esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model Description  
Figure 1 illustrates the integrated satellite-terrestrial remote sensing satellite network architecture, containing \(N\) satellites, \(M\) users, and a ground station. Among them, the ground station is denoted by \(G\), the set of users is denoted by \(U=\{u_{1},...,u_{M}\}\), the location of the user \(i\) is denoted as \(Loc(u_{i}\ )=latitude(u_{i}\ ),longitude(u_{i}\ )\), the remote sensing satellite set \(S\) is denoted by \(S=s_{1},...,s_{p}\), the set of communication satellites \(B\) is denoted as \(B=B_{1},...,B_{q}\), and \(p+q=N\). The remote sensing satellite set is divided into \(K\) clusters, with the satellite set of the \(k\)-th cluster denoted as \(S_{k}=S_{k,1},...,S_{k,|S_{k}|}\), and \(|S_{k}|\) represents the number of remote sensing satellites in the \(k\)-th cluster. Remote sensing satellites generate real-time remote sensing data with spatio-temporal characteristics. We consider remote sensing data that include both imagery and attribute data, where the attribute data format is (shooting time, latitude starting point, latitude ending point, longitude starting point, longitude ending point, payload type, resolution level, product level). Among them, \(P\) remote sensing satellites are equipped with caching modules, while communication satellites are solely responsible for data transmission. Users directly communicate with satellites via satellite terminals to access data, ensuring visibility to at least one communication satellite at any given time. Satellites can relay content to ground stations via feeder links; however, ground stations are typically located in remote areas, incurring high transmission costs and significant service delays. Therefore, proactively pushing content of potential interest to users can substantially reduce request latency. On the other hand, limited user cache resources necessitate intelligent push strategies to conserve cache usage. Thus, the core challenge lies in designing an intelligent delivery strategy to dynamically push newly generated remote sensing data to relevant users, aiming to minimize the average content delivery latency. The mathematical model of this system can be described as follows.
## 2 Materials and Methods
### Satellite Data  
Precipitation estimates data used in this work are from the IMERG product, the GPM level 3 multi-satellite precipitation algorithm, which combines all microwave sensors in the constellation and infrared-based observations from geosynchronous satellites [41]. The IMERG version 6 product fuses the early precipitation estimates by the TRMM satellite from the 2000 to 2015 with more recent precipitation estimates by the GPM satellite from 2014 to present. The IMERG version 6 products on a half-hour 0.1\({}^{\circ}\) grid were accessed on NASA's Goddard Space Flight Center website [[https://pmm.nasa.gov/data-access/downloads/gpm](https://pmm.nasa.gov/data-access/downloads/gpm)]([https://pmm.nasa.gov/data-access/downloads/gpm](https://pmm.nasa.gov/data-access/downloads/gpm)) (accessed on 17 December 2020). In this study, the IMERG data collection period was from June 2000 to December 2019, then accumulated to a daily data.
## 3 Results and Discussion
### MD Performance  
DCMD performance tests were performed for selected membranes with varying casting thicknesses (500 and 1000 \(\upmu\)m) and exposure times (5, 10 and 15 min). These particular membranes were selected because they offered an acceptable range of contact angle and pore sizes (Table 5). The results (Table 5) showed a flux range of 9-32 L/m\({}^{2}\)-h and SR \(>\) 99%. These flux values are within the range of MD flux reported for PVDF membranes [27; 28; 29]. In general, no obvious trend could be extracted from the salt rejection data since they are considerably high for all samples. When comparing the impact of casting thickness, at exposure time of 5 and 10 min, the flux of the 1000-\(\upmu\)m membrane was higher than the 500-\(\upmu\)m one, while the opposite trend was encountered at the 15-min exposure time. The trend of flux with exposure time for both casting thicknesses was also different. For the 500-\(\upmu\)m thickness, the flux changed with exposure time: 8.75 (at 5 min), 13.77 (at 10 min) and 22.19 L/m\({}^{2}\)-h (at 15 min). Meanwhile, for the 1000-\(\upmu\)m membranes, the flux values were 27.76, increasing to 31.82 and then dropping to 10.98 for exposure times of 5, 10 and 15 min, respectively. The trend of flux values unfortunately cannot be very well explained with the existing data in this study. It is not a function of MPS, nor a function casting thickness. In addition to surface morphology and MPS, it is most likely that the VIPS process also alters other properties (e.g., tortuosity, pore density, surface porosity or other) that affect the mass transfer resistance of the resulting membranes. Nevertheless, the ability to obtain DCMD membranes having an acceptable rejection and flux while improving their LEP has been well proven in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Casting** & **Exposure** & **Average Flux** & **Average Salt** & **Porosity** & **MPS (\(\upmu\)m)** & **Net Thickness** \\
