esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study examined the BaZrO\({}_{3}\):Eu\({}_{0,025}\) phosphors prepared by the solid-state reaction method and BaCO\({}_{3}\) + ZrO\({}_{2}\) + 0.0125 Eu\({}_{2}\)O\({}_{3}\) precursors to investigate their photoluminescence properties. From the XRD patterns, as the same heating temperature was used, the diffraction intensity of the (110) peak of the two-step Eu-BZO powders was larger than that of the one-step Eu-BZO powders. The FWHM values of the (002) diffraction peak of the two-step Eu-BZO powders was smaller than that of the one-step Eu-BZO powders. The emission spectrum of the Eu-BZO powders consisted of sharp peaks of two strong bands corresponding to the \({}^{5}\)D\({}_{0}\)-\({}^{7}\)F\({}_{0}\) (574 nm) and \({}^{5}\)D\({}_{0}\)-\({}^{7}\)F\({}_{1}\) (596 nm) transitions of Eu\({}^{3+}\) ions, and of two weak bands corresponding to the \({}^{5}\)D\({}_{0}\)-\({}^{7}\)F\({}_{2}\) (620 nm) and \({}^{5}\)D\({}_{0}\)-\({}^{7}\)F\({}_{3}\) (650 nm)  
Figure 8: Diagrams for the decay rates of BaZrO\({}_{3}\):Eu\({}_{0,025}\) phosphors. (**a**) With same quantity \(N\) and different decay time \(\tau_{i}\); (**b**) With different quantity \(N_{i}\) and same decay time \(\tau\).  
transitions of Eu\({}^{3+}\) ions, respectively. The one-step the two-step Eu-BZO phosphors exhibited decay lifetimes extending from 2.52 ms to 3.98 ms and from 3.78 ms to 6.02 ms, respectively. As the heating temperature was equal to and higher than 1300 \({}^{\circ}\)C, the one-step Eu-BZO phosphors presented a reddish-orange emission. For two-step Eu-BZO powders, as the heating temperature increased from 1100 \({}^{\circ}\)C to 1400 \({}^{\circ}\)C, the dominant transition changed from \({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{1}\) to \({}^{5}\)D\({}_{0}\)\(\rightarrow\)\({}^{7}\)F\({}_{0}\) and the emission changed from a reddish-orange to orange color. In this study, we prove that Eu-BZO phosphors with very bright emission intensity can easily be obtained by heating oxide precursors.  
The authors acknowledge financial supports of MOST 104-2221-E-390-013-MY2 and MOST 104-2622-E-390-004-CC3. [PERSON] and [PERSON] helped proceeding the experimental processes, measurements, and data analysis; [PERSON] helped proceeding the experimental processes and measurements; [PERSON] and [PERSON] organized the paper and encouraged the paper writing.  
The authors declare no conflict of interest.
## 4 ME-SSA Receiver Design
### Preamble Searcher  
The aim of the PS is to detect the presence of one or multiple bursts within the current search window (stored in a local memory at the receiver). This goal is achieved by cross-correlating the received signal with a local replica of the spread preamble, over a time-frequency grid, i.e., formulating a certain number of time and frequency hypotheses at which a burst could be received [14]. If, for a given pair of time-frequency hypotheses, the correlation value is above a given threshold, the burst detection is positive and the PS returns the associated time-frequency hypothesis for a subsequent demodulation attempt.  
A trade-off between computational complexity and performance has been undertaken in order to identify the best PS algorithm depending on the scenario, leading to the following conclusions:  
* A coherent PS strategy, where the coherent integration time is equivalent to the preamble duration, is sufficient in the case of a GEO satellite,
* A non-coherent PS strategy is instead needed for LEO or MEO scenarios where the Doppler effect has to be taken into account. Here, the preamble is divided into \(N_{\mathrm{c}}\) contiguous segments, where coherent integration is carried out on each segment. Then, the resulting \(N_{\mathrm{c}}\) values are combined, according to the differential post detection integration (DPDI) approach [14].
## 4 Data-Driven CIM Construction
### Example of Data-Driven CIM Construction
#### 4.3.1 Intelligent Transportation System  
Intelligent transportation systems (ITSs) utilize advanced technologies such as AI, big data analytics, and the IoT to monitor, manage, and optimize urban transportation networks effectively. The integration of CIM into an ITS brings significant improvements in three key areas: the seamless integration of traffic data, dynamic optimization of traffic signal control, and predictive analytics for traffic incident management.  
