esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evolution Characteristics of the ESV in Different Landform Types
#### 3.2.2 ESV Terrain Effect in Different Landform Areas
The maximum pure-karst area ESV ranged from 6\({}^{\circ}\) to 15\({}^{\circ}\), accounting for approximately 24% in each period. The minimum values of the three landform areas were all in the slope greater than 45\({}^{\circ}\), and the proportion of the ESV in each period was significantly different. The proportion of the ESV in all periods was approximately 55% in the pure-karst area, followed by approximately 28% in the non-karst area, and approximately 15% in the semi-karst area. The ESV in the non-karst and semi-karst areas decreased above 25\({}^{\circ}\) of the slope, and their ESV per unit area also showed a similar change pattern. The reason for the decline is that the non-karst and semi-karst areas cumulatively accounted for 69.05% and 77.16% of their respective total areas in the range of 0\({}^{\circ}\) to 25\({}^{\circ}\). The forested area of the non-karst area increased from 2% in the 0\({}^{\circ}\) to 6\({}^{\circ}\) range to 28% in the 15\({}^{\circ}\) to 25\({}^{\circ}\) range, and the semi-karst area increased from 2% to 18%. Cropland also showed the same trend, but the proportion was lower than that of the forest areas. In spite of the Grain for Green Policy, the area of each landform type greater than 25\({}^{\circ}\) was small, leading to the characteristics of the ESV in non-karst and semi-karst areas first increasing and then decreasing with the slope change. The total ESV and ESV per unit area of pure-karst area were the lowest among the three landforms, except for ranges less than or equal to 6\({}^{\circ}\). The reason for this is that, in the less than or equal to 6\({}^{\circ}\) slope, the pure-karst area was 973,586 km\({}^{2}\). Although this was twice as large as the non-karst and semi-karst areas, the main contributors to the ESV were forest and cropland areas, accounting for only approximately 2% and 9%, respectively, which led to a slightly higher total ESV than for the non-karst and semi-karst areas, while its value per unit area remained low.
## 3 Results and Discussion  
### Effect of Thermal Pretreatment on Total Curcuminoids Recovery of Fresh Turmeric  
The effect of pretreatment on cell disintegration index and total curcuminoid recovery are shown in Figures 1 and 2, respectively. In high starch content material like _C. Longa_, suitable temperature usage may create beneficial effects for extraction. For instance, the changing of viscosity, diffusivity, solubility, and surface tension [1].  
It was reported that turmeric starch content might reach 35% on a wet basis [13], and amylose content in isolated turmeric starch was up to 48.4% \(w/w\)[36]. In addition, fresh turmeric contains 86.91% of water and was treated in hot water (50-90 \({}^{\circ}\)C) so the behavior of starch is the crucial point and should be taken into account. In the literature, it was revealed that turmeric starch showed no change in rheological behavior change up to 60 \({}^{\circ}\)C; however, in the range of 60-80 \({}^{\circ}\)C, the starch starts to swell, and the water-holding capacity of the starch increased considerably after 80 \({}^{\circ}\)C, and finally lead to pasting or gelatinization [36].  
In the research of [PERSON] and [PERSON] [27], thermal treatment should be higher than the temperature of 70 \({}^{\circ}\)C to achieve the destruction of the cell membrane. For those reasons, in the present investigation, the effect of temperature was examined at 50 \({}^{\circ}\)C, 70 \({}^{\circ}\)C and 90 \({}^{\circ}\)C to gain insight into TP's influence on different cell disintegration levels.The main effect of varying pretreatment methods in the present investigation is mainly on the turmeric cell and tissue. [PERSON], et al. [37] implied that \(Z_{p}\) is the vital parameter to evaluate the rate of damaged cells within the plant after treatment.  
In this work, the obtained results indicated that a preheating of fresh turmeric to 50 \({}^{\circ}\)C (TP-105) did not significantly increase the TC than the untreated sample (Figure 2). Nevertheless, 70 \({}^{\circ}\)C (TP-188) and 90 \({}^{\circ}\)C (TP-272) increased the \(Z_{p}\) up to 39% and 32%, respectively (Figure 1); hence the TC raised to 27% and 17%, respectively (Figure 2). Albeit most intensive TP treatment, TP-272 increased TC (Figure 2) with no significant difference with TP-188.  
[PERSON] and [PERSON] [38] reported that CUR was stableel when exposed at 70 \({}^{\circ}\)C in 10 min. In the case of higher temperatures, CUR starts to decompose lightly. For this reason, applying high temperatures can lead to the decomposition of curcuminoids.
