esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Stage Quantum Secure Direct Communication Using Secure Shared Authentication Key
## 5 Example of the Proposed Protocol
### Initial Authentication Procedure  
The initial authentication procedure is explained as follows:  
1. Suppose that [PERSON] prepares a photon \(\psi_{0}=\)\(|0\rangle\) and generates a random secret angle, \(\theta_{A}=1^{\circ}\). Based on the decimal value of \(\theta_{A}\), she converts it into binary digits \(|00001\rangle\) and employs Pauli-X quantum gate operation to produce \(|11110\rangle\). She converts it back to decimal value, \(\theta_{A}{}^{\prime}=30^{\circ}\). The new value will be used as the security check for the next procedure. She starts to encrypt \(X=\)\(|0\rangle\) using \(\theta_{A}\) and produce quantum state \(\psi_{1}\). The _TimeStamp_ is set to \(t_{A}(1)=0\).
2. [PERSON] generates a random secret angle \(\theta_{B}=15^{\circ}\) and encrypts it with the received quantum state \(\psi_{1}\) and produces \(\psi_{2}\). The _TimeStamp_ is set to \(t_{B}(1)=2\).
3. [PERSON] decrypts the received \(\psi_{2}\) with \(-\theta_{A}\) to generate \(\psi_{3}\) and sends it back to [PERSON]. At this stage, [PERSON] gets \(\theta_{B}=15^{\circ}\). [PERSON] will verify the _TimeStamp_\(=4\) after she gets the photon replied by [PERSON]. If [PERSON] gets the _TimeStamp_\(\
eq 4\), an eavesdropper is detected, and she will terminate the communication. Otherwise, the communication will be continued. The _TimeStamp_ is set to \(t_{A}(2)=4\).
4. [PERSON] applies \(-\theta_{B}\) to obtain \(\psi_{0}=\)\(|0\rangle\). He gets the value of \(\theta_{A}=1^{\circ}\) and converts it into the binary digits \(|00001\rangle\). Using the Pauli-X quantum gate, he gets \(|11110\rangle\), and he converts it back into decimal value and generates \(\theta_{B}=30^{\circ}\). He then encrypts it with the received quantum state \(\psi_{0}=\)\(|0\rangle\) to produce quantum state \(\psi_{4}\) and sends it to [PERSON]. The _TimeStamp_ is set to \(t_{B}(2)=6\).
5. [PERSON] authenticates [PERSON] by comparing and analyzing \(\theta_{B}\) using measurement. If \(\theta_{B}=\theta_{A}{}^{\prime}\), she sends the initial authentication key \(\Phi_{initial}=20^{\circ}\) that is generated using a random number generator and encrypts it with \(\theta_{B}=30^{\circ}\). If \(\theta_{B}\
eq\theta_{A}{}^{\prime}\), the communication will be terminated and need to be restarted.
6. Finally, [PERSON] couples the receiving \(\psi_{5}\) with \(\theta_{B}=30^{\circ}\) to get the authentication key \(\Phi_{initial}\).
7. It can be concluded that the security of the proposed scheme depends entirely on the initial authentication procedure, where the eavesdropping check and identity authentication are implemented at the same time.
# A Survey on Satellite Communication System Security
## 3 Background
### Satellite Constellation Characteristics
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **LEO** & **MEO** & **GEO** \\ \hline Altitude & 500–1200 km & 2000–20,000 km & 36,000 km \\ \hline Propagation Delay & Approx. 30 ms & Approx. 125 ms & Approx. 600 ms \\ \hline Earth Coverage & Small & Large & Very Large \\ \hline Data Loss & Low & Low & Medium \\ \hline Required Satellite & At Least Tens to Hundreds & At Least 6 & At Least 3 \\ \hline Gateway & Numerous Flexible & Local Flexible & Stationary \\ \hline Orbital Period & Approx. 85–120 min (11–17 round & \multirow{2}{*}{Approx. 120–480 min} & \multirow{2}{*}{24 h} \\  & per day) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of satellite constellation. The advantages of LEO are as follows. Firstly, its proximity to the Earth results in a lower communication latency, which is advantageous for real-time communications. Additionally, this closeness to Earth aids in enhancing data transmission speeds. Secondly, LEO satellites are in a lower orbit compared to GEO satellites, which reduces the cost per launch. Lastly, satellites in LEO are capable of collecting higher-resolution images and data [29]. However, there are disadvantages to LEO orbit. First, due to the low altitude of LEO satellites, they can only cover a small portion of the Earth at a time, necessitating a large number of satellites for global coverage. This requirement for a vast number of satellites increases the complexity and cost of the network. Second, LEO satellites are more susceptible to atmospheric drag, which can result in a shorter lifespan. Finally, the LEO orbit is crowded with space debris, posing a heightened risk of collisions.
