esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Scheme for Modulation of Data Transmission Module in Endoscopic Capsule
## 3 Performance Analysis  
### Energy Efficiency (EE), and Spectral Efficiency (SE)  
We assume that all systems utilize the same chaotic signal with an \(R\)-length and unit energy as a reference. In the proposed scheme, the symbol energy \(E_{k}\) is expressed as  
\[E_{k}=E_{data1}+E_{data2}+\ldots+E_{ref}, \tag{14}\]  
where \(E_{ref}\) is the energy of the reference sequence. Thus, \(E_{data1}=E_{data2}=\ldots=E_{ref}\) and \(E_{k}=(q+1)E_{ref}\). It is obvious that \(E_{ref}\) is used to transmit the reference signal while \(E_{k}\) is used to transmit \(m_{c}+tn_{c}\) bits. Thus the energy required for transmitting one bit, which is compared with reference part \(E_{ref}\), is:  
\[E_{b}=\frac{E_{k}}{m_{c}+qn_{c}}, \tag{15}\]  
Subsequently, the energy efficiency (EE), i.e., the number of bits per unit of data energy, can be written as  
\[EE=\frac{E_{data1}+E_{data2}+\ldots E_{datat}}{E_{b}}=\frac{t(m_{c}+qn_{c})}{( q+1)}. \tag{16}\]  
Table 1 presents the energy efficiency (EE) of various chaotic modulation schemes. As observed in the table, DCSK transmits only a single bit across two time slots, resulting in an EE of 1/2. In contrast, the traditional MDCSK transmits \(m_{c}\) bits within the same time period. The table also highlights that 2 IP-MDCSK achieves a significantly higher EE compared to IP-MDCSK, MCS-DCSK, and MDCSK. Furthermore, the EE values for these schemes improve as the value of M increases.  
A comparison of spectral efficiency (SE) between the proposed MIP-MDCSK and other relevant schemes is provided in Table 2. Let B represent the bandwidth. The SE for MIP-MDCSK is given by equation \(SE_{MIP-MDCSK}=\frac{m_{c}+tn_{c}}{R(1+q2^{m_{c}+1})B}\)[17]. Similar expressions for the SE of the other schemes are also included in the table. The results indicate that the proposed MIP-MDCSK consistently offers a higher SE than IP-MDCSK, and it outperforms MCS-DCSK when the parameters are appropriately chosen. However, it falls short of traditional MDCSK in terms of SE. In conclusion, the proposed system achieves a balanced trade-off between SE, EE, and BER performance.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & & **EE** & \\ \hline
**Modulation** & **MIP-MDCSK** & **IP-MDCSK** & **MCS-DCSK** & **MDCSK** \\ \hline expression & & & & \\ \(\frac{q(m_{c}+tn_{c})}{t+1}\) & \(\frac{m_{c}+n_{c}}{2}\) & \(\frac{n_{c}+1}{2}\) & \(\frac{m_{c}}{2}\) \\ numerical value & & & & \\ set \(m_{c}=2,n_{c}=2,t=2\) & 8 & 4 & 1.5 & 1 \\ set \(m_{c}=3,n_{c}=3,t=3\) & 12 & 3 & 2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of energy efficiency (EE).  
Figure 2: Block diagram of the MIP-MDCSK scheme for data transmission module of Endoscopic Capsule.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Military and Defense  
Drones are used for surveillance, reconnaissance, and combat missions. They can gather intelligence, carry out strikes, and perform other tasks without putting human lives at risk [26].
## 6 Future Research Directions  
We will discuss and outline potential future research directions here. Future research should focus on making NOMA technology smarter, more versatile, and easier to implement. The following are some directions that research into deep learning's application to NOMA systems may go in the future.