CIM aggregates and integrates data from a variety of sources, including traffic surveillance cameras, vehicle sensors, GPS systems, and traffic signal infrastructures [64; 65]. This heterogeneous data are processed in real time, offering traffic management authorities real-time insights into traffic flow, road conditions, and incidents. For example, in London, CIM integrates traffic cameras with GPS data from public transport, enabling the system to provide live updates on the traffic conditions and optimize the routing for buses during peak hours. Dynamic Optimization of Traffic Signals: CIM dynamically adjusts traffic signal timings based on real-time data to alleviate congestion. During high-traffic periods, such as rush hours, the system can extend the green light durations to ease bottlenecks. Conversely, during off-peak hours, the system shortens the green light phases to optimize the traffic flow [65]. In Berlin, CIM has been implemented to adjust the traffic signal timings dynamically, improving the flow during rush hours by 20% and reducing the wait times at intersections. Predictive analytics for traffic incident management: CIM plays a crucial role in traffic incident prediction and emergency management. By analyzing historical traffic data alongside real-time flow analytics, CIM identifies high-risk zones and predicts incidents before they happen. For example, in Tokyo, CIM leverages historical accident data and real-time traffic conditions to predict accident hotspots and issue early warnings.  
Figure 4: Example of data-driven CIM construction.  
This allows traffic management teams to deploy emergency resources promptly and reduce the impact of accidents on the traffic flow.  
Singapore's intelligent transportation system: Singapore serves as a notable example of CIM in action. The city uses CIM to optimize the traffic signal timings, predict traffic bottlenecks, and improve the overall traffic conditions. By dynamically adjusting the traffic signals based on live data, Singapore has reduced the congestion during peak hours by 30%, significantly enhancing the urban mobility and efficiency.  
In conclusion, CIM's integration into ITSs enables real-time traffic monitoring, intelligent decision making, and optimized traffic flow. This integration not only improves the traffic management but also contributes to a more sustainable and efficient urban transportation system, benefiting both residents and urban planners.
## 4 Trust Assessment Using Various Approaches
### Reasoning Methods  
**Subjective Logic:** Subjective logic offers a mathematical framework for evaluating trust in 6G networks, enabling the modeling and fusion of uncertain and subjective opinions [74; 75]. This approach facilitates a nuanced and context-aware evaluation by incorporating subjective assessments from various sources, including network nodes, devices, and users [76]. By considering qualitative and uncertain factors such as user experiences, environmental conditions, and dynamic network behaviors, subjective logic provides a comprehensive understanding of trust levels within the network [77]. Moreover, subjective logic allows for the adaptation and learning of trust evaluations over time, enabling dynamic adjustments based on evolving subjective assessments [78].  
However, the utilization of subjective logic for trust evaluation in 6G networks presents certain challenges. These include difficulties in interpreting and processing subjective assessments, the necessity to establish standardized frameworks for capturing and integrating subjective opinions, and the dynamic nature of subjective assessments [76; 78]. Therefore, careful consideration of its implementation, interpretation, and processing is crucial to harness its full potential in enhancing trust and security in 6G communication systems.  
**Bayesian Methods:** Bayesian algorithms offer another approach to evaluating trust levels in 6G networks, leveraging Bayesian networks (Bnets) to model the integration of communications, navigation, sensors, and services [79]. Through Bayesian networks, complex relationships within the network, including entity relationships, can be mathematically and graphically represented [80]. This methodology is particularly suitable for assessing trust in complex 6G networks, as it aids in mitigating malicious behavior, enhancing security measures, and establishing reliable communication channels [81].  
The Bayesian approach facilitates indirect trust calculation based on the uncertainty of direct trust, effectively excluding malicious feedback and providing a robust foundation for trust evaluation in 6G networks [80; 81]. By incorporating probabilistic reasoning, Bayesian methods offer a systematic and principled framework for trust assessment, contributing to the overall reliability and security of 6G communication systems.