## 3 Early Warning Model  
### Kalman Filtering  
Kalman filtering (KF), which was put forward by [PERSON] in 1960, is a linear recursive filtering method based on linear unbiased minimum variance estimation theory. It combines the concept of state space with filtering theory. The cores of this method are the transition equation and the recursive formula. With them, new state and observation values can be predicted according to the estimated value at a previous moment and the observation value at the present moment.  
Given a discrete time system, \(X_{1}\), \(X_{2}\), \(X_{3}\), \(\cdots\),\(X_{k}\) are the system state vectors at \(kT_{s}\), where \(X_{k}\in R^{n}\), \(T_{s}\) is the measuring interval of the system. We defined \(U_{k}\) as the system control input vector and \(W_{k}\) as incentive noise vector, and the state transition equation is described in Equation (1).  
\[X_{k}=AX_{k-1}+BU_{k}+W_{k} \tag{1}\]  
Figure 8: Monitoring equipment on the landslide. (a) Beidou receiver. (b) Crack meter.  
Figure 7: Locations of the equipment on the landslide.  
Defining \(Z_{k}\in R^{n}\) as the observation vector and \(V_{k}\) as the observation noise vector, we obtained the observation formula:  
\[Z_{k}=HX_{k}+V_{k}. \tag{2}\]  
\(A\), \(B\), \(H\) is the state transition matrix, and \(W_{k}\), \(V_{k}\) are the independent and normal distribution of white noise:  
\[W_{k} \sim N(0,Q). \tag{3}\] \[V_{k} \sim N(0,R). \tag{4}\]  
In the state estimating of a discrete time system, Formula (1) is used to obtain \(\hat{X}_{jk}\), which is the best estimating value of \(X_{j}\) at \(T_{s}\), and there are three situations:  
1. When j = k, \(\hat{X}_{jk}\) is the optimum filtering of \(X_{k}\).
2. When j > k, \(\hat{X}_{jk}\) is the optimum predicting of \(X_{k}\).
3. When j < k, \(\hat{X}_{jk}\) is the optimum smoothing of \(X_{k}\).  
The recursive formula can be described as time update process and state update process. The time update process:  
\[\hat{X}_{kjk-1} =A\hat{X}_{k-1|k-1}+BU_{k-1} \tag{5}\] \[P_{kjk-1} =AP_{k-1|k-1}A^{T}+Q \tag{6}\]  
where \(P\) is the error estimating covariance matrix:  
\[E_{k} =X_{k}-\hat{X}_{k} \tag{7}\] \[P_{k} =E\Big{(}E_{k}E_{k}^{T}\Big{)}. \tag{8}\]  
The state update process:  
\[K_{k} =P_{k|k-1}H^{T}\big{(}HP_{k|k-1}H^{T}+R\big{)}^{-1} \tag{9}\] \[\hat{X}_{k|k} =\hat{X}_{k|k-1}+K_{k}\Big{(}Z_{k}-H\hat{X}_{k|k-1}\Big{)},\] (10) \[P_{k|k} =(I-K_{k}H)P_{k|k-1} \tag{11}\]  
The computational process of the KF algorithm is shown in Figure 9.  
Figure 9: The computational process of the Kalman filtering (KF) algorithm.
## 4 Experiments and Results
### Map-Merging Results
Figure 11: A large-scale drivable region map created using two methods (a single run without splitting data; the proposed method) is manually overlaid on a satellite image. To ensure a fair comparison, only the drivable regions were retained. (**a,b**) Each image depicts a large-scale drivable map created from data captured in a single run. Despite using the same dataset in the same environment, the results showed significant inconsistencies. (**c**) Image of a large-scale drivable map created via the proposed method.
###### Abstract  
Investigating the absorption and scattering effects of atmospheric particles, i.e., raindrops and fog droplets, is required to establish a comprehensive and accurate channel model. However, for long-distance communication in outdoor scenarios, research on the propagation characterization of fog and rain attenuation in the terahertz (THz) band is insufficient. In this study, fog and rain attenuation characterization with different conditions are characterized. First, fog attenuation at different temperatures and diverse visibility is explored using Rayleigh approximation theory and Mie theory. The results demonstrate that visibility and frequency have a stronger effect than temperature on fog attenuation. Then, rain attenuation as a function of rainfall rate is theoretically determined using Mie theory and the Joss, M-P, and Weibull distribution. The results show that rainfall rate and frequency have greater impact than raindrop distribution on rain attenuation. There are large differences in rainfall attenuation under diverse distributions. Accurate fog and rainfall attenuation information can be used to better estimate path loss and the link budget for terahertz communication in outdoor scenarios.  