# Improved Autocollimator for Roll Angle Measurement with an Enlarged Measuring Range
## 2 Methods
### Measurement of Roll Angle
\tag{6}\]  
Following the previous analytical method, the directing vector of the beam reflected by the WP's front is calculated as  
\[{A_{1}}^{\prime}=[0\ 0\ 1]^{\mathrm{T}}. \tag{7}\]  
Figure 2: Roll angle measurement: (**a**) principle and (**b**) light spots on CMOS before and after WP’s rolling. Meanwhile, the directing vector of the beam experiencing refractions by the WP's front and back, reflection by the PM, and repetitive refractions by the WP's back and front is calculated as  
\[{A_{6}}^{\prime}=[2\theta(n-1)\cos\gamma\;2\theta(n-1)\sin\gamma\;1]^{\text{T}}. \tag{8}\]  
When comparing the directing vectors \(A_{1}\), \(A_{6}\), \({A_{1}}^{\prime}\), and \({A_{6}}^{\prime}\), that is, Equations (2), (4), (7) and (8), it can be observed that before and after the WP rolls, light spot 1 stays at the origin of the CMOS, while light spot 2 rotates around the origin, whose moving path can be expressed as the parameter equation system of the circle:  
\[ \begin{cases}x=L\times\cos\gamma,\\ y=L\times\sin\gamma,\end{cases}  \tag{9}\]  
where \(L=\)\(f\times 2\theta(n-1)\) signifies the rotation radius of light spot 2; and \(f\) signifies the focal distance of the OL. As shown in Figure 2b, if the WP rolls by an angle \(\gamma\), light spot 2 will rotate around the origin by the same angle. Thus, it is recapitulated that the roll angle of the WP is equivalent to the rotation angle of the straight line determined by the origin and light spot 2. As long as the CMOS detects the coordinates of light spot 2, the roll angle of the WP can be calculated.
# Seismic Hazard Evaluation and Strain Dynamics in the
## 4 Discussion and Findings
### Comprehensive Evaluation of Seismic Hazard Components  
When all findings are evaluated together within this study, the obtained high \(a\)-value, low \(b\)-value, and calculated recurrence periods collectively demonstrate that the current tectonic stress in the region is gradually released through energy distribution via small-scaleearthquakes; however, the potential for generating large-magnitude seismic events and the persistence of regional seismic risk remain significant. Spatiotemporal energy density analyses reveal that the energy distribution, which was predominantly concentrated around the GFZ in the early 1900s, shifted toward the Simav segment and the EF to the north during the earlier 1970s. Although energy density has been predominantly concentrated in the central segment of the SFZ from 1970 to 2025, following the 1970 Gediz earthquake and the 2010s Simav earthquakes, the seismic silence at the intersection of the GFZ and SFZ, in light of earlier seismic activity and the strain rates obtained from this study, raises significant concerns. The current strain rates--reaching values as high as approximately 90 ns/yr in the western Smudrgi segment--especially at the eastern and western ends of the Simav Fault Zone (SFZ), along with the westward shift of seismic activity from the eastern Afyon-Aksehir Graben (AAG) and a history of destructive earthquakes in the area, collectively suggest that future major seismic events in this zone are most likely to occur in its eastern and western segments. Even with the limited catalog used in this study and the strain values derived from the non-uniformly distributed GNSS network around the Simav Fault Zone (SFZ), the findings suggest that the eastern and western segments of the SFZ exhibit a high level of seismic hazard potential. However, given the relatively sparse distribution of 20 GNSS stations across a 205 km-long fault zone and the limitations of a 120-year earthquake catalog, it is concluded that detailed monitoring studies specific to the Simav Fault Zone (SFZ) should be conducted. In addition to seismic hazard assessments, comprehensive seismic risk analyses are also deemed necessary.
Hydrodynamic Simulation of the Semi-Submersible Wind Float by Investigating Mooring Systems in Irregular Waves  
[PERSON]  
1 Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University,  
Tainan City 70101, Taiwan; [EMAIL_ADDRESS]  
1  
[PERSON]  
1 Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University,  
Tainan City 70101, Taiwan; [EMAIL_ADDRESS]  
1  
Footnote 1: email: {yu-h.li/li  
or quasi-static methods. In this method, the quasi-static mooring model involves an analytical derivation of the mooring line tensions. The mooring force consists of the pre-tension force and the linearized mooring force [6]. The pre-tension force is measured using excess buoyancy when the platform is un-displaced. The linearized mooring force can be initially analyzed by the restoring matrix. Nevertheless, the linearized mooring force is not realistic in nature because the mooring systems in most cases are nonlinear. [PERSON] et al. [7] indicated that the tension calculated by the quasi-static mooring model could be overestimated in the operational sea condition, but would be underestimated in the severe sea condition. To improve accuracy, there is an expansion of the quasi-static model called the multi-segmented, quasi-static (MSQS) model. [PERSON] et al. [8] clarified that MSQS allows one to model the nonlinear stiffness matrix and static forces of practical mooring systems with arbitrary connection geometries and profiles. Combining several individual catenary cables, the MSQS model has established the foundation for dynamic mooring analysis.  
The dynamic mooring model uses discretization along the cable length and an assembly of the mass and internal forces, which is called the lumped mass method (LMM) [9]. The mooring lines are modeled as a chain of Morison-type elements. Each cable element considers tension force, shear force, and the bending moment. Because the hydrodynamic forces on each cable element are time-variant, the dynamic mooring model is more accurate than the quasi-static mooring model theoretically. In addition, the seabed in the dynamic model is comprised of nonlinear springs and dampers for minimizing discontinuities and energy losses due to the discretization.  
