esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Critical Metrics for Industrial Monitoring and Control
### Real Time Capability  
Based on the criticality and importance of the applications, the International Society of Automation (ISA) considers six classes of wireless communication, from critical control to monitoring applications, in which the importance of the message response time and Quality of Service (QoS) requirements varies [12]. In the more critical applications, process values need to be transmitted to the destination in a reliable, timely and accurate manner. The details of the classes are shown in Table 2.  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Category** & **Class** & **Application** & **Description** \\ \hline \multirow{3}{*}{Safety} & \multirow{3}{*}{0} & \multirow{3}{*}{Emergency action} & Always critical \\  & & & e.g., Instrumented protective \\  & & & systems/Safeguarding systems \\ \hline \multirow{6}{*}{Control} & \multirow{3}{*}{2} & Closed-loop regulatory control & Often critical \\  & & & e.g., Regular control loops \\ \cline{1-1} \cline{2-4}  & \multirow{3}{*}{1} & Closed-loop supervisory control & Usually noncritical \\ \cline{1-1} \cline{2-4}  & & & e.g., Set point manipulation for control \\ \cline{1-1} \cline{2-4}  & \multirow{3}{*}{3} & \multirow{3}{*}{Open-loop control} & Human in loop \\ \cline{1-1} \cline{2-4}  & & & e.g., Manual human actions on alerts \\ \hline \multirow{6}{*}{Monitoring} & \multirow{3}{*}{4} & \multirow{3}{*}{Alerting} & Short-term operational consequence \\  & & & e.g., Short term operational \\ \cline{1-1} \cline{2-4}  & & & consequence, event-based maintenance \\ \cline{1-1} \cline{2-4}  & \multirow{3}{*}{5} & Logging and & No immediate operational consequence \\ \cline{1-1} \cline{2-4}  & & downloading/uploading & e.g. history collection, sequence-of- \\ \cline{1-1} \cline{2-4}  & & & events, preventive maintenance \\ \hline \end{tabular}
\end{table}
Table 2: Different classes of applications as defined by ISA.  
While ISA100.11a supports industrial applications from class 1 to 5, WirelessHART supports industrial applications ranging from class 2 to 5 [12]. ZigBee Pro is designed for applications which have softer real-time requirements [13]. Traditional wireless sensor networks (WSNs) are deployed in class 4-5 applications [12], where low-power consumption is given priority over providing a bounded response time delay. Such WSNs are not suitable for controlling tight control loops as nodes usually spend a large proportion of the time in a low-power sleep state.  
WISA is the only wireless protocol that is suitable for factory automation applications as it can provide some strict real-time guarantees. There are related basic wireless requirements in such applications, for example, low additional latency due to wireless link (e.g., \(<\)10 ms).  
We carry out a more detailed analysis of the real-time capabilities of ZigBee Pro, WirelessHART, WIA-PA, WISA and ISA100.11a later in the paper by discussing specific details relating to the MAC layer contention mechanism and priority management schemes.
## 4 Experimentation
### Experiment Results and Ablation Study
#### 4.4.2 Qualitative Results and Demonstration of Context-Dependency Learning of DMRMs
In contrast, the upper-right image demonstrates the result of our proposed DMRMs after the joint learning technique, showing the successful recognition of \"shoplifting\"--an event that is very similar to the \"normal event of shopping\"--through context-dependency learning. Similarly, in (b), the left-hand image gives a false alarm for an anomalous event given that many cars are passing by. In contrast, in the right-hand image, our framework learned the context of this video as well as the difference between \"car accident\" cases and those of a \"normal road with many cars passing by.\"  
The qualitative analysis of context-dependency learning of DMRMs is demonstrated in Figure 7. For better visualization, the localization of moving objects is avoided in Figure 7. (a)-(h) reflect the result of an anomalous event detection task and context-dependency learning of DMRMs. Events (a), (c), and (e) have background similarities with the events (b), (d), and (f)-(h), respectively. (a) and (b) both are events from shopping marks--(a) is an anomalous event of \"shoplifting,\" while (b) is a \"normal event of shopping.\" Similarly, events (c) and (d) take place near the reception area--(c) is an anomalous event of \"shoplifting at reception,\" while (d) is a \"normal event of some query at reception.\" Furthermore, events (e) and (f) are cases of driving on crossroads--(e) is an anomalous event of \"car accident at crossroads,\" while (f) is a \"normal event of driving at crossroads.\" Additionally, similar to the event (e), events (g) and (h) are also cases of driving on crossroads with different scenarios (much rush on bigger crossroads) and with substantial illumination differences, respectively. Despite all these background similarities, our proposed framework, DMRMs, successfully recognized all the events from (a)-(h), demonstrating the context-dependency learning of DMRMs. Figure 5: Qualitative analysis of the proposed framework, DMRMs, for multiple instances of anomalous events in a single video. The red curve shows the abnormality score of the corresponding frame (ranging between 0 and 1). The light blue box shows the ground truth of the temporal region of anomalous events, while the arrow indicates the corresponding visual frame of the recognized anomalous event along with the localization of moving objects. (**a**,**b**,**d**) are testing results from UCF-Crime testing dataset and (**c**) is from ShanghaiTech testing dataset, where (**d**) is a failure case of our proposed framework, DMRMs. Meanwhile, all successful results in recognizing multiple-instance anomalous events are shown in (**a–c**).
