive approaches. * **The passive approach:** the methods based on this approach continuously update the model over time without the need for an explicit drift detection procedure [46]. Example of passive learners include forgetting mechanisms [23; 46], weighting schemes [47; 48; 49; 50], window-based [51] and ensemble methods [37]. * **Active approach** the active methods explicitly detect the concept drift and then update the model according to change rates. In other words, the active approaches typically work by employing change detection modules such as Drift Detection Method (DDM) [21], Early Drift Detection Method (EDDM) [52], Max-Margin Early Event Detectors (MMED) [53], Adaptive Windowing (ADWIN) [54], among others. From our review, it was noticed that the later approaches, i.e., active approaches, are rarely exploited for concept drift in RS. Therefore, in this study, we focused mainly on the passive approaches on which the exiting temporal models are based, as detailed in the next subsection.
## 4 Results and Discussion
### Performance Comparisons Using the Evaluation Metrics

Table 4: _HbA1c_ estimation performance comparison between this study and the previous study. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Zone**} & \multirow{2}{*}{**A**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{**C**} \\  & & & & \\ Blood vessel [14] & \(15\) (75) & \(5\) (25) & \(0\) (0) \\ Whole finger [14] & \(18\) (90) & \(2\) (10) & \(0\) (0) \\ Blood vessel (Proposed1) & \(15\) (75) & \(5\) (25) & \(0\) (0) \\ Whole finger (Proposed1) & \(17\) (85) & \(3\) (15) & \(0\) (0) \\ Blood vessel (Proposed2) & \(16\) (80) & \(4\) (20) & \(0\) (0) \\ Whole finger (Proposed2) & \(17\) (85) & \(3\) (15) & \(0\) (0) \\ \hline \end{tabular}  
* Proposed1: using ratio calibrations only; Proposed2: using both ratio calibrations and value (_HbA1c_) calibrations.  
\end{table} 
Table 5: EGA-based comparison between this study and the previous study. The \(SpO_{2}\) estimation performance comparison using the evaluation metrics is summarized in Table 7. We can see from Table 7 that the RCF score of \(SpO_{2}\) estimated in this study was almost the same as that of the previous study (whole finger). Considering other statistical analyses, we can see that the overall performance of the \(SpO_{2}\) estimation in this study was slightly better compared to that of the previous study [14]. Table 8 summarizes the comparisons based on the Bland-Altman analysis between the previous study [14] and this study in estimating \(SpO_{2}\). From this, it is clear that the proposed method performed well in comparison with the previous study.