According to our previous research [10], the modular simulation system is well developed and can be used to estimate hydrodynamic forces for different types of floating offshore wind turbines with the quasi-static mooring lines in waves. Furthermore, this study attempts to represent the hydrodynamic coefficients and the motion responses of the semi-submersible using a numerical simulation based on the linear potential flow theory and the assumption of the 2 nd order wave exciting force. Compared to the dynamic mooring system, the motion responses of a platform moored using quasi-static cables are discussed in detail. The simulation results will be compared with published data for verifying accuracy as well.
# Tries-Based Parallel Solutions for Generating Perfect Crosswords Grids
## 3 Perfect Crosswords Grid Generation Problem
### Backtracking-Based Methods
#### 3.3.1 A Search Algorithm with Predefined List of Prefixes  
**Definition 1**.: _A group of \(l\) letters is an admissible \(l\)-prefix in the dictionary \(D\) if there is at least one word in \(D\) that starts with that group of letters._  
Let \(w=(w_{0},w_{1},...,w_{n-1})\) be the letters of the given word; the letters \(w_{0},w_{1},...w_{n-1}\) are \(1\)-prefixes for the words in each row.  
Going further, the solutions could consider:  
* \(L_{2}\)--the set of all \(2\)-prefixes;
* \(L_{3}\)--the set of all \(3\)-prefixes;
* \(L_{4}\)--the set of all \(4\)-prefixes;
*...
* \(L_{n-1}\)--the set of all \(n-1\)-prefixes.  
These lists of prefixes could be computed and stored in the first stage of the computation. These could be solved in a single traversal of the dictionary, but how these lists are represented could influence the final time complexity. This stage could be parallelized by applying a data decomposition of the dictionary.  
We schematize and analyse a word-by-word backtracking solution that uses prefixes with a maximal length equal to \(l\) and stored into linear lists. We emphasize here only the solutions domain, the continuation conditions and the solution verification:  
* The solutions are formed from the tuples \(X_{1},...,X_{n-1}\), where \(X_{j}\) is a word from the set \(S_{n}\) to be put in the column \(j\).
* The continuation conditions for the level \(k\) are:
* If \(k\leq l\) then all the obtained prefixes (on each row) should be admissible in the dictionary \(D\) (belongs to \(L_{k+1}\)). The prefix in row \(i\) is obtained in the following way: \(x_{i,0}x_{i,1}x_{i}x_{i,2}...x_{i,k}\), where \(x_{i,j}\) is the letter \(i\) of the word \(X_{j}\).
* If \(k>l\), then no other conditions are verified.
* Solution verification: verify that the words on each row belong to the dictionary \(D\).  
For a backtracking solution the time complexity is difficult to calculate rigorously, but we tried to make an estimation. We denote with \(M_{i}\) the average number of words from \(S_{n}\), for which, when placed in the column \(i+1\), the resulting \((i+1)\)-prefixes are admissible; for each \(k>l\), \(M_{k}=M_{n}\). If we ignore the time complexity for the verification of the continuation conditions, we may approximate the number of possible choices that need to be verified as being \(M_{1}\times M_{2}\times\cdots\times M_{n-1}\).  
In order to verify that a word is admissible to be placed in the \(i\)-th column, we need \(n\times l_{i}\) operations (\(l_{i}\) is the size of the list \(L_{i}\)), which verifies that the new created prefixes are admissible.  
Parallelisation is possible based on the decomposition of the set of all words that could be placed in the second column (\(S_{n}\) with \(L_{2}\) prefixes), and so the time complexity could be reduced by a factor equal to \(M_{1}\).