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
## 2 Historical Review
### Light Emitting Diode Literature  
The first LED was developed in 1960. It was not until 1996 that Dr. [PERSON] invented white LED to break through the long-term technical bottleneck. Since then, many LED-related issues have been studied by scholars [13; 14]. For the application of LED, thermal management has always been an important issue. In 2007, [PERSON] [15] studied the technology of the thermal management for LED lamps. In 2012, [PERSON] [16] et al. simulated the thermal model for LED lamps and analyzed the corresponding thermal management. The study used infrared cameras and thermocouples to monitor specific measurement points on LED lamps and driver boards. The experimental results were used to establish and verify the simulation model for the LED lamp. Using models of simplified electronic components and circuit boards, the thermal simulation of driver boards was built. According to the results of the measurements and thermal simulation by [PERSON], the temperature of the LED lamp increased significantly, and the temperature of the LED circuit board was raised at a percentage of 15% with the environmental temperature of 22 \({}^{\circ}\)C. Glass fiber epoxy substrate is a common raw material in printed circuit boards and is made by several materials such as epoxy resin, glass fiber cloth, and copper foil. The PCB (printed circuit board) of the LED lamp was made by pressing, and the model number included G-10, FR-4, FR-5, etc.  
[PERSON] et al. [17] investigated the electrochemical energy storage for graphene in 2015. In 2016, [PERSON] et al. [18] studied the influence of the thermal conductivity on the temperature distribution for surface-mount device (SMD) LEDs of FR4 copper foil substrates, packed with an aluminum housing. The commercial software, FloEFD, was used for the thermal simulation. Meanwhile, the corresponding experiments used the current inputs of 50 and 100 mA for measuring the thermal parameters. The simulation and experimental results by [PERSON] showed that the LED lamp of an aluminum package with a power of 5 W had a lower thermal resistance and junction temperature. In 2017, [PERSON] et al. [19] synthesized a graphene epoxy composite material as a thermal interface material for LED lamps where the thermal performance and junction temperature of LED lamps were measured. The results showed that the thermal conductivity of graphene epoxy composites increased with the increase in the graphene mixing ratio. Taking 10 W power LED lamps as an example, the junction temperature could be reduced from 72 \({}^{\circ}\)C to 67 \({}^{\circ}\)C. In 2017, similar studies were published for the heat conduction of graphene as shown in [12; 20].
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 5 Results
### Metamorphic Rocks  
With reference to the metamorphic group, slates hold the lowest emissivity (mean 0.895), followed by migmatites and granitidis, which are two macroscopically similar rock types (Figure 4). Serpentinite is characterized by a wider emissivity range (0.92-0.95), due to mineralogical heterogeneity of its surface. At the ranking top, marble samples are found, with an emissivity difference based on the polished or smooth surface condition. As for igneous granites, the polished marble sample is characterized by a lower \(\varepsilon\) (0.955 against 0.98 belonging to the smooth one).
