esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 3 Reinforcement Learning-Based Routing
### Route Discovery  
After clustering, CHs will have complete information about its clusters. In this special scenario, nodes only need to communicate with the temporary CC, so CHs can keep the routing table for CC. To find this routing table, the CH will start the route discovery process with the destination always set as a CC in which the UAV can also act as a relay. Initially, the CH will broadcast the Route Request (RREQ), and the CHs that receives this RREQ will reply with a Route Reply (RREP). The routing tables are maintained at the CHs. If a packet is transmitted successfully, then acknowledgment will be received. If this is not the case, then it will re-transmit the packet. With three consecutive packet re-transmissions, the respective CH will start the route discovery again and if the problem persists, then the CH will declare the destination inaccessible. The route discovery process is again initiated after every 250 transmissions and the same steps are repeated.
# BDS Satellite Clock Prediction Considering Periodic Variations
## 2 Materials and Methods
### BDS Satellite Clock Prediction Model Considering Periodic Variations  
As a widely used and representative satellite clock prediction model, the SAM is expressed as:  
\[\begin{split} x_{i}=& a_{0}+a_{1}(t_{i}-t_{0})+a_{2}(t_{i}-t_{0})^{2}+\sum \limits_{r=1}^{l}A_{r}sin(2\pi f_{r}(t_{i}-t_{0}))\\ &\qquad+\,B_{r}sin(2\pi f_{r}(t_{i}-t_{0}))+\Delta_{i}\;(0\leq i \leq n),\;\overline{P}\end{split} \tag{2}\]  
where \(x_{i}\) is clock offset of epoch \(t_{i}\), \(t_{0}\) represents the reference epoch, and \(a_{0}\), \(a_{1}\) and \(a_{2}\) are clock offset (phase), clock velocity (frequency), and clock drift (frequency drift), respectively. \(l\) is the number of periodic terms, and \(A_{r}\), \(B_{r}\), \(f_{r}\) denote the amplitude and frequency of the periodic terms, respectively. \(\Delta_{i}\) is the residual of the model at epoch \(t_{i}\). \(\overline{P}=\textit{diag}(i(t_{i}-t_{0}))\), \(i=\)\((1,2,\ldots,n)\) is the weight matrix [26].  
Generally, parameters \(l\) and \(f_{r}\) in Equation (2) are determined first by spectrum analysis, and then other parameters such as \(a_{0}\), \(a_{1}\), \(a_{2}\), \(A_{r}\) and \(B_{r}\) are estimated using LS. From Section 2, there are multiple frequency components in BDS satellite clock offset periodic noise. Inappropriate \(l\) may easily cause overfitting, which adversely affects the prediction accuracy [3]. The current research generally adds one or two periodic terms in clock prediction model to compensate for the periodic noise [26; 27; 28]. Considering the SAM  
Figure 7: The spectrogram of C11 and C14 for WHU (**left**) and GFZ (**right**) clock offsets.  
with one periodic term is commonly used in BDS ultra-rapid clock products [26], we set \(l\) to 1 in this paper. The accurate frequency parameter \(f_{r}\) is critical for the fitting and prediction accuracy of the SAM. From the previous analysis, the amplitude spectrum calculated by FFT is a global spectrum analysis result and cannot give the periodic variations in time dimension. Awareness of time-varying periodic term can be used to improve the clock prediction model, in this paper, the STFT is utilized to detect and quantify the frequency parameter \(f_{r}\) of the main periodic term. Based on constantly updated clock offsets, the time-varying amplitude spectrum can be obtained by STFT, and the time-varying parameter \(f_{r}\) can be determined accurately. Since the periodic term coefficient is obtained by STFT, the improved clock prediction model is referred to as the time-frequency analysis model (TFAM).
