esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Key Sensitivity  
Key sensitivity analysis determine the affect of small change in the encryption key on the ciphered data. The encryption scheme in which small change in the encryption key leads to considerable difference in cipher text considered to be a good encryption scheme. The proposed joint image compression and encryption scheme used at least four keys. For the diffusion and permutations of the lower band 64 \(\times\) 64 integer wavelet coefficients. A tiny change in the permutation key affects the whole SPIHT encoding. Similarly a one bit change in the permutations sequence at any level of Kd-tree applied on the SPIHT output changed completely the bit stream. The other two keys are varying parameters logistic and quadratic chaotic maps keys which are also very sensitive to the change. Here, the key sensitivity results are presented in the form of PSNR and mean structural similarity index (MSSIM) after apply tiny changes at different levels of keys. The PSNR of the wrong key deciphered image was calculated thorugh Equation (14), SSIM and MSSIM were obtained with Equations (15) and (16) respectively. Here \(\mu\) and \(\sigma\) are the mean and variance respectively. Table 3 shows the key sensitivity test results of the proposed scheme.  
\[SSIM(x,y)=\frac{\left(2m_{x}m_{y}+c_{1}\right)\left(2\sigma_{xy}+c_{2}\right) }{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma_{y}^ {2}+c_{2}\right)} \tag{15}\]  
\[MSSIM(X,Y)=\frac{1}{n}\sum_{i=1}^{n}SSIM(x_{i},y_{i}) \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proposed Method** & **Ref. [(8)]** & **Ref. [(9)]** & **Ref. [(10)]** & **Ref. [(11)]** \\ \hline
2\({}^{1024}\) & 2\({}^{183}\) & 2\({}^{776}\) & 2\({}^{320}\) & 2\({}^{96}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key size comparison with some novel pseudorandom number generators.
## 2 Preliminaries for Designing a Sliding Mode Controller
### Methodology Principles  
The sliding mode control methodology, called variable structure control, is a nonlinear control method. The design of an SMC consists of two steps.  
* In the initial stage, a custom surface must be designed. Later, it is called the choice of sliding surface.
* In the second phase, a feedback control must be designed to ensure the convergence of a system's trajectory to the sliding surface; hence, the sliding surface must be achieved in a finite amount of time. The motion of the system on a sliding surface is known as the sliding mode [1].  
Once on the sliding surface, the dynamics of the plant is constrained to the equations of the custom surface, and they are robust to internal and external disturbances. Furthermore, they are insensitive to variations in control parameters [1].  
To illustrate the methodology, the following parts show the steps to obtain the controller: choosing the sliding surface; determining the control law; and finally, establishing a convergence or reaching condition.
# Routing Schemes in FANETs: A Survey
## 4 Routing Framework  
Routing framework consists of steps for nodes to find routes from source to destination nodes. In general, there are three main steps. Firstly, neighbor and route discoveries enable nodes (a) to broadcast control messages (e.g., hello messages) periodically to identify neighboring nodes in order to form an up-to-date neighbor set as the network topology changes as time goes by and (b) to exchange routing messages, whereby the source node sends messages, such as route request (RREQ), to the destination node to gather node information (e.g., node addresses and geographical location) along potential routes and the destination node sends messages, such as route reply (RREP), to the source node using the same route that the RREQ message traverses. Secondly, route selection enables nodes to use routing metrics to select routes. For instance, link expiration time (M.2) can be used to prolong route lifetime. Thirdly, route maintenance enables nodes to reestablish routes in order to cater for the dynamicity of network topology. There are four main categories of routing frameworks based on how route selection is made in FANETs as shown in Table 3. The rest of this section presents the frameworks of the routing schemes. Adaptive: The adaptive routing scheme learns, selects, and changes routes dynamically based on the current network condition (e.g., network congestion level and network connectivity) and the updates of the routes. This enables nodes to select the optimal routes based on knowledge as time goes by. The advantage is that packets can be sent along optimal routes, contributing to reduced link disconnections and network congestion and, hence, improved network performance. Nevertheless, there is a shortcoming whereby computational complexity increases due to learning. Some adaptive routing schemes used in MANETs, VANETs, and FANETs are adaptive routing in dynamic ad hoc networks (AROD) [108], static-node assisted adaptive routing (SADV) [109], and self-learning routing protocol based on