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 1 Introduction
Following hardware failure of the SMAP satellite, it was converted to GNSS-R mode (i.e., SMAP-R), where H polarization and V polarization antennas are employed to receive the reflected signals [19; 20]. The European Space Agency plans to launch the HydroGNSS in 2024, the scientific objective of which is the detection of essential climate variables using the dual polarization of the corresponding receiver antennas [21]. In summary, regardless of whether ground-based, airborne, or spaceborne GNSS-R is used, the polarization properties are not definitive. However, given the importance of using GNSS-R polarization in remote sensing of the land surface, corresponding research should be undertaken. Owing to the lack of targeted experiments, research based on the theoretical mechanism model is especially important. Theoretical study can assist in subsequent experimental design, data analysis, terrestrial receiver design, and quantitative inversion of parameters [22]. Therefore, based on our previous work related to GNSS-R polarization, this study employed theoretical models to undertake simulation analysis to promote the scientific development and engineering design of GNSS-R polarization technology. Our developed models are based on the previous backscattering microwave scattering models and they are presented in Section 2. The theoretical simulations are described and analyzed in Section 3. The derived conclusions are presented in Section 4.
## 5 Results
### Quality Ratings  
The average perceived quality of the sonic environment by predetermined _type_ is illustrated in Figure 1. There are parallels between the respective _types_ in the two experiments. Rural parks and Bars were rated as most _calm_, while Shops and Cafes were most _chaotic_. The other _types_ were rated in between; that is, within their respective contexts, Eateries and Urban Parks, and MixFusion and Dining, were perceived as neutral. The plots reveal that the environmental _types_ line up quite close to the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicator** & _Pleasantness_ & _Eventfulness_ & \(L_{\textbf{Aeq}}\) & \(N_{\textbf{10}}\) & \(L_{\textbf{A10}}\)-\(L_{\textbf{A90}}\) & \(N_{\textbf{10}}\)-\(N_{\textbf{90}}\) \\ \hline \(L_{\textbf{Aeq}}\) & \(-0.55\)* & 0.77 *** & - & - & - & - \\ N\({}_{10}\) & \(-0.55\)* & 0.76 *** & 0.99 *** & - & - & - \\ \(L_{\textbf{A10}}\)–\(L_{\textbf{A90}}\) & \(-0.02\) & 0.67 *** & 0.26 & 0.31 & - & - \\ N\({}_{10}\)–\(N_{90}\) & \(-0.31\) & 0.89 *** & 0.79 *** & 0.82 *** & 0.77 *** & - \\ \(L_{\textbf{C-A}}\) & 0.61* & \(-0.38\) & \(-0.25\) & \(-0.22\) & \(-0.097\) & \(-0.20\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: [PERSON]’s coefficient of correlation amount soundscape indicators and descriptors for the 15 restaurant soundscapes in Experiment 2. For explanations, see text. * \(p\leq 0.05\), ** \(p\leq 0.01\), *** \(p\leq 0.001\). *** \(p\leq 0.001\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Experiment 1 (\(n\) = 43)**} & \multicolumn{3}{c}{**Experiment 2 (\(n\) = 45)**} & \multicolumn{3}{c}{**(48; \(n\) = 1813)**} \\ \hline
**Trait** & **Mean** & **SD** & **Range** & **Mean** & **SD** & **Range** & **Mean** & **SD** \\ \hline Extraversion & 4.5 & 1.4 & 1... 6.5 & 3.8 & 1.6 & 1... 7 & 4.4 & 1.5 \\ Agreeableness & 4.9 & 1.0 & 2... 6.5 & 5.1 & 1.1 & 2.5... 7 & 5.2 & 1.1 \\ Conscientiousness & 5 & 1.2 & 2... 7 & 4.8 & 1.3 & 2.5... 7 & 5.4 & 1.3 \\ _Emotional stability_ & 5.1 & 1.1 & 2.5... 7 & 4.9 & 1.3 & 1.5... 7 & 4.8 & 1.4 \\ Openness & 5.4 & 1.1 & 3... 7 & 5.3 & 1.1 & 2.5... 7 & 5.4 & 1.1 \\ _Noise sensitivity_ & - & - & - & \(-4.7\) & 12.5 & \(-29\ldots 26\) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Personality characteristics of participants in Experiment 1 and 2, compared with normative data published by [PERSON] et al. [48].  
calm-chaotic_ axis. This suggests that a single perceptual dimension might be sufficient for these types of soundscapes. See [5] for a discussion of soundscape descriptors, and [44] for an interesting approach that involves functional context of environments. More work is needed to rule out an effect of the sampling method, or that the method of calculating composite scores for _Pleasantness_ and _Eventfulness_ is sub-optimal.