# Boat-to-Grid Electrical Energy Storage Potentials around the
## 5 Conclusions  
There is more and more proof that the bidirectional smart connection between vehicles and electric energy networks in V2G systems can create a win-win situation for utilities and suppliers and the owners of the vehicles, in which the former can manage their energy resources in a better way, while the latter can generate some extra income by selling the power they feed back into the network. The vehicles themselves double as means of transport when needed and energy storage facilities when connected to the grid. The rapidly spreading and large-scale deployment of electric energy produced with gradually decreasing carbon emissions plays an extremely important part in achieving Hungary's climate policy targets. As a result, sustainable batteries and V2G solutions will have an increasingly decisive role in satisfying the thusly growing demand for electric energy.  
As seen above, thanks to the existence of Lake Balaton, the boat-to-grid concept, as part of the V2G technology, represents a considerable potential for storing energy in Hungary. The results of this study suggest that the small electric boats kept in the sailing marinas around the lake have a significant aggregate energy storage capacity, which is worth utilizing for the more optimized operation of the grid-connected solar power plants and household-sized PV systems located at Lake Balaton. It is expected that by 2030 this storage size will exceed that of today's (2021) largest battery energy storage system in Hungary substantially, even 2.6 times. Such a storage capacity would be suitable for the regulation of a PV system of even up to 78.1 MWp. Hungary's current economic environment, favorable to the spread of small electric boats, will contribute more and more effectively to the development of the green infrastructure of the lake, and if suitable incentives are in place, these small electric vessels will become an integral part of virtual generation integration, local energy communities and microgrid solutions, since they provide an effective and--rather importantly--cost-effective potential for creating novel types of flexibility services.  
[PERSON] conceived, designed and performed the experiments. All authors contributed equally in the analysis of the data and the writing and revision of the manuscript. All authors have read and agreed to the published version of the manuscript.  
We acknowledge the financial support of Szechenyi 2020 under the EFOP-3.6.1-16-2016-00015.  
Not applicable.  
The data presented in this study are available within the article.  
We acknowledge the financial support of Szechenyi 2020 under the EFOP-3.6.1-16-2016-00015.  
The authors declare no conflict of interest.
# Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
## 3 Bit Error Probability  
### Error Probability for AWGN channels  
From (2), we can derive a random variable such that \(\rho_{i}=|\phi_{i}|_{i\
eq k}\). Since \(\phi_{i}\) is a complex zero-mean Gaussian noise process, then \(\rho_{i}\) will be a Rayleigh random distributed variable such that [12]  
\[F_{\rho_{i}}(x)=1-\exp\left[-\frac{x^{2}}{2\sigma^{2}}\right], \tag{5}\]  
where \(\sigma^{2}=\frac{N_{0}}{2}\) and \(N_{0}\) is the noise spectral density. The symbol error probability is given such that  
\[P_{e|k}=\Pr\left[\max_{i,i\
eq k}(\rho_{i})>\beta_{k}\right], \tag{6}\]  
where \(\beta_{k}=|\sqrt{E_{s}}+\phi_{k}|\) follows a Rician distribution with shape parameter \(K=\frac{E_{s}}{2\sigma^{2}}=\frac{E_{s}}{N_{0}}\). In this way, the probability density function of \(\beta_{k}\) can be written as  