## 4 Results and Analysis
### Extraction Results of Building Information
#### 4.1.2 Extraction Results of Building Function  
In earthquake insurance, buildings are classified into four types of use: residential, industrial, commercial, and public services. The AOI and POI were reclassified into four building functions [26], and the results are shown in Figure 9. AOI data was used as a priority. The spatial overlay of the AOI surface and the building footprint was used to obtain some building functions, and the recognition rate was about 81%. Then, POI data was used as a supplement to identify the functions of buildings through spatial overlay such as polygons and kernel density analysis. The kernel density surface of POI points was obtained, as shown in Figure 10. The building functions were assigned according to the kernel density thresholds. The kernel density thresholds for residential buildings, commercial buildings and public buildings are 1,000,000, 8,000,000, and 2,000,000, respectively. It should be noted that industrial buildings are generally in the form of industrial zones, and their building functions are mainly determined by the AOI. Finally, the use function distribution of buildings in urban areas of Tangshan City is shown in Figure 11. From the figure, it can be seen that commercial buildings are mainly distributed on both sides of the street or at corner intersections in a point or strip distribution, which is in line with the accessibility, convenience, and visibility of urban commercial facilities. Residential buildings are clustered and distributed, which is consistent with the characteristics of residential space. Public service buildings are scattered in a dotted pattern, in line with the public nature and openness of public buildings. Industrial buildings are mainly distributed at the boundary of the urban area, far away from civil buildings, which helps to protect the urban ecological environment and reduce pollution to residential areas. Additionally, industrial areas gather with each other to meet their collaboration and transportation needs. From the results of the distribution of building functions, the buildings in the urban area are generally distributed in a mixed state, and each functional building is intricately connected with each other.  
Figure 8: The distribution of buildings in Tangshan urban area.  
Figure 11: Distribution map of building function in Tangshan urban area.  
Figure 10: Example of the kernel density and building function results. (**a**) Kernel density of public service buildings; (**b**) kernel density of commercial buildings; (**c**) kernel density of residential buildings; (**d**) example of extraction results of building functions.  
Figure 9: POI data and AOI data display.
## 3 Results  
In order to verify the effectiveness and feasibility of the proposed neural network-based adaptive PID (RBFNNPID) algorithm for DO concentration control of the activated sludge wastewater treatment process, comparison simulation of RBFNNPID and traditional PID are designed in this section, including tracking performance and anti- disturbance performance.  
We have selected the BSM1 as the simulation model and the dry weather wastewater data provided by IWA as the source data. The dry weather data contains two weeks long actual operational data of a wastewater treatment system, sampled at every 15 min. Figure 5 shows the dynamic influent data and the changes to the concentrations of some of the components between days 7 and 14.  
Figure 5: Dynamic data for experimental use: (**a**) Influent flow rate (days 7 to 14); (**b**) Concentration of some of the chemical species in the influent (days 7 to 14) (\(S_{\text{S}}\)—readily biodegradable substrate; \(S_{\text{NH}}\)—ammonium and ammonia nitrogen; \(S_{\text{ND}}\)—soluble biodegradable organic nitrogen).  
In the simulation, the three parameters of both the traditional incremental PID algorithm and the proposed RBFNNPID algorithm are set as: \(k_{p}=5\), \(k_{i}=1\), \(k_{d}=0.5\); the learning rate, \(\eta\) of the three parameters of PID is 0.2; the momentum factor \(\alpha\) is 0.05; the network sampling period is 0.001 s; the structure of the RBFNN is defined as \"3-6-1\", that is to say, the input layer has three nodes, the hidden layer has six nodes and the output layer has one node.
## 3 Disturbance-Rejection Controller Design Based On Linear Extended State Observer (LESO)
### LESO Design  
The disturbance acting on the system cannot be measured directly, so the extended states of the system are introduced to estimate the disturbance, and we assume that the disturbance is a constant or is a slowly changing variable generated by a linear system, so we design a corresponding linear extended state system to estimate the disturbance. This system is the LESO.  