**Thickness (\(\upmu\)m)** & **Time (min)** & **(L/m\({}^{2}\)-h)** & **Rejection (SR) (\%)** & **(\%)** & **(\%)** & **(\(\upmu\)m)** \\ \hline \multirow{3}{*}{500} & 5 & 8.7 \(\pm\) 3.2 & 99.95 & 71 & 0.0863 & 246 \\  & 10 & 13.8 \(\pm\) 7.0 & 99.75 & 87 & 0.2347 & 265 \\  & 15 & 22.2 \(\pm\) 6.9 & 99.91 & 76 & 0.1649 & 294 \\ \hline \multirow{3}{*}{1000} & 5 & 27.8 \(\pm\) 1.9 & 99.81 & 65 & 0.0693 & 307 \\  & 10 & 31.8 \(\pm\) 2.9 & 99.91 & 68 & 0.0641 & 327 \\ \cline{1-1}  & 15 & 11.0 \(\pm\) 7.5 & 99.99 & 66 & 0.068 & 381 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of direct-contact membrane distillation (DCMD) performance using membrane samples fabricated at 80% RH and 12% \(w/w\) PVDF casting solution.  
Figure 10: Impact of casting thickness on the properties of PVDF membrane prepared using VIPS process, all using 12% \(w/w\) PVDF polymer solution and under 80% RH. (**A**) Contact angle profile; (**B**) Bubble point profile; (**C**) LEP profile.
## 2 Scenario Illustration  
In Figure 1, patients carry wearable sensors collecting sensed medical data. Subsequently, medical data is transmitted through Zigbee with LBMR routing, which is what this paper focuses on, to reliably deliver a data-intensive wireless flow for real-time medical monitoring. LBMR can offer medical monitoring service a healthy, high-data rate, large coverage and robust transmission with balanced flow on distributed nodes. After the data safely and correctly arrives at the data center, professionals and doctors can perform remote diagnoses and provide timely warnings or advice.  
LBMR is proposed to cooperate with a large-scale wireless Zigbee network, in which two types of bottlenecks can occur. In a Zigbee Personal Area Network, medical data is usually sent to the coordinator, which manages the network and has direct links (_i.e._, USB connections) to the gateway, and clearly the coordinator is the bottleneck of the network. [PERSON] proposed a successful remote ECG monitoring system of large scale Zigbee sensors [20]. This system eliminates this bottleneck with the Coordinator Traffic Diffusion (CTD) method to redirect excessive traffic from coordinator to the gateway. However, although CTD can diffuse the traffic near the coordinator, so the traffic load may not equally go through routers, when the traffic load increases significantly due to a large number of medical sensors, routers having more neighbors can experience relatively heavy traffic load and become a second type of bottlenecks.  
To prevent all possible bottlenecks, LBMR guides Zigbee routers in choosing next-hops with the lowest traffic loads. Each LBMR router in a Zigbee network serves network traffic equally to prevent the router from becoming a potential bottleneck. LBMR not only removes the bottlenecks near the gateway, but also eliminates possible bottlenecks in the entire Zigbee network. Therefore, LBMR routing ensures a reliable large scale Zigbee network service for data-intensive medical monitoring.  
Taking a closer look at LBMR in Figure 2, LBMR provides an alternative routing service for Zigbee networks without modifying the existing Zigbee stack by adding a hook at the network (NWK) layer, as shown in Figure 2. Once a Zigbee router forwards medical data, LBMR guides the Zigbee NWK layer to choose a next-hop with the least load toward the gateway. Besides, LBMR also ensures the next-hop is closer to the gateway than the router itself to prevent routing loops. Therefore, LBMR design can easily cooperate with existing Zigbee stacks.  
Figure 1: Example scenario of LBMR routing for real-time medical monitoring.
# Sensors for Sustainable Smart Cities: A Review
## 3 Results
### Study Cases
_Energy._ Using engine sensors, information about the fuel consumption associated to the operation of the A/C in light-duty vehicles was monitored via OBD for a five-month period [131]. Results showed that specific fuel consumption due to A/C usage is higher at lower speeds of the vehicle and it is lower at higher driving speeds; and shows the potential of proposing solutions towards vehicle energy efficiency by analyzing information coming from engine sensors through the OBD port.