\[f_{\beta_{k}}(y)=\frac{y}{\sigma^{2}}\exp\left[-\frac{(y^{2}+E_{s})}{2\sigma^ {2}}\right]I_{0}\left(\frac{y\sqrt{E_{s}}}{\sigma^{2}}\right). \tag{7}\]  
The random variable \(\rho=\max(\rho_{i})|_{i\
eq k}\) is defined as the maximum of \((2^{SF}-1)\) i.i.d. Rayleigh random variables. Since the \((2^{SF}-1)\) variables are independent, the cumulative distribution function for \(\rho\) can be given as [12]  
\[F_{\rho}(x)=\left[1-\exp\left[-\frac{x^{2}}{2\sigma^{2}}\right]\right]^{2^{SF} -1}. \tag{8}\]  
Using (6)-(8), and equally probable symbols, we can express the average bit error probability \(P_{b}\) as  
\[P_{e|k}=\int_{0}^{\infty}\left[1-\left[1-\exp\left[-\frac{y^{2}}{2\sigma^{2}} \right]\right]^{2^{SF}-1}\right]f_{\beta_{k}}(y)dy. \tag{9}\]  
Expression (9) is the average bit error rate probability for AWGN channels. We can further simplify (9) using the [PERSON]'s binomial identity  
\[1-(1-e^{x})^{N}=\sum_{q=1}^{N}\binom{N}{q}(-1)^{q+1}e^{\alpha q}, \tag{10}\]  
then (9) can be written as  
\[P_{e|k}=\sum_{q=1}^{2^{SF}-1}(-1)^{q+1}\binom{2^{SF}-1}{q}\int_{0}^{\infty}e^ {-\frac{\alpha q^{2}}{2\sigma^{2}}}f_{\beta_{k}}(y)dy. \tag{11}\]Now, if we replace (7) into (11), we have  
\[\begin{split} P_{e|k}&=\sum_{q=1}^{2^{SF}-1}(-1)^{q+1} \binom{2^{SF}-1}{q}e^{-\frac{E_{s}}{2\sigma^{2}}\frac{q}{q+1}}\times\\ &\int_{0}^{\infty}\frac{y}{\sigma^{2}}I_{0}\left(\frac{\sqrt{E_{s }}y}{\sigma^{2}}\right)e^{-\frac{(q+1)^{2}+\frac{E_{s}}{q+1}}{2\sigma^{2}}}dy. \end{split} \tag{12}\]  
Fortunately, the integral in (12) can be computed as  
\[\int_{0}^{\infty}\frac{y}{\sigma^{2}}I_{0}\left(\frac{\sqrt{E_{s}}y}{\sigma^{2} }\right)e^{-\frac{(q+1)^{2}+\frac{E_{s}}{q+1}}{2\sigma^{2}}}dy=\frac{1}{1+q}, \tag{13}\]  
Then, using (4), we find a closed-form expression for the error probability given the symbol \(k\)[12]  
\[P_{e|k}=\sum_{q=1}^{2^{SF}-1}\frac{(-1)^{q+1}}{q+1}\binom{2^{SF}-1}{q}\exp \left[-\frac{q}{q+1}\gamma\cdot 2^{SF}\right]. \tag{14}\]  
Then, according to [12], we finally find the average bit error rate such that  
\[P_{b}=\frac{2^{SF}-1}{2^{SF}-1}\sum_{q=1}^{2^{SF}-1}\frac{(-1)^{q+1}}{q+1} \binom{2^{SF}-1}{q}\exp\left[-\frac{q}{q+1}\gamma\cdot 2^{SF}\right]. \tag{15}\]
## 4 Discussion
### Comparison of Clustering Effect between Incremental DBSCAN and DBSCAN  
In order to check the improvement of the incremental DBSCAN in the quality and speed of trajectory clustering, DBSCAN and incremental DBSCAN were used to cluster trajectories based on the same distance scale to see the cluster quality and speed.  
Regarding cluster quality, since DBSCAN did not delete the expired trajectories, clusters still existed after the prohibition of the channel of the Yushan Bridge (Figure 14). On the contrary, incremental DBSCAN could delete expired trajectories and clusters according to the TWW, so it would not keep the old traffic pattern all the time. Additionally, with the increase of noise trajectory, DBSCAN might merge irrelevant independent clusters. For example, the cluster in Figure 15 merged the cluster 3-6 (Figure 10) and the cluster 1-4 (Figure 11). It could be predicted that if the clustering time was long enough, more independent clusters would merge. Unlike DBSCAN, incremental DBSCAN deleted expired trajectories and only maintained the minimum number of trajectories reflecting current traffic patterns. It could not only adapt to the traffic pattern change caused by the traffic environment but could also reduce the impact of noise trajectory on the clustering result.  
Figure 14: Cluster result using DBSCAN. After the construction of Yushan bridge, the clusters cross the Yushan bridge still exist, although no ships cross.  