# Satellite Network Task Deployment Method Based on SDN and ICN
## 3 System Model and Problem Formulation
### Task Model
#### 3.4.2 Delay in Task Data Transmission  
The transmission of the data required by the task to the task deployment node needs to be transmitted by multiple satellites. The communication bandwidth between any two satellites is, however, different. Therefore, they need to be calculated separately. Let \(M=\{m_{1},m_{2}\ldots m_{n}\}\) be the collection of all satellite numbers in the network. If there is a link between the satellite nodes numbered \(m\) and \(n\), the transmission rate between the satellite nodes numbered \(m\) and \(n\) is \(r_{m}^{n}\). Therefore, the delay to complete the transmission of a data volume of \(W^{m}\) is:  
\[T_{m}^{n}=\frac{W^{m}}{r_{m}^{n}},\ \forall m,n\in M \tag{6}\]  
Using the whole network of an SDN, the path that the task needs to take is then determined and the transmission delay between any two satellites can be calculated by (6). Assume that the satellite number is \(m\), \(n\), \(b\)... \(x\); \(h\) is the route, the forwarding times are \(k\), and the communication delay is:  
\[Z_{k}=T_{m}^{n}+T_{n}^{b}+\ldots+T_{x}^{h} \tag{7}\]  
Because the transmission is synchronous, the transmission delay is the longest subtask transmission time. Assume that the forwarding times of a single subtask are \(O_{1}\), \(O_{2}\)... \(O_{n}\), then the transmission delay is:  
\[Z_{n}^{2}=\max\{T_{o_{1}},T_{o_{2}}\ldots T_{o_{n}}\} \tag{8}\]
## 6 Conclusions  
1. The vegetation NDVI in Ordos City shows an obvious increasing trend, and the vegetation growth has been greatly improved in 21 years. The interannual growth trend of vegetation NDVI is the largest (0.0075/a), and the growth trend in autumn is (0.0067/a). The area of improving vegetation NDVI in autumn is 72.76%, that in summer is 54.18%, and that in spring is 69.50%. The areas of unstable vegetation NDVI are mainly distributed in the central part of Hangjin Banner and Wushen Banner.
2. A total of 80.94% of the regional Hurst index in Ordos is between 0.3 and 0.5, and the average index value is 0.43, showing an opposite development trend of that in the past. The future change of vegetation NDVI is divided by the change trend, and the trend of future change of vegetation NDVI is mainly anti-continuous improvement, and the area of the anti-continuous improvement accounts for 82.88% of the interannual change. Among three seasons, the percentage of anti-continuous improvement in summer is relatively small, but it also reaches 79.14%. If no effective measures are taken in Ordos City, the vegetation NDVI will be degraded extensively in the future.
3. As a typical arid and semi-arid region, the vegetation growth in Ordos City is mainly influenced by precipitation. Precipitation plays a dominant role in promoting the growth of vegetation, and, generally, it is mostly a direct effect. The comprehensive effect of precipitation on vegetation NDVI in spring, summer, autumn, and the whole season was 0.526, 0.552, 0.489, and 0.556, respectively. Temperature plays an inhibitory effect on the growth of vegetation. In spring and summer, the inhibitory effect is deduced from the indirect effect. In contrast, in autumn, the inhibitory effect was produced through direct effect. The comprehensive effect of temperature on vegetation NDVI in spring, summer, autumn, and the whole season was \(-\)0.235, \(-\)0.471, \(-\)0.142, and \(-\)0.205, respectively.
4. The spatial distribution pattern of vegetation NDVI in Ordos City is inseparable from the land use pattern, and the land use pattern largely determines the spatial distribution of vegetation NDVI. The improvement of vegetation NDVI is obvious in land use types such as grassland and unutilized land. The improvement of vegetation mostly occurs in the areas where the land use type has not changed; therefore, stable land use is favorable to the improvement of vegetation.  
This research was funded by National Key R&D Program of China (grant number 2018 YFD1100101) and National Natural Science Foundation of China (grant number 42001131).  
Not applicable.  
All the data analyzed/generated are included and available in the manuscript.  
The author declare no conflict of interest.
# Virtual Replication of IoT Hubs in the Cloud:
## 4 Experimental Analysis
### Performance Evaluation
Unlike a VM-based approach, container-based IoT Hub instances can be instantiated and removed dynamically, depending on the instantaneous load and without affecting the host machine or requiring infrastructure-related efforts (such as file/template management, configuration and activation of a new machine).
## 2 Materials and Methods
### Back Trajectory Analysis  
Back trajectory analysis was performed with HYSPLIT Model-Trajectory Frequencies of the National Oceanic and Atmospheric Administration, USA [20,21] in order to grasp the long-range transport of air masses. The results from this model show the frequency that the trajectory passed over a grid cell taking into account residence time. The main analysis conditions were the same as the previous report [19].
