esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 2 Theorem and Methods
### Basic Theorem of DeltaDOR
While the subindex 'egrs' means calibration quasar, 'SC' means spacecraft, and 'inst', 'clock', 'EOP', 'pos', 'atmo', 'iono' corresponds to the errors caused by instrument delay, station clock, earth orientation, antenna location, atmosphere delay, and ionosphere delay, respectively. A linear interpolation is induced to obtain the common systematic error at time \(t_{2}\) based on systematic error at time \(t_{1}\) and \(t_{3}\). \[ \begin{array}{ll}\tau_{egrs-error}(t_{2})=\tau_{egrs-inst}(t_{2})+\tau_{ egrs-clock}(t_{2})+\tau_{egrs-EOP}(t_{2})\\ \qquad\qquad+\tau_{egrs-pos}(t_{2})+\tau_{egrs-atmo}(t_{2})+\tau_{egrs-iomo}(t _{2})\end{array}  \tag{6}\]  
Under the condition of small angular separation and temporal intervals, the following assumption could be made. \[\tau_{SC-error}(t_{2}) \approx \tau_{egrs-error}(t_{2}) \tag{7}\] \[=\tau_{SC-inst}(t_{2})+\tau_{SC-clock}(t_{2})+\tau_{SC-EOP}(t_{2} )+\tau_{SC-pos}(t_{2})\] \[+\tau_{SC-atmo}(t_{2})+\tau_{SC-iomo}(t_{2})\]  
Then, the observable of spacecraft \(\tau_{SC-geo}(t_{2})\) could be obtained with Equations (4) and (7). \[\tau_{SC-geo}(t_{2})=\tau_{SC}(t_{2})-\tau_{SC-error}(t_{2}) \tag{8}\]
## 3 Results
### Analysis of Orbit Determination Accuracy for Different Orders  
The accuracy evaluation in this study starts 60 min after the orbit determination starts in order to account for the filter's notable convergence characteristic during the initial phase. This method removes the impact of initial state mistakes on the accuracy evaluation and guarantees that the results represent the filter's stable performance. Using the EIGEN6C model as an example, when the gravity field order is 50, the radial, tangential, and normal position accuracies are 0.523 m, 0.233 m, and 0.290 m, respectively, with a three-dimensional position accuracy of 0.654 m. When the order is increased to 60, the radial, tangential, and normal position accuracies improve to 0.420 m, 0.191 m, and 0.225 m, respectively, with a three-dimensional position accuracy of 0.522 m. The radial, tangential, and normal accuracy improves by around 0.01 m, 0.004 m, 0.007 m, respectively, while the three-dimensional position accuracy improves by about 0.013 m. The accuracy of orbit determination increased by over 20% overall. Further study demonstrates that, while orbit determination accuracy improves with increasing gravity field model degree, the rate of progress gradually slows. Given the trade-off between accuracy increases and computational expense, a degree of 60 is suggested as the ideal choice. This strikes a sensible compromise between orbit precision and resource consumption, providing an effective and feasible optimization technique for onboard processing systems. The next stage is to determine whether the receiver can handle the system resources required by changes in the gravity field order. Figure 6 and Table 5 below show the specific results for orbit determination accuracy at different orders.  
Figure 5: Execution flow of the orbit determination software.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order** & **R** & **T** & **N** & **3D** \\ \hline
40 & 0.8787 & 0.43745 & 0.5158 & 1.0845 \\
50 & 0.5233 & 0.2328 & 0.2931 & 0.6412 \\
60 & 0.4153 & 0.1912 & 0.2252 & 0.5138 \\
70 & 0.4004 & 0.1723 & 0.2146 & 0.4944 \\
80 & 0.3988 & 0.1686 & 0.2121 & 0.4902 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Orbit determination error accuracy for different orders of EIGEN-6C.
## 2 Overview of IoT Security
### Internet of Things
#### 2.1.2 IoT Security Challenges  
The nature of the interconnected structure of IoT systems is one of the major reasons causes of risks of malicious attacks [19]. As shown in Figure 2, specific attacks are designed  
Figure 1: Overview of IoT architecture.  
for each layer of the IoT architecture. According to the literature, the typical method for ensuring security is ineffective, especially when it comes to user safety in IoT systems [20]. In addition, some deliberately designed attacks are able to not only compromise certain devices in the IoT but also cause a series of failures as a a consequence, i.e., cascading failures [21]. Therefore, it is important to secure every processing phase of the IoT before data are fed to a higher layer [22].  