Regarding cluster speed, incremental DBSCAN was more efficient than DBSCAN with an increase in time. Figure 16 is a time-consuming comparison between DBSCAN and incremental DBSCAN. The first 40 days of clustering, the processing time of the two algorithms was almost equal. Between 40 and 80 days, the processing time of incremental DBSCAN increased slightly due to the addition of deletion programs. However, after 80 days, the processing time of DBSCAN was longer than that of incremental DBSCAN, and the gap between them widened. Overall, the processing time of the incremental DBSCAN increased almost linearly, while that of DBSCAN tended to follow an upward parabolic trend.  
Figure 16: Comparison of clustering time between DBSCAN and incremental DBSCAN.  
Figure 15: Independent trajectory merging result without deleting expired trajectories.
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 4 Algorithm Design  
In this section, the above sub-problems are analyzed. Two algorithms are proposed to solve problems based on task separability and resource separability, respectively. To better understand our proposed solutions, we will briefly introduce the algorithmic process, explaining the concepts related to Monte Carlo methods, Markov decision processes, reinforcement learning, and the mathematical definitions of value and reward functions.
###### Abstract  
Besides X-ray and fluoroscopy, a previously introduced X-ray scanner offers a 3D cone beam option (Multitom Rax, Siemens Healthcare). The aim of this study was to evaluate various scan parameters and post-processing steps to optimize image quality and radiation exposure for imaging of the parasinus region. Four human cadaver heads were examined with different tube voltages (90-121 kV), dose levels (DLs) (278-2180 nGy) and pre-filtration methods (none, Cu 0.2 mm, Cu 0.3 mm and Sn 0.4 mm). All images were reconstructed in 2 mm slice thickness with and without a metal artifact reduction algorithm in three different kernels. In total, 80 different scan protocols and 480 datasets were evaluated. Image quality was rated on a 5-point Likert scale. Radiation exposure (mean computed tomography volume index (CTDI\({}_{\text{vol}}\)) and effective dose) was calculated for each scan. The most dose-effective combination for the diagnosis of sinusitis was 121 kV/DL of 278/0.3 mm copper (CTDI\({}_{\text{vol}}\) 1.70 mGy, effective dose 77 uSv). Scan protocols with 121 kV/DL1090/0.3 mm copper were rated sufficient for preoperative sinus surgery planning (CTDI\({}_{\text{vol}}\) 4.66 mGy, effective dose 212 uSv). Therefore, sinusitis and preoperative sinus surgery planning can be performed in diagnostic image quality at low radiation dose levels with a multipurpose X-ray system.  
X-ray; medical imaging; computed tomography; three-dimensional; DVT; parasinus region; sinusitis +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Loss Function Construction  
#### 2.1.1 Category-Weighted Loss Function  
Data imbalances across different ability levels are a prevalent issue in data evaluations. To address this, researchers commonly employ data sampling methods and loss function optimization techniques. This paper introduces category weights \(\alpha_{i}\) to enhance the conventional cross-entropy loss function, aiming to mitigate the imbalance problem in sample categories.  
**Definition 2**.: _Let \(S(x_{i},y_{j})\)\(i=1\), \(2\),...\(N\)) be the set of training samples created by the ability evaluation model. \(y_{i}\) indicates the category of the \(i\)th sample, \(N_{y_{i}}\) represents the count of samples in the same category as \(y_{j}\), and \(N_{-y_{i}}\) represents the count of samples in categories other than \(y_{j}\), and \(N_{y_{i}}+N_{-y_{i}}=N\), thus_  
\[\alpha_{i}=\frac{N_{-y_{i}}}{N_{y_{i}}+N_{-y_{i}}},\alpha_{i}\in(0,1) \tag{2}\]  
Here, \(\alpha_{i}\) denotes the weight of the \(i\)th sample. The magnitude of \(\alpha_{i}\) in Definition 2 positively correlates with \(N_{-y_{i}}\) and negatively correlates with \(N_{y_{i}}\), meaning that if the sample (\(x_{i}\), \(y_{i}\)) falls into the majority category, its loss weight is reduced. Therefore, incorporating category weights \(\alpha_{i}\) into the cross-entropy loss function can augment the impact of a minority of low-ability-level samples on the loss function and diminish the loss weights of high-ability-level samples, thus neutralizing the loss discrepancies induced by the imbalanced distribution of high- and low-ability-level samples.  