reinforcement learning (RLSRP) [100]. Proactive: The proactive (or active) routing scheme determines routes prior to packet arrivals to provide immediate data transmission; in other words, routes leading to all destination nodes in the network are established, stored, and maintained in routing tables in advance at UAVs. Since a route leading to a destination node is either readily available or unavailable at a source node, packets can be either sent or dropped immediately. The advantage is that packets can be sent immediately without any initial delay incurred to establish routes. Nevertheless, there are two shortcomings. Firstly, nodes must exchange a large number of messages (or routing overheads) among themselves periodically to establish, update, and maintain routes in the routing table due to the high dynamicity of network topology in which link disconnections and network partitions are commonplace. Consequently, routing overhead is high and bandwidth utilization is inefficient. Secondly, routes in the routing table
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 3 The Proposed Protocol  
In this section, we propose an adaptive power-controlled depth-based routing protocol (APCDBRP). The primary objective of the proposed routing protocol is to extend the network's lifetime by balancing the energy consumption of each node in the network and controlling the transmitting power. Additionally, we utilized the depth information of sensor nodes as a substitute for complete geographical location information to reduce overhead. The network model of UWSNs is illustrated in Figure 1. Sensor nodes are distributed randomly in the underwater environment.  
Figure 1: Three-dimensional network structure of underwater sensor network.
## Appendix C Derivation of the Tracking Performance Index  
The transfer function of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter is expressed as (9) and (10):  
\[X_{\text{p}}(z)=\frac{\left(\alpha+\beta+\frac{\gamma}{2}+\frac{ \delta}{6}\right)\!z^{3}+\left(-3\alpha-2\beta+\frac{2}{3}\delta\right)\!z^{2 }+\left(3\alpha+\beta-\frac{\gamma}{2}+\frac{\delta}{6}\right)\!z-\alpha}{f_{ \alpha\beta\gamma\delta}(z)}X_{\text{o}}(z), \tag{19}\]  
\[f_{\alpha\beta\gamma\delta}(z) =z^{4}+\left(\alpha+\beta+\frac{\gamma}{2}+\frac{\delta}{6}-4 \right)\!z^{3}+\left(-3\alpha-2\beta+\frac{2}{3}\delta+6\right)\!z^{2}\] \[\quad+\left(3\alpha+\beta-\frac{\gamma}{2}+\frac{\delta}{6}-4 \right)\!z+(1-\alpha). \tag{20}\]  
Using this transfer function, we calculate \(s_{\text{c}}(kT)^{4}/24-x_{\text{p},k}\) in the \(z\)-domain to derive the tracking performance index (42). The \(z\)-transform of the first term is  
\[Z[s_{\text{c}}(kT)^{4}/24]=\frac{z(z^{3}+11z^{2}+11z+1)}{24(z-1)^{5}}s_{\text{c }}T^{4}, \tag{21}\]where \(Z[\ ]\) denotes the \(z\)-transform. The predicted error in the \(z\)-domain is defined as  
\[E_{\text{p}}(z)\equiv X_{\text{o}}(z)-X_{\text{p}}(z). \tag{100}\]  
Here, to calculate the steady-state bias error (mean error), we assume that the mean error in \(x_{\text{o},k}\) becomes zero because of the white Gaussian noise. Thus, \(X_{\text{o}}(z)=Z[s_{\text{c}}(kT)^{4}/24]\) is satisfied. By substituting (101) and (102) into (100), we obtain  
\[E_{\text{p}}(z)=\frac{z(z^{3}+11z^{2}+11z+1)}{24(z-1)f_{\text{a} \beta\gamma\delta}(z)}s_{\text{c}}T^{4}. \tag{102}\]  
By applying the final value theorem to (102), the tracking performance (43) is derived as follows:  
\[e_{\text{fin},\alpha\beta\gamma\delta}=\lim_{k\to\infty}\{s_{\text{c}}(kT)^{4 }/24-x_{\text{p},k}\}=\lim_{z\to 1}\frac{z-1}{z}E_{\text{p}}(z)=\frac{s_{ \text{c}}}{\delta}T^{4}. \tag{103}\]
## 5 Real Data Application  
We performed a thorough examination of the nut's dataset in this application, following [PERSON]'s [7] documentation and [PERSON] et al.'s [35] recent adoption. The dataset is accessible through the COUNT library in the R program. The data collection, which includes fifty-two observations and four (4) predictors. In particular, the number of cones that squirrels remove is represented by the response variable. The predictors are the number of DBH (diameter at breast height) per plot (\(x_{1}\)), mean tree height per plot (\(x_{2}\)), canopy closure (as a percentage) (\(x_{3}\)), and standardized number of trees per plot (\(x_{4}\)). To determine which model was best suited for the dataset, we fitted the Poisson regression model (PRM), negative binomial regression model (NBRM), and Poisson-Inverse-Gaussian regression model (PIGMR). The results are shown in Table 4. The PIGRM provides the best fit based on residual variance, as it has significantly smaller errors. In terms of AIC, NBRM is marginally better than PIGRM, but the substantial reduction in residual variance for the PIGRM suggests that it may offer a better model despite the slightly higher AIC. PRM performs worse on both metrics, making it the least desirable model. Furthermore, over-dispersion was discovered in the model using the dispersion test, which made the Poisson regression model inappropriate for this dataset [35].  