On the other hand, most existing IoT applications assume that there is a central server that can remotely communicate and control the devices. Due to the pressure to quickly launch applications in the market, security issues may be overlooked. For instance, the software system of the applications must be improved, in addition to the security of lower levels, such as hardware and firmware. Many devices in the IoT, on the other hand, are not updated over time, which may lead to system vulnerability [23].  
As a result, there is an urgent need for critical attention to current security concepts regarding effective IoT security procedures. The main reasons for this urgency can be summarized as follows. (i) With booming data and devices, various novel IoT applications are explored promptly. Many of these applications may not have been systematically analyzed for potential risks of malicious attacks and lack a proper design that is resilient to system attacks. (ii) Conventional security approaches such as encryption, authentication mechanisms, access control algorithms, etc., are becoming less effective in large-scale IoT scenarios [24]. Novel technology may also enable novel attacks that are able to bypass traditional security approaches. Furthermore, novel attacks may be able to attack some parts of IoT applications that are not protected by traditional security measures. (iii) Most existing IoT systems utilize a wide range of dependable and cost-effective smart devices. Such devices are normally resource-limited. Hence, it becomes even more difficult to implement security algorithms to detect or eliminate attacks [25].
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 4 MIMO Array Antenna
### Methodology  
The proposed 1 \(\times\) 2 array antenna, discussed in Section 3, is extended to a two-port MIMO array antenna, as shown in Figure 12. Initially, at stage 1 (Ant 1), the MIMO configuration is formed by mirroring the 1 \(\times\) 2 array antenna towards the \(x\)-axis at an element spacing of 9 mm (less than one-fourth of the wavelength) as shown in Figure 12a. The second stage (Ant 2) shows the final proposed MIMO antenna incorporating two vertical stubs that form the entire connected ground and DGS decoupling structure. Figure 13 displays the simulated scattering properties of the MIMO configuration's evolution stages. From  
Figure 10: Array radiation patterns at (**a**) 3.75 GHz and (**b**) 5.76 GHz.  
the figure, the reflection coefficient of the first evolution stage has a reduced bandwidth and frequency of operation.  
Figure 11: Gain and radiation efficiency plots of the array antenna.  
Figure 12: Evolution stages of the MIMO configuration (**a**) without DGS and (**b**) with DGS (ground connected with stubs, as proposed).  
Moreover, the minimum isolation throughout the entire bandwidth is less than 15 dB for the first band. However, the final proposed stage (Ant 2) provides an enhanced 22 dB isolation for both of the operating frequency ranges. The rectangular stubs that are connected to the ground create an alteration in the current path, thereby canceling the coupling current and enhancing isolation. The volume of the proposed MIMO antenna is 57 \(\times\) 50 \(\times\) 0.1 mm\({}^{3}\). Figure 14 presents the dimensions and final configuration of the suggested MIMO antenna.
## 5 Simulation
### Performance Measure of Security  
The security level is determined in terms of confidentiality. From this, the security level (\(\zeta\)) is measured based on the ratio of the number of data packets that are accessed only by authorized users to the total data packets considered as input. Accordingly, the security level (\(\zeta\)) is mathematically calculated using the following formula:  
\[\zeta=\frac{\
u_{auth}}{\
u}.100 \tag{14}\]  
In Equation (14), '\(\
u_{auth}\)' defines the data packets that are accessed only by authentic users in a satellite network environment, whereas '\(\
u\)' indicates thetotal data packets assumed as input for experimental work. The security rate of inter-satellite communication is estimated in terms of percentage (%).  