Assume the change rate of disturbance \(d^{\prime}\) is \(d^{\prime}=h(t)\), where \(h(t)\) is the first-order derivative of \(d^{\prime}\), so the system can be written as:  
\[\dot{\omega} = J^{-1}v+d^{\prime} \tag{16}\] \[\dot{d}^{\prime} = h(t) \tag{17}\]Based on system models (16) and (17), we designed a second-order disturbance observer. The second-order LESO system is  
\[\dot{z}_{1} = J^{-1}v+z_{2}+l_{1}(\tilde{\omega}-z_{1}) \tag{18}\] \[\dot{z}_{2} = l_{2}(\tilde{\omega}-z_{1}) \tag{19}\]  
where \(z_{1},z_{2}\) are the corresponding states of \(\tilde{\omega},d^{\prime}\), and \(l_{1},l_{2}\) are positive constants. Insert Equation (14) into (18), and the Laplace transform of LESO is  
\[sz_{1} = (z_{2}-d^{\prime})+s\tilde{\omega}+l_{1}(\tilde{\omega}z_{1}) \tag{20}\] \[sz_{2} = l_{2}(\tilde{\omega}-z_{1}) \tag{21}\]  
as a characteristic polynomial. We can get the transform function of \(d^{\prime}\) and \(z_{2}\) as  
\[z_{2}=\frac{l_{2}}{s^{2}+l_{1}s+l_{2}}d^{\prime} \tag{22}\]  
We needed to design \(l_{1},l_{2}\) to ensure \(z_{2}\) tends to \(d^{\prime}\). According to the Routh-Hurwitz stability criterion, \(s^{2}+l_{1}s+l_{2}\) should be Hurwitz. However, there are countless solutions in the parameter space of \(l_{1},l_{2}\). Considering response time and overshoot, we considered the critical damping of the second-order system, which had damping coefficient 1, and we selected \(l_{1}=2\omega_{o},l_{2}=\omega_{o}^{2},\omega_{o}>0\), which balanced the response time and overshoot, and the characteristic polynomial of Equation (22) is Hurwitz. There is only one parameter \(\omega_{o}\) for tuning, which simplifies the design, tuning, and implementation.  
Figure 3: Control scheme. LESO: Linear Extended State Observer.  
The estimate error is  
\[\begin{split}\tilde{d}^{\prime}&=d^{\prime}-z_{2}\\ &=d^{\prime}-\frac{l_{2}}{s^{2}+l_{1}s+l_{2}}d^{\prime}\\ &=\frac{s^{2}+l_{1}s}{s^{2}+l_{1}s+l_{2}}d^{\prime}\end{split} \tag{23}\]
# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 4 Experiments and Analysis
### Downstream Task Evaluation  
As compressed remote sensing images are typically used for downstream tasks, e.g., classification and object detection, we investigated accuracy in classification tasks by applying the KNN method [47] on compressed images and the JSCIFI-F method on ground-truth images. The results are shown in Figure 11. It can be seen that the proposed compression method does not degrade the performance on the classification task.  
Figure 11: Visual results of HIS classification task.  
Figure 10: Visual results using different methods on Indian Pine dataset [32; 34; 41].
## 3 Effect of Surfactants on Mechanico-Thermal Properties
### Effect of Anionic Surfactants  
Table 7 shows the mechanico-thermal properties of polymer nanocomposites modified by anionic surfactants. SA (chemical structure showed in Figure 3a) could modify inorganic nanofillers like MMT and modified MMTs have been used for the preparation of MMT/PP nanocomposites [2]. The modification of MMT improved the mechanico-thermal properties of the nanocomposites. The mechanical properties, such as tensile strength, elongation at break, and impact strength, of the nanocomposites increased by up to 5.3%, 125%, and 50%, respectively. This is due to the dispersion state of SA-MMT being very good [2]. In contrast, the Young's modulus of the nanocomposites decreased because the tougher the composites, the less stiff their character. The melting temperature of the nanocomposites slightly increased by up to 1.0% due to the strengthening of mechanical properties of the nanocomposites. However, the crystallization temperature of the nanocomposites decreased compared to the nanocomposite without SA because of the very good dispersion of SA-MMT in the nanocomposites [2]. OA (chemical structure showed in Figure 3b) could modify inorganic nanofillers, such as TiO\({}_{2}\) NRs, to prevent the agglomeration of TiO\({}_{2}\) NRs and modified TiO\({}_{2}\) NRs have been utilized for the preparation of TiO\({}_{2}\) NR/PS nanocomposites [3]. The modification of TiO\({}_{2}\) NRs improved the loss modulus of the nanocomposites by up to 22% compared to neat PS. This is attributed to the PS chains becoming softer due to the presence of OA-modified TiO\({}_{2}\). [3]. Moreover, the glass transition temperature of the nanocomposite decreased because of the soft PS chains caused by the OA molecules. In addition, the storage modulus of the nanocomposite decreased due to the plasticization effect of OA, which is present at the TiO\({}_{2}\) NR surface [3]. Nevertheless, the maximum degradation temperature of the nanocomposites increased by up to 1.3% because the thermal stability of the TiO\({}_{2}\) NRs was more than 400 \({}^{\circ}\)C.  