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 4 Numerical Examples
### Numerical Results
Besides, a higher total cost is obtained in model \(\mathcal{M}3\) compared with model \(\mathcal{M}2\), which verifies that the facility capacities affect the determination of facility locations. Figure 5: Globally optimal facility locations in the planning area for Instance 26. (**a**) \(m=3\); (**b**) \(m=4\); (**c**) \(m=5\); (**d**) \(m=6\). Figure 6: Globally optimal facility locations in the planning area for Instance 27. (**a**) \(m=3\); (**b**) \(m=4\); (**c**) \(m=5\); (**d**) \(m=6\). Figure 7: Globally optimal facility locations in the planning area considering capacities for Instance 24. (**a**) \(m=3\); (**b**) \(m=4\); (**c**) \(m=5\); (**d**) \(m=6\). Figure 8: Relationship between the number of facility and objective function value in model \(\mathcal{M}\)1. (**a**) \(c\) = 0.10; (**b**) \(c\) = 0.15; (**c**) \(c\) = 0.20; (**d**) \(c\) = 0.25. Figure 9: Cross-validation of \(m\) and F on the total cost while \(c\) = 0.10. (**a**) Instance 24; (**b**) Instance 25; (**c**) Instance 26; (**d**) Instance 27. Figure 11: Cross-validation of \(m\) and F on the total cost while \(c\) = 0.20. (**a**) Instance 24; (**b**) Instance 25; (**c**) Instance 26; (**d**) Instance 27. Figure 10: Cross-validation of \(m\) and F on the total cost while \(c\) = 0.15. (**a**) Instance 24; (**b**) Instance 25. (**c**) Instance 26; (**d**) Instance 27. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Facilities**} & \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{\(\mathcal{M}1\)} & \multicolumn{2}{c}{\(\mathcal{M}2\)} & \multicolumn{2}{c}{-\(\mathcal{M}3\)} \\  & & **Facility Locations** & **Obj1** & **Facility Locations** & **Obj2** & **Facility Capacities** & **Facility Locations** & **Obj3** \\ \hline \multirow{3}{*}{3} & 1 & \((55,43)\) & & \((55,43)\) & & & & \((49,43)\) & \\  & 2 & \((21,39)\) & 2932 & \((21,39)\) & 3292 & 350 & \((63,79)\) & 3332 \\  & 3 & \((63,81)\) & & \((63,81)\) & & & \((21,39)\) & \\ \hline \multirow{3}{*}{4} & 1 & \((21,37)\) & & \((21,37)\) & & & \((21,37)\) & \\  & 2 & \((51,43)\) & & \((51,43)\) & & & \((51,43)\) & \\  & 3 & \((63,83)\) & & \((63,83)\) & & & \((81,43)\) & \\  & 4 & \((83,51)\) & & \((83,51)\) & &
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 5 Simulation and Experiment Results
### Experimental System and Initialization  
This subsection puts emphasis on the introduction of the experimental equipment being applied and their initialization settings. There are three instruments in the testing system: a HwaCreatTM GNSS signal simulator, an IF sampler, as well as a SDR (software-defined receiver). Their connection is shown in Figure 7. As aforementioned, we build up the scenario containing the lunar upper stage and a certain GPS satellite in the GNSS signal simulator. The radio frequency (RF) export of the simulator is connected to the IF sampler, so that the simulated signals can be collected and stored in the sampler. The sampler is capable of playback to ensure that the recorded data can be used for testing repeatedly, and make it possible that sufficient data can be obtained for statistical analysis. Finally, the digitized IF data is delivered into the SDR for processing. Note that the SDR is designed as three different structures in the following order: SSV RRAM, adaptive four-state KF-based structure illustrated in Figure 3, and INS-assisted structure illustrated in Figure 4.  
For the conventional RRAM of CL form, the bandwidth of DLL, FLL, and PLL are 0.1 Hz, 2 Hz, and 18 Hz, respectively. The IF of the SDR is set at 4.092 MHz, whereas the sampling frequency is 16.368 MHz.  