Table 8 and Figure 3 demonstrate the simulation results of security level based on data confidentiality in a satellite environment using the developed MRFCO-ZTS approach and state-of-the-art ZTAP-HECC [1]. During the simulation process, varying numbers of data packets in the range of 50 to 250 are considered as input. As exposed in the above tabulation and graphical comparative analysis, the developed MRFCO-ZTS approach achieved a higher security level for data communication in the satellite environment via continuously authenticating all the users at each session. In addition, the developed MRFCO-ZTS approach provides better security performance while increasing the number of input data packets thereto be transmitted over satellite networks when compared to conventional ZTAP-HECC [1]. This is because the process of MRFCO and ZTS in our work advances state-of-the-art works by balancing computational cost, security, and data integrity. The MRFCO-ZTS approach continuously observes user activity, such as keyboard input patterns, device behavior, and network activity, to notice any deviations from the established baseline. From this, the MRFCO-ZTS model greatly prevents unauthorized access via classification. Hence, the developed MRFCO-ZTS approach's ratio of the number of data packets that are accessed only by authentic users in a satellite environment improves. As a consequence, the developed MRFCO-ZTS approach obtains a 97.88% security level while considering 250 users as input, whereas the conventional ZTAP-HECC [1] obtained 93.32%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**User\_ID** & **Sessions** & **Avg\_Risk\_Score** & **Suspicious\_Sessions** & **Total\_Cost** & **User\_Type** \\ \hline “User\_1” & 39 & 0.82 & 27 & 318.82 & “Attacker” \\ \hline “User\_2” & 43 & 0.62 & 22 & 268.25 & “Detector” \\ \hline “User\_3” & 37 & 0.63 & 16 & 223.85 & “Detector” \\ \hline “User\_4” & 34 & 0.63 & 17 & 214.93 & “Detector” \\ \hline “User\_5” & 32 & 0.7 & 18 & 253.74 & “Attacker” \\ \hline \hline \end{tabular}
\end{table}
Table 7: Real-world simulation performance results.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 4 Case Study Results
### Misfit Vectors by Imagery Source  
Misfit vectors were also calculated for web imageries other than GE imagery using the same GCPs (Table 5). The highest errors were associated with the Bing web imagery and the lowest with the Queensland imagery (Tables 5 and 6). By way of comparison, the misfit vector estimated for the drone imagery collected at CQ-1/G1 was 0.016 m and bearing 0.123574\({}^{\circ}\) (the average across eight GCPs).  
Figure 5: Misfit vectors calculated from the GNSS GDA2020 assessed the location of the GCP and Google Earth images over five years at four farm sites. Vectors are displayed in 2016 for the Google Earth image for (**A**) and the 2021 image for (**B**), (**C**), and (**D**), respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Fam/CP**} & \multicolumn{4}{c}{**Misfit Vectors in Web Imagery**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Bing**} & \multicolumn{2}{c}{**Google**} & \multicolumn{2}{c}{**ESRI**} & \multicolumn{2}{c}{**QLD Globe**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Magitude (m)**} & \multicolumn{2}{c}{**Bearing (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Magitude (m)**} & \multicolumn{2}{c}{**Bearing (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Magitude (m)**} & \multicolumn{2}{c}{**Bearing (\({}^{\circ}\))**} \\ \cline{2-9}  & \multicolumn{2}{c}{**GCP**} & \multicolumn{2}{c}{**GCP**} & \multicolumn{2}{c}{**GCP**} & \multicolumn{2}{c}{**GCP**} & \multicolumn{2}{c}{**GCP**} & \multicolumn{2}{c}{**GCP**} \\ \hline CQ-1/G1 & 5.712 & 225.053691 & 1.768 & 49.570205 & 1.141 & 251.483588 & 0.429 & 69.585256 \\ FNQ-1/M1 & 2.357 & 218.344693 & 1.883 & 219.4743312 & 4.218 & 261.485087 & 1.919 & 190.089089 \\ FNQ-2/G1 & 4.049 & 268.553544 & 1.897 & 144.832330 & 2.596 & 290.962091 & 4.306 & 114.349462 \\ FNQ-3/W4 & 4.774 & 164.826241 & 5.758 & 170.538496 & 1.918 & 306.876470 & 1.024 & 161.457136 \\ \hline Mean & **3.365** & **218.086680** & **1.963** & **163.813842** & **2.319** & **276.766311** & **1.556** & **136.118854** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Misfit vectors for the images of four sites from four web imagery providers (doa 15/4/2023).  
In comparison, the horizontal and vertical positional accuracies achieved for the drone imagery were 0.0127 and 0.0029 m, respectively. A similar result was reported with the use of a moderate number of GCPs by [PERSON] et al. (2018), who reported a spatial resolution of 7.4, 14.8, and 24.7 mm and an accuracy of 1.5, 2.6, and 2.5 (\(\pm\)1.2) mm for UAV mosaiced images collected at flying altitudes of 30, 60, and 100 m, respectively, using a 12.3 MP Olympus EP-1 camera.