# Development and Application of Intelligent Temperature Control System for Large Aqueduct
## 2 Intelligent Temperature Control System for Large Aqueduct
### Features of Intelligent Temperature Control System  
(1) The system builds the Internet of Things and adopts wireless transmission to dynamically display temperature, cooling water flow and other data in real time combined with BIM technology, which can realize project visualization, strong applicability and a high degree of automation and intelligence.  
(2) The system has a wide range of applications, which can be applied to most hydraulic aqueducts, as well as mass concrete temperature control and crack prevention, and the intelligent data collection can avoid human error.  
(3) The system is highly integrated with equipment, monitoring and control are integrated, and data processing is effective and high-speed.
# Characterization of Thermal Properties of Ruby Chocolate Using DSC, PDSC and TGA Methods
## 3 Results  
### Fatty Acid Composition and Their Positional Distribution  
The fatty acid composition of the RC is in agreement with the expectation for milk chocolate. It can be stated that the main fatty acids are C18:0, C18:1n9c and C16:0 in the proportions of 34.08%, 31.60% and 25.38%, respectively (Table 1). These fatty acids are also present in milk powder, which is used in the production of milk chocolate. In ruby chocolate, short-chain and medium-chain fatty acids like caproic, caprylic, capric, lauric and myristic acids were also found. These fatty acids are also typical for milk fat. Due to its origin, chocolate is not expected to contain polyunsaturated fatty acids in significant concentrations [20]. The content of linoleic and \(\alpha\)-inolenic acids is determined at levels of 3.49% and 0.30%, respectively. Our results are in agreement with those obtained by [PERSON] et al. [20] and [PERSON] et al. [21] for milk chocolate.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type of Fatty Acid** & **Acid Common Nomenclature** & **Ruby Chocolate** \\ \hline C6:0 & Caproic & 0.19 \(\pm\) 0.05 \\ C8:0 & Caprylic & 0.12 \(\pm\) 0.01 \\ C10:0 & Capric & 0.30 \(\pm\) 0.03 \\ C12:0 & Lauric & 0.42 \(\pm\) 0.04 \\ C14:0 & Myristic & 1.35 \(\pm\) 0.07 \\ C14:1 & Tetradecanic & 0.15 \(\pm\) 0.01 \\ C15:0 & Pentadecanoic & 0.18 \(\pm\) 0.01 \\ C16:0 & Palmitic & 25.38 \(\pm\) 0.32 \\ C16:1 & Palmtideic & 0.42 \(\pm\) 0.01 \\ C17:0 & Margaric & 0.32 \(\pm\) 0.01 \\ C17:1 & Margaroleic & 0.06 \(\pm\) 0.00 \\ C18:0 & Stearic & 34.08 \(\pm\) 0.33 \\ C18:1n-9 cis & Oleic & 31.60 \(\pm\) 0.26 \\ C18:2n-6 trans & Linoleialidic & 0.03 \(\pm\) 0.00 \\ C18:2n-6 cis & Linoleic & 3.49 \(\pm\) 0.02 \\ C18:3n-3 cis & \(\alpha\)-linolenic & 0.30 \(\pm\) 0.01 \\ C20:0 & Arachidic & 1.35 \(\pm\) 0.01 \\ \hline \end{tabular}
\end{table}
Table 1: Fatty acid composition (%) of fats extracted from ruby chocolate.  
The size of unsaturated oleic and linoleic acids in the sn-2 position of the triacylglycerolcycl is 54.64 and 59.98%, respectively, which means that these fatty acids are mainly located in the internal position.
# Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System
## 3 The TiMAC Policy
Both constraints, \(q^{k+1}\geq N\) and \(q\geq kD+1\), are exploited by the Chlamtac-Farago algorithm to calculate \(q\) and \(k\) given \(N\) and \(D\). In order to gain more insight into TiMAC, in Figure 0(a), an example chain topology is depicted. Regarding the depicted topology, both the corresponding \"traditional\" and the TiMAC TDMA frames are presented in Figure 0(b). The traditional TDMA frame is given on the left side of Figure 0(b). It consists of three slots that are repeated three times and each node has been assigned a fixed slot in a way that ensures a collision-free communication among neighbors. On the other hand, a TiMAC frame is depicted on the right side of the figure. It consists of three subframes on which the slot assignment has taken place in accordance with the corresponding arbitrarily assigned polynomials. It is observed here that, when the nodes operate under TiMAC, every node is guaranteed at least one successful transmission per frame, even in the cases where topology changes occur. However, when traditional TDMA is chosen, special care has to be taken after a topology change, as a slot reassignment may be necessary. The next step is to implement this particular MAC policy in a system that consists of low-cost devices and evaluate its performance. The proposed system's characteristics are given in the following Section 4. Figure 1: (**a**) a chain topology of nine nodes; (**b**) traditional and TiMAC TDMA Frame examples.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 3 Methodology
### Neural Network Architectures  
A literature analysis (see Section 2.3) revealed the following network types to be suitable for time series problem and SoC estimation:  
* Feed-Forward Neural Networks (FNNs): the baseline for comparison
* Recurrent Neural Networks (RNNs)
* Convolutional Neural Networks (CNNs)
* Hybrid CNN and RNN
* Transformer-based neural networks
* Fusion of dynamic and static features: Fusion Hybrid Model
## 4 Conclusions  
An integrated model was developed to describe the joint effects of both temperature and current on the metering error and consistency of SEMs based on a binary quadratic polynomial. The results show that a lower temperature and a larger current result in a higher metering error of SEMs. Temperature significantly affects metering error--low temperatures (\(-\)15 \({}^{\circ}\)C) generate the highest measuring error, and high temperatures (32 \({}^{\circ}\)C) produce the smallest measurement error, which is consistent with existing studies. Moreover, temperature determines the effects of current on metering error since the current generates more severe influences on metering error under low temperatures than under high temperatures.  
In addition, the effects of temperature on standard deviation are different from the influences of current on standard deviation. The standard deviation declines with decreasing temperature from [32 \({}^{\circ}\)C, 15 \({}^{\circ}\)C], but increases from [15 \({}^{\circ}\)C, \(-\)15 \({}^{\circ}\)C]. The effects of current on standard deviation depend on the power coefficient, and exhibit a decreasing trend, a stable trend, and an increasing trend with decreasing current under power coefficients 1.0, 0.8C, and 0.5L, respectively.  
For the effects of power coefficients on the metering error of SEMs, the metering error exhibited disordered behaviors under power coefficient 0.5L compared to 1.0 and 0.8C. If we investigate how the power coefficient will affect the influences of temperature and current on metering error, the effects of current on measurement error are determined by the power coefficient, whereas the influences of temperature on the measuring error are slightly disturbed by the power coefficient.  
Why temperature, current, and power coefficient affect the metering error of SEMs needs to be further studied.  
Conceptualization, J.Q. and Y.S.; Data curation, B.Z. and G.S.; Investigation, J.Z.; Project administration, S.X. and B.Z.; Supervision, J.Z.; Visualization, Z.C.; Writing--original draft, S.X., B.Z. and Z.C.; Writing--review & editing, J.Q. and Y.S. All authors have read and agreed to the published version of the manuscript. This research was funded by Science and Technology Project of State Grid Shandong Electric Power Company, grant number 2021230001000044, 2021230001000045. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments**: We also would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their contribution to the paper.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# General Periodic Cruise Guidance Optimization for Hypersonic Vehicles
## 4 Numerical Examples and Analysis
### Optimal Velocities of Steady-State Cruise at Different Altitudes  
First, the optimal velocities of steady state cruise at different altitudes should be obtained, laying the foundation for further periodic optimization and comparison. The optimal velocity can be obtained through trajectory optimization by nulling the altitude rate, the Mach number rate, and the elevation angle rate in (2), respectively. The Matlab (Mathworks, Natick, MA, USA) function of _fmincon_ is invoked to conduct this mission, wherein _fmincon_ finds the constrained minimum of a function of several variables. In this method, we convert all dynamic equations into algebraic equations in terms of \(M\), \(\alpha\), and \(s\) in a steady situation for a specified altitude, \(h\). Here, \(M\) is the state variable, while \(\alpha\) and \(s\) are two control variables, which have their respective ranges to be determined by the user. The cost function is the fuel consumption rate with respect to these three variables. The optimization results for OSS cruise in the altitude range [42 km, 45 km] are shown in Table 2. Compared with the optimal steady-state result in [15] of 1.555 kg/km, these results are slightly higher because of the numerical calculation discrepancies from different software. The optimal cruising velocity is larger at a higher altitude, in line with our empirical knowledge.