On the other hand, PA (chemical structure showed in Figure 3c) could modify inorganic nanofillers like LDHs and modified LDHs have been applied for the preparation of LDH/PCL nanocomposites [22]. The modification of LDHs improved the mechanico-thermal properties of the nanocomposites, such as tensile modulus, impact strength, storage modulus, and crystallization temperature. The tensile modulus and impact strength properties increased by up to 12% and 65%, respectively, compared to the neat PCL, which was due to the reinforcement property of the PA-modified LDHs (PA-LDHs), and they could also act as an impact strength modifier in the nanocomposite system. Moreover, the storage
# Immobilization of Pectinolytic Enzymes on Nylon 6/6 Carriers
## 3 Results and Discussion  
### Comparison of Different Methods for the Assessment of Pectinase Activity  
There are several methods for the determination of pectinase hydrolytic activity. In many studies, it has been assessed by measuring the amount of reducing sugars released during the reaction by the 3,5-dinitrosalicylic acid (DNS) method [15; 33; 34]. However, this method is not specific to galacturonic acid as it is sensitive to other reducing sugars like glucose and galactose as well. Other methods for the determination of the produced galacturonic acid are iodometric titration method [32] and spectrophotometric method based on copper-reducing reaction [35]. We evaluated the latter two methods for the determination of pectinase and polygalacturonase activities expressed in international units U (\(\mu\)mol galacturonic acid released per one minute). The results for Pectinex Ultra SPL preparation as an example are shown in Table 1.  
Compared to pectinase activity declared by the producer (\(\geq\)3800 U/mL), the activity determined was considerably lower (\(\geq\)3 times) with both methods used. Compared to the titration assay, the spectrophotometric method indicated approximately 20% lower estimations of both pectinase and polygalacturonase activity. As the relative activities are similar, we selected the spectrophotometric method for the determination of the activity of immobilized enzymes in the current study because the assessment procedure was less time-consuming and the experimental errors were smaller. In addition, this method is selective for uronic acids over neutral sugars like glucose, galactose, and r  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Pectinase Activity** & **Polygalacturonase Activity** \\  & **(U */mL Enzyme)** & **(U/mL Enzyme)** \\ \hline Iodometric titration & 1333 \(\pm\) 249 ** & 2133 \(\pm\) 94 \\ \hline Spectrophotometric method & 1065 \(\pm\) 94 & 1732 \(\pm\) 97 \\ \hline \hline \end{tabular}  
* U is defined as 1 \(\mu\)mol galacturonic acid released per 1 min [36]. ** All values are expressed as mean \(\pm\) standard deviation (n = 3).  
\end{table}
Table 1: Pectinase and polygalacturonase activities of Pectinex Ultra SPL preparation determined using titration and spectrophotometric methods.  
Figure 1: Nylon threads coiled around Teflon rings.
# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
## 4 Experiments and Results
### Ablation Experiments
#### 4.2.2 The Effectiveness of the Improved Coarse-Grained Multi-Scale Feature Extraction Method
Comparing Figure 12 with Figure 11b, it can be seen that the accuracy of the unimproved ordinary time-frequency domain feature extraction model tends to be stable after 1000 iterations, and the accuracy of the improved multi-scale time-frequency domain feature extraction model tends to be stable after 800 iterations. Comparing Figure 13 with Figure 12b, it can be seen that for fault category 2, the model can make a correct diagnosis. For fault category 1, the model based on ordinary time-frequency domain feature extraction makes three diagnostic errors, and the model based on improved multi-scale feature extraction makes two diagnostic errors. For fault category 3, the model based on ordinary time-frequency domain feature extraction makes one diagnostic error, and there is no error after using the improved multi-scale feature extraction. For fault category 4, both methods make three diagnostic errors. For fault category 5, the model based on ordinary time-frequency domain feature extraction makes six diagnostic errors, and the model based on multi-scale feature extraction makes four diagnostic errors. The final fault identification accuracy of the two methods is 94.8% and 96.4%, respectively. By comparing Figure 14 with Figure 13b, it can be seen that the improved multi-scale time-frequency domain feature extraction model has a larger inter-class distance and smaller intra-class distance, which does not achieve the desired effect.