## 2 Model and Method
### Model and Method for the Rail Mobility/_Receptance_
#### 2.1.2 Timoshenko-Beam Track Model  
To investigate the rail mobility/receptance, a Timoshenko-beam track model is established based on the structural properties of the monolithic track bed, as shown in Figure 2.  
The model includes a long straight rail and fasteners. The sleepers, the track bed and the infrastructure are all considered as the fixed constraint. The rail is considered as a Timoshenko beam. As the cross-sectional deformation of the rail cannot be considered, the model is limited to frequencies of 1500 Hz [18]. To reduce the influence of the wave reflections, the non-reflecting boundary conditions are applied at both ends of the rail. In this way, the length of the model can also be shortened. The fastener is considered as a vertical extension spring whose upper end is connected to the rail beam and lower end is fixed. The fastener spacing (denoted by \(a\)) is obtained from the field measurement. To consider the damping of the fasteners, the complex vertical fastener stiffness \(k_{\mathrm{cv}}\) is adopted for the vertical extension spring:  
\[k_{\mathrm{cv}}=k_{\mathrm{v}}(1+i\eta_{\mathrm{f}}) \tag{1}\]  
Figure 1: The monolithic track bed structure: (**a**) Photograph and (**b**) Sectional view.  
Figure 2: The Timoshenko-beam track model.  
where \(k_{v}\) is the vertical fastener stiffness and \(\eta_{\rm f}\) is the damping loss factor of the fastener. A vertical harmonic point excitation \(F_{v}\)=\(F_{0}\)e\({}^{\rm i\omega t}\) is applied on the rail at the middle of the model, where \(F_{0}\) is the amplitude, \(\omega\) is the angular frequency, and \(t\) is the time.
# Trustworthiness of Situational Awareness: Significance and Quantification
###### Abstract  
Situational awareness (SA) is of tremendous significance for successful operations in many domains, such as surveillance, humanitarian, search, and rescue missions, and national security. SA is particularly important for the defense sector, and is regarded as the decisive factor in military and air combat engagements. Commanders and operators rely on the accuracy and fidelity of SA for comprehending the environment, decision-making, and carrying out actions based on these decisions for accomplishing a mission. SA, however, is susceptible to adversarial attacks that can compromise the security and trust of SA systems. In this paper, we discuss the significance of security and trust of SA from an air force perspective. We then propose a model for quantifying the trustworthiness of an SA system. We further present numerical examples that demonstrate the quantification of trustworthiness of an SA system using our proposed model. Finally, we conclude this paper with future research directions for quantifying the security of SA systems.  
situational awareness; security; integrity; trustworthiness; common operating picture; modeling 2024
# Ozone Activation on TiO\({}_{2}\) Studied by IR Spectroscopy and Quantum Chemistry
## 1 Introduction  
The type of interaction of ozone with solid surfaces is a key factor determining the activity of different materials for catalyzing ozone decomposition and the reactivity towards other adsorbed molecules. Adsorption at the surface of atmospheric aerosol particles could affect the thickness of the ozone layer. Water purification by photo-ozonolysis catalyzed by TiO\({}_{2}\) in the case of certain types of contamination may result in the formation of undesirable products. Ozone emitted by some devices is harmful for human health. Although the problem is of extreme importance for industry and ecology, the number of spectral studies regarding ozone adsorption is limited, apparently because of the technical difficulties of working with this highly active and unstable compound. Using low-temperature FTIR spectroscopy it was possible to study ozone adsorbed on SiO\({}_{2}\)[1, 2, 3], Al\({}_{2}\)O\({}_{3}\)[4], MgO [5], CaO [6], TiO\({}_{2}\)[2, 7, 8], CeO\({}_{2}\)[8, 9], ZnO, and some zeolites [8]. In the spectra of TiO\({}_{2}\) and CeO\({}_{2}\), besides the bands of weakly bound molecules, ozone chemisorption with serious frequency shifts with respect to the band positions of physisorbed molecules were observed.  