**Definition 3**.: _Given \(y_{j}\) and \(p_{j}\) as illustrated in Definition 1, we have_  
\[L_{\pi}(p_{i},y_{i})=-\alpha_{i}{y_{i}}^{T}\log(p_{i}) \tag{3}\]  
This Equation represents the category-weighted cross-entropy loss function. The distinction between Equation (3) and the standard cross-entropy loss function is the inclusion of sample category weights \(\alpha_{i}\) to counterbalance the loss variation arising from differing sample counts across categories. If \(\alpha_{i}\) is designated as 1, then Equation (3) reverts to the standard cross-entropy loss function.
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Reliability Allocation Methodology Design  
Reliability allocation is the process of decomposing system reliability indicators into different parts and specifying the reliability requirements for each part to ensure the reliability of the system. Through reliability allocation, designers can develop corresponding reliability indicators and plans for different subsystems and components, thereby achieving the reliability of the entire system. Specifically, reliability allocation can clarify the reliability requirements of each part, optimize the reliability design of the system, improve the reliability level of the system, and provide a basis for reliability verification. In the process of implementing reliability allocation, the following four steps are required:  
1. Firstly, it is necessary to define the overall reliability requirements of the system, analyze its characteristics, and determine goals and constraints. These steps are prerequisites for reliability allocation.
2. Subsequently, it is necessary to establish an accurate model to determine the reliability of the system and use appropriate decision algorithms to calculate the reliability indicators of each part.
3. Next, it is necessary to choose a suitable reliability assessment method and use the results of reliability allocation to comprehensively evaluate the reliability of the system.
4. Finally, the estimated reliability value needs to be compared with the system-defined reliability requirements. If the estimated value is lower than the system-defined reliability requirements, reliability allocation needs to be performed again and recalculated until the reliability requirements are met.
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 2 Methodology
### System Model  
In mobile edge computing environments, the EDC placement problem can be modeled as a network represented by an undirected graph \(G=(V,E)\). This graph comprises numerous mobile users, base stations, and potential EDC locations. Specifically, \(V=B\cup L\), where \(B\) denotes the set of base stations and \(L\) denotes the set of potential EDC locations. The edges, \(E\), represent the connections between base stations and EDCs located at the points in \(L\). We assume that there are \(K\) EDCs, each designated to a different location, where \(K\) is a constant, and that each base station connects directly to its assigned EDC. We also consider that each EDC is responsible for a subset of base stations in \(B\) to process the mobile user requests, and the same base station is not shared between any two EDCs.  
Each base station has its own EDC, and the access delay to the EDC is proportional to the distance between the base station and the EDC. To optimize EDC placement in mobile edge computing networks, we need to account for this access delay.  
Figure 1 depicts an example of the EDC placement problem, where we can see that EDC \(L_{1}\) manages a group of base stations: \(b_{1},b_{2},b_{3},b_{4},b_{5}\). Therefore, we need to determine the optimal placement of \(L_{1}\) among the base stations to minimize access delay. Along with solving this problem, it is also crucial to define the dominant area of each EDC to achieve a balanced workload.  