Keywords:6G; THz communication; fog and rain attenuation; Rayleigh approximation theory; Mie theory +
Footnote †: journal:  
_Acticle_
# A Method to Determine BeiDou GEO/IGSO Orbital Maneuver Time Periods
## 2 Theory of Orbital Maneuver Period Detection
### Detection Factor of Orbital Maneuver Time Period
The start and end time discrimination factor \(L_{start}\) and \(L_{end}\) of the satellite orbit maneuver is defined by:  
\[L_{start}=D_{back}-T_{Max} \tag{8}\]  
\[L_{end}=D_{for}-T_{Max} \tag{9}\]  
where \(T_{Max}\) is the empirical threshold of \(V_{back}\) and \(V_{for}\). The empirical threshold \(T_{Max}\) of the satellite is given in advance, which is key to detecting the period of orbital maneuvers. Considering that the absolute value of the difference between \(S\) and \(\widehat{D}_{obs}\) follows normal distribution, the values of \(V_{back}\) and \(V_{for}\) in the normal condition of satellites are all in the interval of 99.73% (3\(\sigma\)) confidence coefficient. That is, if \(V_{back}\) and \(V_{for}\) are out of the corresponding confidence interval, it is considered to be abnormal. Thus, \(T_{Max}\) for satellites is given by the upper limit of the confidence interval of the difference between \(S\) and \(\widehat{D}_{obs}\) for the 99.73% confidence coefficient, which uses data from 2017. When \(L_{start}\) is greater than 0 and keeps a sustained decreasing trend in a period, a 5 min time period (10 epochs) is chosen for this study. The time of the last epoch when \(L_{start}\) is greater than 0 is the start time of orbit maneuvering. Similarly, when \(L_{start}\) is greater than 0 and keeps a sustained growth trend in a period, the end time of orbit maneuvering is the time of the first epoch when \(L_{end}\) is greater than 0. The orbital maneuver time period can be determined by the start time and end time detected by the start time and end time factors.
## 4 Simulation Evaluation  
### Simulation Parameters  
In this section, we evaluate the P-MAC design in comparison with S-MAC and RMAC using the OPNET modeler. For fairness, we give the simulation result of both the basic P-MAC without the  
Figure 4: Two-sink case. Each number denotes a corresponding grade.  
routing function and the full P-MAC that is seamlessly integrated with routing. In comparison with the full P-MAC, the difference is that in the basic P-MAC, the RTS is sent to a concrete next node, which has its address passed down by the networking layer. So only the node for which the RTS is destined will reply with CTS without waiting for CW time. Figure 5 illustrates the basic P-MAC, and the maximum time of the _SEND/RECEIVE DATA_ period, \(T_{S\&R}\), is shown below. This result differs from that calculated by Equation (1):  
\[T_{{}_{S\&R}}=CW+\text{DIFS}+3\text{SIFS}+durRTS+durCTS+durDATA+durACK. \tag{5}\]  
Each node uses the two-ray ground radio propagation model and has a single omni-directional antenna. Table 1 shows the key networking parameters used in our simulation. These parameters are the default settings in the standard S-MAC simulation module distributed with the ns-2.29 package. The sizes and transmission latencies of different types of packets are shown in Table 2, and the settings are the same as in [5].  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Bandwidth** & 20 Kbps & **Tx Range** & 250 m \\
**Idle Power** & 0.45 W & **Carrier Sensing Range** & 550 m \\
**Sleep Power** & 0.05 W & **Contention Window (CW)** & 64 ms \\
**Rx Power** & 0.5 W & **DIFS** & 10 ms \\
**Tx Power** & 0.5 W & **SIFS** & 5 ms \\ \hline \end{tabular}
\end{table}
Table 1: Networking Parameters.  
\begin{table}
\begin{tabular}{|c|c|c|} \hline  & **Frame Size (bytes)** & **Tx Latency (ms)** \\ \hline
**RTS/CTS** & 10 & 11.0 \\
**ACK** & 10 & 11.0 \\
**PION (RMAC)** & 14 & 14.2 \\
**DATA** & 50 & 43.0 \\ \hline \end{tabular}
\end{table}
Table 2: Transmission Duration Parameters.  
Figure 5: Basic P-MAC without routing function.  
PION relaying number \(N\) in RMAC defines the distance (hops) a PION frame can be forwarded in the _DATA_ period. In our simulation, we set \(N=4\), as in [5]. The cycle related parameters are shown in Table 3. Both S-MAC and RMAC keep the same duty cycle (about 6%). Because the cycle division in P-MAC is different from that of both S-MAC and RMAC, the full P-MAC is set with \(\mathit{sleep\_factor}=14\) to have the same cycle duration as RMAC and the basic P-MAC is set with \(\mathit{sleep\_factor}=21\) to have a similar cycle duration to the full P-MAC.