Furthermore, the estimated variance inflation factors are 40.89, 39.61, 16.332, and 4.474, which clearly show that the model contains correlated regressors. This leads to both multicollinearity and over-dispersion in the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & \(PRM\) & \(NBRM\) & \(PIGRM\) \\ \hline Residual Variance & 661.2 & 661.2 & 0.5498 \\ \hline AIC & 873.1 & 397.9 & 406.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model Adequacy Check.  
Figure 2: Graphical Illustration of MSE against Sample Size for MSE for \(\gamma=0.99,\omega=6\) and \(\mathrm{p}=4\).  
Table 5 displays the estimated regression coefficients derived from the various estimators examined in this study. We assessed the estimator's performance using the SMSE, prediction mean squared error (PMSE), and prediction mean absolute error (PMAE). The dataset was randomly split into 80% training and 20% test sets. We estimated the model parameters on the training set and calculated the performance criteria on the test set. The results revealed that \(\hat{\mathbb{a}}^{PIG-MLE}\) exhibits poor performance in handling multicollinearity within the PIG regression model, as evidenced by its high SMSE, PMSE, and PMAE. The proposed estimator is the most preferred among the shrinkage estimators, achieving lower PMSE and PMAE.
# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 4 Clutter Statistics and Detection
### K-Distribution
#### 4.1.1 Method of Moments (MoM)  
Estimating the unknowns of the K-distribution using the maximum likelihood method is computationally intensive as it requires many iterations to maximize the likelihood function. An alternative and rather fast way is to apply MoM based approaches. The number of looks \(L\) is either known or can be estimated using the ratio of the square of the mean and the standard deviation [40]. One way to solve \(\mu\) and \(v\) is to use first and second moments of the data. This method is referred to as \(MV\) (mean and variance), contrast based or V-statistic. The equations are [41]  
\[\hat{\mu}=\langle I\rangle, \tag{18}\]  
\[\left(1+\frac{1}{\hat{v}}\right)\!\left(1+\frac{1}{L}\right)=\frac{\langle I^ {2}\rangle}{\langle I\rangle^{2}}, \tag{19}\]  
where \(<>\) is the expectation operator. Knowing the number of looks \(L\) and the mean \(\hat{\mu}\), the shape parameter \(\hat{v}\) can be estimated using (19). It is found that the V-statistic works quiet well in most of the cases and is therefore preferred. Another estimator proposed in [42] is referred as X-statistic, which is computationally faster and gives a more straightforward solution than other log based estimators. The mathematical expression for the X-statistic is  
\[\left(\frac{1}{\hat{v}}+\frac{1}{L}\right)=\frac{\langle I\cdot\log I\rangle}{ \langle I\rangle}-\langle\log I\rangle, \tag{20}\]  
The mean \(\hat{\mu}\) is estimated using (18).