The formal definition of the EDC placement problem has been established in [15] and is detailed below. A binary decision variable, \(x_{i,j}\in 0,1\), is used to indicate whether a base station, \(b_{i}\), is assigned to an EDC, \(L_{j}\). Specifically, \(x_{i,j}=1\) if base station \(b_{i}\) is assigned to \(L_{j}\); otherwise, \(x_{i,j}=0\). This applies for all \(i\) and \(j\) where \(1\leq i\leq|B|\) (with \(|B|\) defining the total number of base stations) and \(1\leq j\leq K\). This approach assumes that each EDC is situated with one of the base stations. Since there are \(|B|\) base stations, the number of possible locations for placing the EDCs is \(|B|\). For each assignment, we assign a score, \(c_{i,j}\) to evaluate its suitability for an EDC. This score takes into account the distance from the EDC to the group of base stations it manages (latency) and the load of the users connected to all those base stations (workload).  
For the distance, a smaller distance from each base station to its assigned EDC is preferred, while, for the workload, a more balanced distribution of the workload among each EDC is preferred.  
The EDC placement model is as follows:  
\[\begin{split}\text{Minimize}&\sum_{i=1}^{|B|}\sum _{j=1}^{K}c_{i,j}x_{i,j}\\ \text{subject to:}&\sum_{j=1}^{K}x_{i,j}=1,\quad \forall i\\ & x_{i,j}\in\{0,1\}\end{split} \tag{1}\]  
where \(\sum_{j=1}^{K}x_{i,j}=1\) ensures that each base station is assigned to one and only one EDC.  
Figure 1: An example of mobile edge computing with EDC placement.
## 2 Materials and Methods
### Horizontal Measurement Method  
The application of the uniform model is simple and quite similar to the estimation of a distance on a printed map. The pixel size (\(\gamma(\mathrm{w})\)) allows the conversion from pixel length into mm length. Considering the dependence of \(\gamma\) on the working distance, the following steps were followed for horizontal measurements using the uniform model. The working distance was estimated from the positions of the laser points [24]; then the appropriate value of the pixel size was computed from the uniform model. The pixel length of the target object was measured on the image; then the pixel length was multiplied with the multiplicative factor of pixel size to estimate its mm length.  
The application of the non-uniform model is more involved, and it is described under two categories of radial and general measurements. A radial measurement is defined as the length of an object that has one of its ends on the FOV center. The non-uniform model was trained using circles centered at the FOV center. Therefore, the model can estimate the mm radius of a circle centered at the FOV center, which would be equivalent to a radial measurement. Thus, the following steps were followed for horizontal radial measurements using the non-uniform model. The working distance was estimated from the positions of the laser points [24]. Then, the pixel length of the target radial object was measured on the image. The values of working distance and pixel length were fed into the trained non-uniform model (Equation (3)), and the mm length of the object was estimated.  
A general measurement needs to be expressed in terms of radial measurements before the application of the non-uniform model. Figure 6 shows this process. The main goal is to determine the length of the line segment AB in mm. We can construct the triangle AOB on the image, where O is the FOV center. Referring to Figure 6, OA and OB each has one of their ends at the FOV center, and hence they constitute radial measurements, and their mm lengths can be computed using the non-uniform model. At the same time, we can measure  
Figure 5: Models for horizontal measurements: (**A**) non-uniform model and (**B**) uniform model.  
the angle \(\alpha\) from the image. Let \(\rm A\) and \(\rm B\)\(\left|\begin{array}{c}x_{\rm B}\\ y_{\rm B}\end{array}\right.\) on the image, then, we can determine the angle between OA and the positive x-axis (\(\rm\theta_{A}\)) as follows,  
\[\rm\theta_{A}\ =\ \tan^{-1}\left(\frac{y_{\rm A}}{x_{\rm A}}\right) \tag{6}\]  
where \(\tan^{-1}\) denotes the four-quadrant inverse tangent function. The angle between OB and the positive x-axis (\(\rm\theta_{B}\)) can also be measured, similarly. Finally, the angle \(\alpha\) can be computed as,  
\[\rm\alpha=\left|\theta_{A}-\theta_{B}\right| \tag{7}\]  
Now, we can apply the law of cosines for determining the mm length of the line segment AB:  
\[\rm AB^{2}=\rm OB^{2}+\rm OA^{2}-2\cdot\rm OA\cdot\rm OB\cdot\cos(\alpha) \tag{8}\]