# Ultra-Low-Power FinFETs-Based TPCA-PUF Circuit for Secure IoT Devices
## 2 Adiabatic FinFET-Based PUF  
### Adiabatic Logic  
Adiabatic switching is commonly used for minimizing the energy lost during the charging/discharging period at all nodes of the circuit. The main concept of adiabatic switching is shown in Figure 2b, which indicates a transition that is considered sufficiently slow such that heat is not significantly emitted. The adiabatic dissipated energy is expressed as:  
\[\mathrm{E_{adiabatic}}=\frac{RC}{\tau}\mathrm{CV_{dd}}^{2} \tag{1}\]  
where \(R\) is the effective resistance in the driven device, \(C\) is the output node capacitance to be switched, \(\tau\) is the time over which switching occurs, and \(\mathrm{V_{dd}}\) is the voltage to be switched across. Ideally, the charging energy, \(\mathrm{E_{adiabatic}}\), tends to zero by increasing the length of \(\tau\). Conversely, the conventional CMOS logic operation is shown in Figure 2a, with the following equation:  
\[\mathrm{E_{CMOS}}=\frac{1}{2}\mathrm{CV_{dd}}^{2} \tag{2}\]  
Here, in Equation (2), it is possible to reduce the charging energy only by reducing Vdd or capacitor, C. Figure 2c shows a comparison of the peak supply current for the equivalent RC models of the conventional CMOS logic versus the adiabatic logic. The comparison result in this figure shows that the instantaneous peak supply current of the adiabatic logic is significantly lower than that of the conventional CMOS logic style.
## 4 A Framework for Improving Accessibility of Serious Games in Handheld AR
### Methodology for Automated Collection, Storage and Analysis of User Interactions
#### 4.1.2 Selection of Technology and Implementation of User Interaction Tracking  
After selecting the data to be tracked in the context of accessibility in the previous activity, it is necessary to define a mechanism for tracking user interactions. The goal of the second activity of the methodology is to enable the automatic tracking of user interactions when playing a serious game. This requires selecting the technology for implementing the software for tracking user interactions at key interaction points in a serious game and a method for distinguishing individual game sessions by users. A serious game that generates events to be tracked in the context of accessibility must be integrated with an analytics platform that supports event tracking. As the focus is on software solutions that are serious games, it is necessary to consider how different game engines can support data tracking, e.g., Unity [68] or Unreal Engine [69].  
It is recommended that support for tracking interaction parameters is implemented during game development while functionality for sending events with specific interaction parameters to an analytics platform should be implemented at defined key interaction points in a serious game. The xAPI standard supports event-based tracking in serious games (as described in the previous section) whereby xAPI statements are usually stored in a Learning Record Store (LRS). This is a database with defined endpoints to which data is sent, and which stores it securely and enables further processing. Some of the well-known LRSs that support the xAPI standard are Learning Locker [70], Watershed [71], Rustici LRS [72] and Yet Analytics LRS [73]. Although the focus of LRSs is on storing and managing xAPI statements, some LRSs offer advanced analytics options. Most platforms offer basic reporting and data visualisation. If more advanced analytics options are required that  
Figure 3: Graphic representation of the methodology for the automated collection, storage and analysis of user interactions in the form of a sequence of activities 1–4.  
the chosen analytics platform does not offer (e.g., data modelling, predictive analytics, machine learning algorithms), other tools or platforms should be used and integrated or implemented as required.
## 4 Conclusions  
The morphology and electrical properties of TiN thin films grown on Si/SiO\({}_{2}\) <100> substrates by using a DC sputtering system were investigated. The effects of source power and the working pressure on roughness and resistivity were systematically studied. The TiN thin films with suppressed secondary phase (Ti\({}_{2}\)N) formation remained stable against oxidation at a high source power, resulting in very low resistance. The smallest R\({}_{\rm q}\) value of 0.3 nm and the lowest resistivity of 0.10 mf\(\cdot\)cm were obtained for the TiN thin film fabricated at a high source power (150 W) and low working pressure (0.5 \(\times\) 10\({}^{-3}\) Torr). Moreover, we applied the MIM structure to the TiN thin film and confirmed that its electrical properties were comparable to those of noble-metal thin films. These results suggest that TiN films are potential application candidates in MIM capacitors and Si technology.  