## 2 Methodology
### Bayesian Optimization Methods
#### 2.2.1 Gaussian Process Estimation  
The set [\(L(p_{1})\), \(L(p_{2})\), \(\cdots\), \(L(p_{s})\), \(\cdots\), \(L(p_{r})\)] was obtained by selecting r samples in [\(p_{1}\), \(p_{2}\), \(\cdots\), \(p_{s}\), \(\cdots\), \(p_{r}\)] and calculating the loss function of the calculated and observed values of the model water level corresponding to the vector of the hydraulic conductivity. A new set of observation points \(D_{r}\) = {(\(p_{s}\), \(L(p_{s})\)), \(s\) = 1, 2, \(\ldots\), \(r\)} could then be constructed. The \(D_{r}\) obeyed the Gaussian distribution as shown in the Equation (1):  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value/Range** \\ \hline Hydraulic conductivity (m\(\cdot\)d\({}^{-1}\)) & 0.01\(\sim\)15 \\ Specific yield (\%) & 0.2 \\ Longitudinal dispersivity (m) & 20 \\ Horizontal dispersivity (m) & 4 \\ Pollution source concentration (mg\(\cdot\)L\({}^{-1}\)) & 500–9000 \\ The pollution sources leaking time (d) & 1–1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pollution source and aquifer parameters.  
Figure 2: Overview of the chemical industry park.  
\[\left\{\begin{array}{l}L(p_{s})\,\sim N(0,K)\quad s=1,2,\cdots\,,r\\ \\ K=\left[\begin{array}{c}k(p_{1},p_{1})\cdots k(p_{1},p_{r})\\ \vdots\qquad\vdots\\ k(p_{r},p_{1})\cdots k(p_{r},p_{r})\end{array}\right]\end{array}\right. \tag{1}\]  
where: \(k(p_{r},p_{r})\) is the kernel function, representing the covariance and \(K\) is the covariance matrix.
## 1 Introduction  
High-speed-railway tunnels are prone to various problems such as lining cracks and falling blocks [1; 2; 3] owing to many factors such as a complex geological environment and construction defects. Two main forms of tunnel lining spalling blocks are encountered: recent mortar, flaky blocks located at the lining surface layer and blocky, concrete blocks located at the construction joint or joint of the tunnel lining [4]. The main spalling position is the vault, as shown in Figure 1. Under the repeated action of inducing factors such as the vibration load of high-speed trains and aerodynamic pressure in the tunnel, lining blocks may spall off during the train operation period, affecting driving safety (see Figure 1c). For example, on 12 January 2016, lining blocks comprising the construction joint of the vault associated with a tunnel in Pingshan in China spalled off (cement block size: length: 10 cm, width: 5.5 cm, and thickness: 1.8 cm). This resulted in a train speed limit of 120 km/h and affected the normal running of the train for more than 2 h [5].  
Many studies have focused on the cause, mechanism, and crack propagation process of lining fractures through analytical formulas [6; 7; 8], theoretical analyses [9; 10; 11], field investigations [12; 13], numerical simulations, and model tests [14]. However, the influence of train aerodynamic factors remains unexplored. In addition, several studies have used the real vehicle test [15; 16], the model test [17], numerical simulations [18], and other methods. These works considered the distribution characteristics of the train surface, tunnel wall, train wind in the tunnel, and micropressure wave at the entrance of the tunnel. However, each study focused on the single motion of the train, and the gas-solid-coupling motion of falling blocks under a train wind environment was overlooked. The driving safety under different natural environments [19; 20; 21] and infrastructure [22; 23; 24; 25], as well as the safety of ancillary facilities in tunnels [26; 27], under a train wind environment have been investigated in various works. However, the influence of spalling and falling blocks of high-speed-railway tunnel linings on driving safety has yet to be investigated.  
In this work, a three-dimensional gas-solid-coupling calculation model of spalling blocks-train-tunnel-air was established on the basis of the basic theory of gas-solid interaction and the numerical simulation software, FLUENT [28]. The characteristics of the flow field structure change during the falling process of spalling blocks comprising a tunnel lining were determined. Based on these characteristics, a coupling effect during the falling process of spalling blocks under a train wind environment was analyzed, and the aerodynamic evolution mechanism of the lining blocks was obtained. Spalling blocks affected driving safety in different circumferential tunnel positions. Therefore, the influence partitions of these blocks on driving safety were obtained, identifying key prevention areas for spalling lining pieces in high-speed-railway tunnels.
## 3 System Overview
### Battery Voltage Measurement  
The actual battery voltage level is computed as  
\[Battery Voltage Level=\frac{ADC\ reading\times 3.3\ \mathrm{V}}{Maximum\ ADC\ reading\ (4095)}. \tag{5}\]  
The ESP32 analog input can measure voltage levels between 0 V and 3.3 V. The corresponding ADC reading range is from 0 to 4095. If the measured battery voltage is less than 3.1 V, the ESP32 publishes a \"0\" message to a MQTT topic, and the battery voltage status is shown as low on the Grafana dashboard. If the measured battery voltage is not less than 3.1 V, the ESP32 publishes a \"1\" message on the topic, and the battery voltage status is shown as good on the Grafana dashboard.