## 5 Discussion
### Application Prospects  
In the contemporary landscape of increasingly diverse and complex application scenarios for GIS, ranging from real-time monitoring and the regulation of traffic flow in smart city construction to the efficient analysis of massive geographic datasets in natural resource management, GIS service platforms are encountering escalating pressure from concurrent requests. The adaptive task traffic-shaping method proposed in this paper undoubtedly offers a promising solution to address the performance bottleneck issue faced by GIS service platforms in high-concurrency scenarios, thereby providing robust and reliable geographic information support for the development of various industries.  
In the realm of smart city traffic management, the adaptive task flow-shaping method can dynamically adjust the data processing rate based on real-time traffic data. For instance, during morning and evening rush hours when system load is high, it can automatically increase the token fill rate, accelerate road condition analysis, optimize signal light timing, alleviate congestion, and enhance the overall efficiency of urban traffic operations. This dynamic adaptive flow-shaping capability is anticipated to become increasingly crucial in urban development and may eventually serve as a standard feature in smart city traffic management systems.  
In the context of natural resource management, large volumes of geospatial data are generated during land resource surveys and forest resource monitoring, with data processing tasks often being sudden and intricate. The adaptive task traffic-shaping method can continuously monitor server load and flexibly adjust the task processing rate according to resource load evaluation results. When conducting remote sensing detection tasks such as pest and disease outbreaks, it can automatically expedite processing, thereby enhancing the scientific accuracy and timeliness of decision-making in natural resource management.  
In the domain of emergency response, particularly during natural disasters such as earthquakes and floods, the GIS service platform may instantaneously receive a substantial number of geographical information requests from rescue sites and disaster area monitoring equipment. The adaptive task flow-shaping method can promptly detect changes in system load, swiftly adjust the task processing sequence, prioritize critical rescue information, and thereby improve rescue efficiency.
###### Abstract  
This study investigates the impact of various economic sectors on household income in Thailand. It is conducted in light of the substantial \"digital wallet\" scheme initiated by the Thai government, with the goal of providing empirical evidence and suggesting alternative policies for regional development informed by sectoral and spatial insights. The research aims to deepen the understanding of how different economic sectors affect household income, filling a gap in the current understanding of the relationship between sectoral productivity and income. Utilising spatial lag models (SLM), the study analyses data spanning from 2005 to 2021, testing the effects of 19 economic sectors comprising the Gross Provincial Product (GPP) of Thailand's 76 provinces on the average household income. The findings indicate direct associations between agriculture, real estate, professional services, support services, and leisure sectors and household income, alongside pronounced spatial autoregression. This implies that income levels in one province can substantially influence those in neighbouring provinces. This research extends the understanding of economic influences at the regional level and highlights the importance of considering spatial factors in economic policymaking.  
Keywords:spatial econometrics; spatial lag model; household income; gross provincial product; regional development; Thailand +
Footnote †: journal: _economies_
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 2 Technical Background
### Specific Meaning of RAIM Being Available  
RAIM being available refers to \(P(\text{HMI})<P_{r}(\text{HMI})\), i.e., \(P(\text{HMI}|1F)<P_{r}(\text{HMI}|1F)\) for the arbitrary measurement bias value while RAIM being unavailable refers to \(P(\text{HMI}|1F)\geq P_{r}(\text{HMI}|1F)\) for at least one measurement bias.  
A specific example is used to intuitively explain the meaning of RAIM being available. The 32-satellite GPS constellation is used in this example. The pseudorange measurement is assumed to be the dual-frequency ionosphere-free combination of L1 and L5. The standard deviation of the measurement noise for \(VS_{i}\), signed as \(\sigma_{i}\), is set according to the ARAIM interim report [11]. For the location of 37\({}^{\circ}\) N latitude, 117\({}^{\circ}\) E longitude and height 0 m and the epoch of UTC 14 March 2019 17:15:00, there are 9 visible satellites with a masking angle of 10\({}^{\circ}\). Their vertical slope values are recorded in Table 1.  