## 5 Implementation
### Implementation Design
#### 5.2.3 Peer-To-Peer Firmware Update Distribution  
As shown in Figure 9, the implementation of a peer-to-peer firmware update consists of four entities: the client, the IoT device (IoTivity server), IoT gateway 1, and IoT gateway 2. Similarly to the direct firmware update protocol, when the client wants to update the firmware, the client needs to set the time, the action, and the URL to the IoTvity server that resides in the IoT device. We also implemented three REST APIs to support communication between IoTivity server and IoT gateway: Check New Version, Download Update, and Perform Upgrade. The flow is the same as we explained in Section 5.2.2, except, in this case, IoT gateway 1 has the new firmware update on its local database, and IoT gateway 2 does not have the new firmware update. Instead of the manufacturer, IoT gateway 2 will download the new firmware from IoT gateway 1. Since they are in the same network and each gateway has an IP address list, IoT gateway 2 will send a broadcast message to the IP address list to determine which one has the new firmware update. After finding the gateway with the newest firmware update, IoT gateway 2 will download the update from this gateway instead of the manufacturer. Thus, in the peer-to-peer update, the primary interaction is between the gateways. It will not include the manufacturer as the host to download the firmware update's file. Since gateways help the firmware update distribution jobs, it will help to avoid the single-point-of-failure problem and increase overall firmware update availability. Additionally, it maintains the firmware update file's reliability by using a smart contract in the blockchain network.  
Figure 8: The.patch file created using Diff3.
## 3 Experimental Results
### Correlations between the CT Dose Indices  
In addition, this study examined the relationship between the CT dose indices using linear regression models. Table 7 provides a summary of the correlation results between these indices.  
Figure 1: Plots of the simple linear regression models for (**a**) the chest CT exam, (**b**) the cardiac angiogram CT exam, (**c**) the cardiac calcium score CT exam and (**d**) the abdomen/pelvis CT exam. The blue points represent the samples and the red dotted line represents the linear regression line.  
In all CT exams, statistically significant relationships were apparent between all dose indices as indicated by the evaluation metrics (\(p\)-value \(<\) 0.05). The relationship between CTDI\({}_{\text{vol}}\) and DLP was found to be the most significant (\(r\) \(>\) 0.85 in all exams) compared to the relationships between each pair of the other dose indices. A relatively strong relationship between ED and SSDE was also noticed in the chest and abdomen/pelvis CT exams (\(r\) = 0.82 and \(r\) = 0.73, respectively).
## 4 Experimental Settings
### Algorithms  
#### 4.1.1 Target Object Detection  
To detect a target object in real time, we utilized the You Only Look Once version 8 (YOLOv8) model, which offers both high accuracy and rapid processing, making it well suited for real-time tracking on edge devices [31]. YOLOv8 is available in various model sizes (n, s, m, l, xl), enabling flexibility based on resource constraints and performance requirements. For our experiments, we employed the lightweight YOLOv8n model, to balance computational efficiency and detection accuracy. We chose YOLOv8n primarily because it is widely recognized as a highly efficient and lightweight model, offering a strong balance between detection accuracy and real-time performance on resource-constrained devices. Its straightforward fine-tuning process and reliable empirical performance make it especially well suited for our edge-based tracking framework, where both speed and accuracy are crucial. YOLOv8n has the fewest parameters and the lowest computational load, still ensuring smooth operation on edge devices with limited resources. For both experiments, the pre-trained YOLOv8n [31] model was fine-tuned using additional data tailored to the experimental environment. This fine-tuning process enhanced the detection performance for specific objects, such as people and emergency vehicles, thereby improving the accuracy of target detection.