Recent data on the splitting of the \(\
u_{1+3}\) combination band of chemisorbed ozone into eight maxima testified to the decrease in the symmetry of molecules which were supposed to be bound to the surface Ti\({}^{4+}\) ion by one of the terminal atoms, forming a kind of monodentate complex [8]. However, the DFT calculation of ozone adsorbed on nanocrystalline TiO\({}_{2}\)[10] has shown that while a triplet monodentate complex, where the molecule is bound to titanium atom via one of the terminal oxygen atoms, is the most stable, the computed difference between the frequencies of \(\
u_{1}\) and \(\
u_{3}\) vibrations is in a better agreement with the experimental values for singlet bidentate species. In contrast, our data on isotopic splitting support monodentate species where the molecule is no longer symmetrical and there are no equivalent oxygen atoms. A profound analysis of theexperimental data, partially presented in [8], in comparison with the DFT calculations of isotopically substituted ozone complexes, clarifies this point.  
In our study we provide a more detailed analysis of the FTIR spectra of different ozone isotopomers adsorbed on TiO\({}_{2}\) at various degrees of enrichment. The obtained data are compared with the results of DFT calculations of ozone complexes with TiO\({}_{2}\) clusters to check the proposed assignment of the distinct components in the complex structure of the observed spectra of chemisorbed isotopically mixed ozone. Modeling the interaction of ozone with clusters of titanium dioxide with differing types of coordination of surface Ti\({}^{4+}\) cations should help to establish the geometry of adsorption complexes and clarify the nature of adsorption sites.
## 3 State-of-the-Art Review
### Related Projects, Systems and Applications  
Different WSN projects, systems and applications have been proposed and developed in the literature for monitoring marine environments. Table 3 summarizes the features of related projects, systems and applications. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Technology** & **Standard** & **Description** & **Throughput** & **Range** & **Frequency** \\ \hline WiFi & IEEE & System of wireless data & 11/54/300 & \(<\)100 m & \\  & 802.11a; & transmission over computational & Mbps & & 5.8 GHz \\  & 802.11b/g/n & networks. & & & 2.4 GHz \\ Bluetooth & IEEE & Industrial specification for WPAN & v. 1.2: 1 Mbps & Class 1: 100 m & 2.4 GHz \\  & 802.15.1 & which enables voice and data & v. 2.0: 3 Mbps & Class 2: 15–20 & \\  & & transmission between different & UWB: & m & \\  & & devices by means of a secure, & 53–480 Mbps & Class 3: 1 m & \\  & & globally free radio link (2.4 GHz). & & & \\ ZigBee & IEEE & Specification of a set of high-level & 250 Kbps & \(<\)75 m & 2.4 GHz \\  & 802.15.4 & wireless communication protocols & & & \\  & & for use with low consumption & & & \\  & & digital radios, based on WPAN & & & \\  & & standard IEEE 802.15.4. & & & \\ WiMAX & IEEE & Standard for data transmission & \(<\)75 Mbps & \(<\)10 km & 2–11 GHz \\  & 802.16 & using radio waves. & & & \\ GSM & & Standard system for & 9.6 Kbps & Dependent on & 850/900/1800 \\  & & communication via mobile & & service provider & /1900 MHz \\  & & telephones incorporating digital & & & \\  & & technology & & & \\ GPRS & GSM extension for unswitched (or & 56–144 Kbps & Dependent on & 850/900/1800 \\  & & packaged) data transmission. & & service provider & /1900 MHz \\ \hline \end{tabular}
\end{table} 
Table 2: Wireless communication technologies [22].
###### Abstract  
The rate of the removal of materials coated with nano\(-\)crystalline diamonds by femtosecond laser etching was examined by adjusting the repetition rate of the femtosecond laser, the energy\(-\)flux density of the concentrated spot, and the scanning speed. The observational results of the white-light interferometer and the numerical fitting approach were used to develop the removal rate function model of the nano-crystalline diamond-covered material etched by the femtosecond laser. The findings demonstrated that the rate of material removal was not greatly affected by the repetition frequency and that the amount of laser energy accumulated over time on the coated surface is steady. The processing outcomes under different laser scanning speeds are different, and the material removal rate tends to increase and then decrease with an increase in scanning speed. The greater the energy\(-\)flux density of the focused spot, the greater the etching intensity, and the greater the material removal rate. With an increase in scanning speed, the rate at which the material is removed often rises initially before falling.  
laser optics; ultrafast optics; nano-crystalline diamond coating; ablation; material removal rate +
Footnote †: journal: Applied Physics Letters
## 4 Materials and Methods
### Total RNA Isolation  
_S. mutans_ UA159 was inoculated into a BHI medium at pH 5.5. Once the cells reached the late exponential/early stationary phase, total RNA was extracted and purified using the miRNeasy Mini Kit (Qiagen, Hilden, Germany), according to the manufacturer's recommended protocol. Samples were prepared in triplicate (IDs: A, B, and C).