For the adaptive four-state KF-based strategy of CL form, the initial covariance matrix \(\mathbf{P}_{0}=diag(P_{00},P_{11},P_{22},P_{33})\), where \(P_{00}=1 chip^{2},P_{11}=1,P_{22}=(50 Hz)^{2},P_{33}=(1 Hz/s)^{2}\), and the initial state \(\mathbf{X}_{0}=(0,0,0,0)^{T}\). Note that the values of the initialization can only affect the convergence speed of the filter instead of the estimation accuracy.  
For the INS-assisted strategy of the OL form, the local Doppler frequency is predicted with the help of the input IMU measurements and GPS ephemeris. There are four different grade IMUs being used in the experiments: MEMS (micro electro mechanical system) grade, civil grade, tactical grade, and navigation grade. Their parameters are provided in Table 4. If the initial misalignment error is \(\delta a_{0}\), the cumulative acceleration error can be modeled as a simplified form:  
\[\delta a_{LOS}=(\delta a_{0}+\
abla+g\cdot\varepsilon\cdot\Delta t)\text{cos}\theta \tag{39}\]  
where \(\
abla\) is the accelerometer error, \(\varepsilon\) is the gyro error and \(\Delta t\) is the drift time after the latest IMU calibration [26], and \(\theta\) is the real-time projection angle between the IMU measurement vector and the LOS direction. In the case when the IMU correction to prevent drift error is unavailable, the estimated frequency error over drift time is:  
\[\delta f_{d}=-\frac{f_{L1}}{c}\int_{0}^{\Delta t}\left(\delta a_{0}+\
abla+g \cdot\varepsilon\cdot\tau\right)\text{cos}\theta(\tau)d\tau \tag{40}\]  
Figure 7: The distribution and connection of the instruments in the experiment. (**a**) Physical connection; and (**b**) schematic connection.
## 4 Power Control For Multiple Sink Cluster WSNs (MSCWSNs-PC)
### Most significant bit sink radius correction (MSBSRC)  
In original MSCWSNs-PC, sink calculates its radio radius always according to the radio range of previous sink. It often induces a border communication void problem. As shown in Figure 7, the border \(|mn|\) is defined as border communication void. For this reason, we provide most significant bit sink radius correction (MSBSRC) scheme. After each sink working out its radio radius using original MSCWSNs-PC, then each sink has highest order in each group decides its radius by comparing \(R_{ij}\) and \(Distance(S_{i,j}\), _vert\({}_{i})\)_, \(i\!\in\!M\), where \(Distance(m,n)\) denotes the distance between \(m\) and \(n\), and \(vert_{i}\) is the vertices set of rectangle work area, \(i\!\in\!\{\)_Topside_, _Underside_, _Lefside_, _Rightside_\(\}\) and \(j\!\in\!\{\)\(numsink_{\textit{Topside}}\)\(numsink_{\textit{Underside}}\), \(numsink_{\textit{Lefside}}\), \(numsink_{\textit{Rightside}}\}\). Set \(w(x_{w}y_{w})\) is a sink node has highest order wants to decide its radius.  
```
0:for all\(w\),
0:// w \(\in\!\{\)\(\textbf{max}\{(X_{\textit{Topside}},Y_{\textit{Topside}})\}\), \(\textbf{max}\{(X_{\textit{Underside}},Y_{\textit{Colorside}})\}\),
0:// \(\textbf{max}\{(X_{\textit{Lightside}},Y_{\textit{Lightside}})\}\), \(\textbf{max}\{(X_{\textit{Rightside}},Y_{\textit{Rightside}})\}\) }
0:if\(R_{ij}\!>=\) distance (\(S_{i}\), _vert\({}_{i}\)_) then
0:// i \(\in\!\{\)_Topside_, _Underside_, _Lefside_, _Rightside_\(\}\)
0:// j \(\in\!\{\)\(numsink_{\textit{Topside}}\)\(numsink_{\textit{Underside}}\), \(numsink_{\textit{Lightside}}\)\(numsink_{\textit{Rightside}}\)\(\}\)
0:\(R_{w}\!=R_{ij}\)
0:else
0:\(R_{w}\!=\) distance (\(S_{i}\), _vert\({}_{i}\)_)
0:endif
1:endfor
```  
**Algorithm 2**\(w\) decides its radius by comparing \(R_{ij}\) and \(d(S_{i,j}\), _vert\({}_{i})\)  
_Distance(\(m\),\(n\))_ computes the euclidean distance between nodes \(m\) and \(n\) in two dimensions, \(\sqrt{\left(x_{n}-x_{n}\right)^{2}+\left(y_{n}-y_{n}\right)^{2}}\).