## 1 Introduction
In this regard, metaheuristic (MT) optimisation algorithms such as GA [53], particle swarm optimisation (PSO) [54] and imperialist competitive algorithm (ICA) [55] can be used to address mentioned drawbacks. In some cases, the ANN performance can be improved by the global search feature of these methods. ANNs and some optimisation techniques have been recently applied for solving nonlinear and sophisticated engineering problems. One of the ANN developments is adaptive neuro-fuzzy inference system (ANFIS) that has become a trending algorithm for prediction [56]. In medical researches, ANFIS has also been conducted for predicting a number of verified patient cases [57; 58]. A novel combination set of ANFIS was developed in China to predict the number of contaminated cases from COVID-19 [59]. Moreover, hybrid algorithms are typically employed to solve multiobjective problems [60; 61]. Therefore, different types of optimisation methods can be integrated with ANFIS to increase the accuracy and improve the performance of the considered algorithm. The multi-verse optimiser (MVO) approach was combined with ANFIS to calculate the consumption of materials [62]. This hybrid algorithm is utilised to solve the consumption rate of material and predict that from a dataset that has been derived from different areas. The MLP neural network is suitable for prediction, especially in problems with stochastic irregularities [63]. As discussed previously, few studies investigated the CFS upright sections by AI algorithms, and almost none of them employed the feature-selection method. First, FE models with different configurations were successfully produced and comprehensively discussed, and their noticeable specifications were mentioned. Secondly, an AI approach was utilised for predicting the compression capacity of the upright frames and verifying the FE models. Since the neural network has a successful background in predicting upright section properties, a multi-layer perceptron (MLP) technique was selected as the central core of the AI method. In this paper, a feature-selection based algorithm has also been used to find the most governing property of compressive strength, and another numerical approach was deployed for verification, optimisation and prediction. Operating a hybrid neural network based on the feature-selection technique successfully led to predicting the ultimate normalised load and the displacement. The validity of the FE results was successfully proven as well. Moreover, a new developed multi-layer perceptron algorithm in combination with particle swarm optimisation was deployed, and obtained results were explained in detail.
# Nucleation Mechanism and Rupture Dynamics of Laboratory Earthquakes at Different Loading Rates
## 3 Results
### Effect of Loading Rate on the Features of Stick-Slip Events
#### 3.2.2 Static Friction  
Figure 7a shows the ratio between the shear stress and the normal stress as a function of time in the stick-slip cycles. This ratio increases linearly with the time during the interseismic stage until it reaches its peak values (static friction, \(\mu^{p}\)) and then drops sharply in the coseismic stage. We define the friction drop (\(\Delta\mu\)) as the difference between the peak friction \(\mu^{p}\) and the residual friction \(\mu^{min}\), i.e., \(\Delta\mu\) = \(\mu^{p}\) - \(\mu^{min}\). Figure 7b describes the peak friction coefficient \(\mu^{p}\) as a function of loading rate. Generally, the peak friction decreases with the increase in the loading rate, despite the scattering. An exponential function is used to fit the results:  
\[\mu^{\text{P}}=0.048\;{\rm V_{L}}^{-0.455}+0.414 \tag{5}\]  
Figure 8 shows the static friction as a function of recurrence interval. The static friction increases with the logarithm of the recurrence interval, which is consistent with previous results [41; 42]. This positive correlation between the static friction and the recurrence interval is also consistent with the time-dependent strengthening of the fault strength suggested by Equation (5). It has been proved that the time-dependent strengthening of the fault strength may arise from the increase in the real area of adhesive contacts with time, which may be due to the localized creep and the degradation of contact hardness [43]. Thus, a lower loading rate may allow for more development of the time-dependent inelastic behaviors of the contacts, leading to larger contact areas.
## Article  
The Impact of Renewable Energy Tax Incentives on Electricity Pricing in Texas  
**[PERSON] 1** and [PERSON] 2,*  
* **Abstract:** Texas has abundant natural resources, making it a good place for renewable energy facilities to build. Unfortunately, property taxes are the highest tax on an incoming renewable energy facility in the state. In order to increase renewable energy in the state, Texas tax code Chapter 313 was introduced. Chapter 313 allows school districts the opportunity to offer a 10-year limit, ranging from USD 10 million to USD 100 million, on the taxable value of a new green energy project. With Chapter 313 ending in 2022, the following question is raised: how do tax incentives that increase the number of applications for producing renewable energy in Texas impact the wholesale, real-time pricing of electricity in the state? Skew-t regression models were implemented on a large dataset, focusing on the designated North, Houston, and West regions of the Electricity Reliability Council of Texas (ERCOT), since these regions account for 80% of the state's energy consumption. Analysis focused on the hours ending at 3 AM, 11 AM, and 4 PM, due to the ERCOT's time-of-day pricing. Three key findings related to the above question resulted. First, tax incentives that increase the number of active wind and solar facilities lead to a statistically significant (\(p<0.0001\)) reduction in wholesale electricity price (USD/MWh), ranging between 2.31% and 6.6% across the ERCOT during different hours of the day. Second, for a 10% increase in tax-incentivized green energy generation, during a 24-hour period, there is a statistically significant (\(p<0.0001\)) reduction in the generation cost (USD/MWh), ranging between 0.82% and 1.96%. Finally, electricity price reductions from solar energy are much lower than those from wind generation and/or are not statistically significant.  
kurtosis; real-time pricing; renewable energy; skew-t distribution; tax incentives