Conceptualization, V.D. and E.-T.K.; Data curation, V.D., D.-B.S. and E.-T.K.; Formal analysis, V.D. and D.-B.S.; Funding acquisition, D.-B.S. and E.-T.K.; Investigation, V.D., D.-B.S. and C.V.A.; Methodology, V.D.; Supervision, E.-T.K.; Writing--original draft, V.D. and D.-B.S.; Writing--review and editing, J.-H.L., J.-H.P. and E.-T.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (2020R1A4A4079397, 2021R1A2C1006241 and 2022R1C1C2005720). This work was also supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004, 2022H-01-02-08-005). This work was also supported by Samsung Electronics.  
Figure 5: (a) XRD pattern, (b) RMS roughness (R\({}_{\rm q}\)) and resistivity of TiN thin film on the Si/SiO\({}_{2}\) <100> substrate with various working pressure.  
Figure 6: (a) High–frequency (1 MHz) capacitance–voltage (C–V) curves, (b) leakage current density, and (c) dissipation factor as a function of frequency for MIM devices with various Al\({}_{2}\)O\({}_{3}\) thickness. The inset of (a) shows the schematic of MIM structure.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 4 MIMT-CNN Modeling
### Multi-Task Loss Function
#### 4.2.1 EMI Classification Loss  
The classification loss \(L_{v}\) uses focal loss function [29]. Compared with the commonly used cross-entropy loss function, the focal loss will not change for the samples with inaccurate classification, while for the samples with accurate classification, the loss will become smaller. This is equivalent to increasing the weight of the inaccurate samples in the loss function, which forces the network to focus on the samples which are difficult to be classified. \(L_{v}\) is calculated by  
\[L_{v}=-\alpha(1-p_{v})^{\gamma}\log(p_{v}) \tag{9}\]  
where \(\gamma\) is the focusing parameter, which increases the sensitivity of the network to the misclassified data. \(\alpha\) is the balancing parameter, which can be set to reduce the weight of negative samples. \(\gamma=2\), \(\alpha=0.25\) are set in this work. \(p_{v}\) is the probability of the predicted category. \((1-p_{v})^{\gamma}\) is the modulating factor, which is used to reduce the weight of easily classified samples.  
Due to the change of the modulating factor, the samples that are difficult to train play a leading role, thus allowing the model to focus more on the samples that are difficult to be classified during training as well as improving the accuracy of the model's classification.
## 2 Background and Related Works
### Task Offloading in Satellite-Terrestrial Cooperative Networks  
Constrained by the size and battery capacity, the IoT terminal devices' computing resources and storage capabilities are limited. To this end, some solutions [24; 25; 26] that offload computational tasks to edge or clouds for execution have been well investigated. Although promising, it will fail in remote regions due to the lack of edge or cloud nodes. Then, remote tasks have to offload to urban TCs via satellite forwarding, which will inevitably cause high delays. To solve this problem, the satellite edge computing architecture (SatEC) is currently proposed [2; 3; 4]. Through carrying edge computing servers on satellites, satellites can provide computing resources. Therefore, tasks can be executed directly by satellites, rather than being sent to urban TCs for execution. For example, [PERSON] et al. [11] proposed a satellite-aerial integrated computing architecture, where aerial users offload tasks to high-altitude platforms or LEO satellites. [PERSON] et al. [27] also explored the satellite-UAV computing architecture, where terrestrial computational tasks can be executed by satellites or forwarded to UAV for execution. Considering the intermittent communication caused by satellite orbiting, [PERSON] et al. [7] proposed an IoT-to-satellite offloading method based on game theory. However, they did not consider the problem that the computing and storage resources of SatEC servers are limited, which will result in a certain queuing delay when a large number of tasks are simultaneously offloaded.  
To address the above issues, ref. [28] first introduced the concept of satellite-terrestrial cooperative task offloading. In their proposed double-edge computing architecture, both satellite and terrestrial servers can be used as communication resources for computing and caching. Moreover, they further designed a double-edge computing offloading algorithm, which can decide the offloading position with low delay and energy consumption. However, they neglected to consider the delay and energy consumption of the algorithm itself. Then, [8] considered deploying satellite edge computing servers in nano-satellite constellations with small size and low cost, which makes it possible to build a satellite-terrestrial collaborative computing network in practice. Additionally, ref. [3] established a task-offloading model according to the offloading energy consumption, and optimized the model based on the optimization theory. Their simulation results demonstrate that the optimized model can effectively reduce the total energy consumption. Ref. [29] proposed a multi-index joint optimization task-offloading strategy based on game theory, which can effectively improve service quality. All these works can make reasonable offloading decisions, but they all ignore the possible privacy leakage problem during the offloading process. This makes them unable to meet the needs of privacy-sensitive services.