Figure 1 presents the base-10 logarithm of \(P(\text{HMI}|1F)\) for PRN5, PRN6 and PRN13 with the measurement bias \(\xi_{b}\) in the interval of \((0.30\text{ m})\). These three satellites have the top three vertical slope values, as shown in Table 1. The \(\xi_{b}-lgP(\text{HMI}|1F)\) curves for these three satellites follow the same order as the slope values, which illustrates that a faulty satellite with a large slope will present a high \(P(\text{HMI}|1F)\). The \(\xi_{b}-lgP(\text{HMI}|1F)\) curves for the other six visible satellites must be lower than that of PRN 13 because their vertical slopes are smaller. In this example, RAIM would be unavailable if PRN5 was the faulty satellite because \(P(\text{HMI}|1F)\) is larger than \(P_{r}(\text{HMI}|1F)\) for \(\xi_{b}\) values in the range from 11 m to 15 m. Therefore, the intersection between the \(\xi_{b}-lgP(\text{HMI}|1F)\) curve of the faulty satellite and the \(P_{r}(\text{HMI}|1F)\) line means that RAIM is unavailable. RAIM would be available if the faulty satellite was one of the other visible satellites except for PRN5, because its \(P(\text{HMI}|1F)\) smaller than \(P_{r}(\text{HMI}|1F)\) at an arbitrary measurement bias. Therefore, the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline PRN & 2 & 5 & 6 & 12 & 13 & 15 & 19 & 25 & 29 \\ \hline \(Slope\) & 0.162 & 6.060 & 3.010 & 1.576 & 2.425 & 1.617 & 2.036 & 0.925 & 1.636 \\ \hline \end{tabular}
\end{table}
Table 1: Vertical slope values for different visible satellites.  
separation between the \(\xi_{b}-lgP(\text{HMI}|1F)\) curve of the faulty satellite and the \(P_{r}(\text{HMI}|1F)\) line means that RAIM is available.  
Because the faulty satellite is unknown in actual situations, RAIM is considered available only if \(P(\text{HMI}|1F)<P_{r}(\text{HMI}|1F)\) is true for the arbitrary measurement bias in the worst case, i.e., the satellite with the maximum slope being faulty.
## 3 Algorithms Used in Correlation Mining Analyses
### Machine Learning-Based Algorithms  
In this study, the relationship between vertical rail deflections and longitudinal levels was examined using predictive monitoring algorithms, such as linear regression, decision trees, and random forests. After data preprocessing through preparation and normalization, the accuracy measures were used to compare the performance of the mentioned ML-based algorithms in the mining of the correlation between rail displacements and both longitudinal levels of the left and right rails. A schematic of the ML-based methods used in the current study is illustrated in Figure 6. The three accuracy measures include the Mean Absolute Error (MAE), which measures the average magnitude of the errors, Mean Squared Error (MSE), which measures the average squared magnitude of the errors, and root mean square error (RMSE), which measures the square root of the average squared magnitude of the errors.
# Safe Three-Dimensional Assembly Line Design for Robots Based on Combined Multiobjective Approach
## 2 Modeling of Assembly Line Layouts Based on the Safety Indicator
### Modeling of Safe Layout
#### 2.2.3 Safety Indicator  
**Definition 1**.: _The safety indicator \(\gamma\) focuses on collision detection by the threshold condition to evaluate the robot assembly status. The physical hardware safety and robot control security planning are not considered. When the safety indicator reaches the threshold condition, the collision detection is performed via point cloud calculations. The quantitative expression of the safety indicator is given in (1), and the parameter is shown in Figure 1. When \(\gamma<0\), the robot does not perform cooperative assembly and only needs to perform rough collision detection. When \(\gamma\geq 0\), the robots are assembled collaboratively, or the robots select workpieces from a machine, and detailed collision detection needs to be performed. The safety level is a product of the safety factor and the device spacing. A safety factor (FS) that reflects the shape of optimized layout entities is established. The quantitative expression is given in (2). A high value of the safety factor allows the use of irregular entity shapes, and a high level of collision detection is required to obtain high accuracy. Ri is the maximum working radius._  
\[\gamma=\left(\left(R_{i}+R_{j}\right)/2-S_{ir}-\delta_{i}\times\sum_{i=0}^{i=2 }FS_{i}\right. \tag{1}\]  
\[FS_{i}=Optimized\ model\ areaactual\ area\ \geq 1\]  
\[\left\{\begin{array}{ll}if\ \gamma\geq 0,&\text{detailed collision detection}\\ if\ \gamma<\ 0,&\text{rought collision detection}\end{array}\right. \tag{2}\]