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 4 The Effect of User Behaviour on HWLNets Performance
### Device Orientation following Light Blockades  
User equipment for LiFi communication must have a type of photodiode (PD) capable of detecting an interpreting light. PDs are small and have a narrow field of view, therefore they cannot detect light in all angles. Data transfer rates and QoS service in VLC are measured in luminosity in contrast to direction of the equipment towards the source of emission. This is a very complex issue that was first researched in [46] with the assumption that randomly oriented devices will follow [PERSON]'s rotation theorem. [PERSON]'s rotation theorem states that in three-dimensional space, any displacement of a rigid body such that a point on the rigid body remains fixed is equivalent to a single rotation about some axis that runs through the fixed point. This type of movement can be best described by formulating three axial rotations in the Cartesian coordinate system, as shown in Figure 9. The researchers obtained rotational data from gyroscopes in smartphones [47], which in turn revealed that while fixed smartphones have Laplace distribution of rotation, smartphones in motion obey Gaussian distribution. Unconstrained smartphone orientation models are presented in [48] with the distinction that here the polar angle of the smartphone best suits Laplace distribution.  
Orientation changes can be researched from a different view such as in [49] using data measurements. In this research, the time over which the channel-gain-values correlation coefficient drops below a predefined threshold in random orientation is in milliseconds. For VLC systems where delay is in nanoseconds, this means that rotation can be managed as slowly-varying channels. The problems with recurring handovers in HWLNets are best described in [50] using a device orientation model with  
Figure 9: Axial rotation of smartphone.  
a practical framework for VLCs. This model is also used to describe load balancing and concurrent asset allocation for users [51].  
Blockage models are incorporated with a RWP model due to the fact that besides walls and objects such as furniture, humans carrying a device can also block light from reaching a PD. This type of blockage can be partial or full depending on the position of the user and device in accordance with LiFi AP and the number of users present in a room. In this model, humans are represented in a cylinder form [52]. These data have been statistically modelled [53] with a basepoint being on a frequency of blockades and its duration. There are a couple of hardware modifications that can be made to alleviate blockades such as LED transmitters with a wide angle to expand light broadcasting but with more interference [54] and omnidirectional receivers with PDs on each side [55]. In HWLNets, another option is for the SND controller (requires vertical handover) to switch users in and out of different networks due to blockades, thus solving this comprehensive problem.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 4 Numerical Application to Bridge  
In order to verify the applicability of the proposed approach for large-scale structures, the numerical example shown in Figure 5 was established. The structure was a pre-stressed concrete box girder bridge with length of 50 m, height of 3 m, and width of 16.8 m. The bridge was composed of 19 segments and the material density was 2500 kg/m\({}^{3}\). The damping ratio for the 1 st and 2 nd modes was 5%. A total of 50 strain sensors were installed longitudinally along the section bottom centerline at a spacing of 1 m, as shown in Figure 5b,c. This sensor layout can be achieved by only one quasi-distributed fiber optic sensor [18] based on resonance frequency mapping.  
The structure was modeled using 556 four-node shell elements giving a total of 569 nodes and 3414 DOFs. Identical elastic moduli were assumed for the elements inside each segment. Except for segments No. 6 and No. 11, the elastic modulus of the segments was 30 GPa and was set to a value of 27 GPa for segments No. 6 and No. 11 with 10% damage. The eigenvalue analysis of the structure gave a natural frequency of 2.083 Hz for the 1 st mode. The mode shape is shown in Figure 5d.  
Figure 4: Model updating process (**a**) when the strain was measured and (**b**) when the acceleration was measured.
## 2 Related Work and Data Origin
### Sensor Screening Method  
The data used in this work is the result of a detailed screening of 64 sensors exposed to nine different analytes. The sensors under test differ in their substrate composition as well as their sinter times and sinter temperatures used during their production. As the operating temperature greatly impacts the sensor performance, a single physical sensor operated at different temperatures can be regarded as multiple virtual sensors with very different sensitivity and selectivity [12]. The operating temperatures were therefore varied during the screening, to record the resulting impact on the sensor performance. Sensor resistance, heater voltage and heater current were sampled with 1 Hz during the entire screening. A single measurement for a sensor and an analyte at a given temperature is repeated at least three times before the temperature is changed and the cycle starts over. The resulting time series for each measurement in the described data set always consists of the following three segments and durations as defined by the screening procedure:  
Figure 1 is an exemplary depiction of the result from a single measurement. The dotted vertical lines indicate the analyte exposure to the sensor, while the toned down color of the curve, left and right of the dotted lines, is used to visualize the baseline and clearing segments as described in Table 1.