## 3 Results and Discussion
### E-Nose Analysis  
The E-nose employs specific sensors and pattern recognition systems to quickly provide comprehensive aroma information on samples [44]. Based on the data obtained from the E-nose (Table S2), the information in Figure 5 was obtained using the principal component analysis method (PCA), which facilitates a more intuitive representation of the aroma disparities among different cherry wines. It can be seen that this model elucidated 94.95% of the total contribution rate (PC1 was 81.77%, PC2 was 13.18%), providing a good explanation of the aroma distinctions among the cherry wine samples. Not all cherry wine samples were clearly separated in PC1 and PC2, which indicated that the aroma components of some cherry wines were similar. As shown in Figure 5A, the locations of cherry wines with low organic acid addition were close to the control, and there was overlap among the different organic acid treatment groups, while significant distinctions were observed with the increase in organic acid addition. This indicated that similar aroma profiles were present in the different treatment groups, while the effects of the different organic acids were various, resulting in significant differences among them with the aroma enhancement of acids.  
Notably, all samples exhibited elevated response values in the WSS, W2S, W1S and W2W responders, indicating an abundance of nitrogen oxides, broad-alcohols and short-chain aromatic and sulfide compounds. However, the various cherry wines displayed distinct response values for these sensors. In comparison with the control, the response values for W2S in the treatment groups were significantly increased, indicating that the addition of acids enhanced the synthesis or release of alcohols and aldehydes, while the  
Figure 5: E-nose data on control and cherry wine with different organic acids. (**A**) Score plot; (**B**) loading plot.  
W5S response value only increased in the LC group, but had no significant difference in the MC and MLC groups. This means the addition of lactic acid probably enhanced the nitrogen oxide production. In addition, higher W1C and W5C values were detected in the MC group; both sensors were sensitive to aromatic compounds. The results suggest that the E-nose could properly characterize the cherry wine samples, but the specific differences among these samples needed to be further identified by GC-MS due to the limitations of E-nose analysis.
## 3 The Proposed Method
### Parameter Estimation Based on SAC Algorithm and 2D-FFT
Therefore, the values of \(F_{0}\) and \(F_{1}\) can be estimated by performing peak detection on the data obtained by Equation (31), thereby estimating the values of \(a_{1}^{\prime}\) and \(a_{2}\). Figure 3: The principle of the SAC algorithm. (**a**) Spectrum. (**b**) Spectrum division. (**c**) Spectrum shifting. (**d**) Signal cross correlation. In the range direction, \(F_{0}\) represents the sampling time corresponding to the peak position, and the sampling interval \(R_{bin}=\sfrac{c}{2f_{s}}\) in the range-time domain is generally at the centimeter level or larger. The error of \(a_{1}^{\prime}\) estimated directly by Equation (31) may be large. Therefore, zero-padding at the end of the data increases the Fourier transform length, when the range dimension inverse Fourier transform is performed, thereby improving the parameter estimation accuracy. So far, three unknown parameters, \(a_{1}\), \(a_{2}\) and \(a_{3}\) in Equation (6), have been estimated, and their estimated values are \(\widehat{a_{1}}\), \(\widehat{a_{2}}\) and \(\widehat{a_{3}}\), respectively.
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 4 Datasets
### Fireworks Dataset  
We downloaded different types of fireworks videos from the web and, to decrease the computational cost, split them into less than 6-s length clips. Our input video size is 240 \(\times\) 320, and we applied a few augmentation techniques such as flip-up, flip-down, and rotate to increase the size of the dataset. However, fireworks videos have been selected under three conditions to maintain the fairness of our dataset and control the scope of our study as: each video should have one firework type, videos should not contain any other moving objects except fireworks, and the background should be static with invisible objects. The dataset was manually categorized into eight classes, _Chrysanthemum_, _Crossette_, _Desi_, _Dot_, _Drop_, _Fish_, _Palm_, and _WaterFlower_, and our current dataset consists of 1500 video clips. Sample videos of the fireworks dataset can be seen on our project web page [46].