###### Abstract
Recently, ML approaches (random forest regression models) were implemented to predict the large reductions in air pollutants, i.e., PM\({}_{10}\), NO\({}_{2}\), O\({}_{3}\), during the COVID-19 lockdown period [83]. Furthermore, PM\({}_{10}\), NO\({}_{2}\) and carbonaceous aerosols (organic carbon, elemental carbon) were also used in ML techniques (Lasso, Random Forest, AdaBoost, Support Vector Machine and Partials Least squares) for analysing air pollutants at street canyons [84]. Studies dealing with PM estimations in Malaysia are rather limited [18; 30]. [PERSON] et al. [85] has reviewed PM\({}_{10}\) modelling studies in Malaysia, and only four studies used ML techniques in predicting PM\({}_{10}\). On the other hand, PM\({}_{2.5}\) studies are even fewer and most of them in Malaysia have been performed at small spatial scales [86; 87]. The current study is the first in Malaysia and one of the very few works conducted worldwide aiming to estimate the PM\({}_{2.5}\) concentrations at a large (national) scale using pollution gases, AOD and meteorological factors based on machine-learning techniques. In order to extend the spatial coverage to the whole country (both Peninsula and Island Malaysia) and aiming to improve the accuracy of PM\({}_{2.5}\) estimates, this study integrates hourly AOD products from Himawari-8 satellite sensor, along with meteorological parameters and gaseous pollutants using machine learning techniques, i.e., random forest (RF) and Support Vector Regression (SVR). The models were developed separately for urban/industrial, suburban/rural sites and for the four dominant seasons in order to better represent the spatial (between sites) and temporal (between seasons) variation of the PM\({}_{2.5}\) concentrations. Variable importance analysis was conducted to identify the primary parameters that affect the PM\({}_{2.5}\) concentrations in Malaysia in a way to develop regression models for estimations of PM\({}_{2.5}\). The performance of RF and SVR was evaluated at different seasons and locations against measured PM\({}_{2.5}\) concentrations. The results will assist in representing the spatial and temporal evolution of PM\({}_{2.5}\) in Malaysia and for establishing measures in a way to improve air quality across the country. Section 2 briefly describes the study area; Section 3 refers to the dataset that was used as variables in the prediction models; Section 4 refers to the PM\({}_{2.5}\) measurements across Malaysia. The results and model evaluation are included in Section 5, while Section 6 summarises the conclusions.
# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 3 Limitations in the Process of IUAVT Adaptation in the Sectors of Economy
However, the special search algorithms are necessary for the effective execution of tasks such as EoM MoEP, MoWAL, MoHGP, MoTES, and TrM. * **The task of organizing communication** in emergency zones with employment of UAVs is a part of a set of applications, including the early warning system, logistics, and data collection [141]. In relation to the applications described above, this task is primarily characteristic of MoHGP. A modern approach is to use a genetic algorithm (GA) with a steady-state population model (GAE) to find those optimal locations for the UAVs in order to maximize network coverage [142]. UAVs in such a situation can be quickly deployed as air base stations over the affected area, and provide communication with the victims;
* **The tasks of cargo delivery** with employment of UAVs are considered as an efficient option for solving logistics problems in the future. In particular, for the effective use of UAV resources, there proposed a method of delivery using flying assistants that are launched from the side of a truck (base) and fly only the \"last mile\", after which they return to the base at a new point in the shortest time [143]. The method increases the efficiency of multiple delivery in one region. The legal restrictions associated with the use of UAVs in an urban environment are proposed to be overcome in the future by the following way [144]: UAVs move along pre-prepared nodes at an acceptable height, without hindering the movement of future passenger drones and minimizing risks to the population. The task of cargo delivery can also be considered as part of the tasks solved within the framework of PA (application of fertilizers, herbicides, biophages), MoWAL (delivery of urgent goods), and MoTFS (delivery of spare parts and tools to repair zones).