esa-sceva/satcom-chunk-collection huggingface dataset:
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 3 A Model for Contact Information Overhead
### Contact Information Size  
While the amount of information exchanged between nodes in order to compute forwarding utilities may vary across protocols, the structure is the same as that of PRoPHET. A node that runs on PRoPHET maintains a set of contact information, \(C\), for every encountered node, \(h\). Let \(C=\{c_{1},\,c_{2},\,c_{3},\,\ldots,\,c_{n-1}\}\) be the set of contact information maintained at a node running on PRoPHET. Each contact information \(c_{h}\ \in\ C\) is a tuple in the form of \(c_{h}=\ \langle h,\ u_{h}\rangle\), where \(u_{h}\) is the node's forwarding utility (which PRoPHET's authors refer to as \"delivery predictability\") for an encountered node \(h\). For analytical purposes, \(h\) and \(u_{h}\) are assumed to be a string and a double data type of \(X_{S}\) and \(X_{D}\) bytes, respectively. The size of \(c_{h}\), a piece of contact information, becomes \(X_{S}+\ X_{D}\) bits. The transitive property of PRoPHET allows a node to maintain delivery predictabilities for nodes it has never encountered, which are determined from contact information received from neighbors that have previously encountered them. This hastens the rate at which contact information is disseminated within the network. Consequently, the number of nodes for which contact information is maintainedeventually reaches \(n-1\) (i.e., \(|C|=n-1\)), where \(n\) is the total number of nodes in the network. The size of the contact information maintained at each node is given by Equation (1).  
\[S_{C}=(n-1)(X_{S}+X_{D})\text{ bytes} \tag{1}\]  
For example, consider the Universiti Teknologi Malaysia (UTM) campus which is located on 11.45 KM\({}^{2}\) of land [55] and registers about 18,000 students (as of 2017) [56]. It is fair to say that every student owns a portable handheld device. An IoT scenario would also consist of other nodes such as smart vehicles, sensors, access points and appliances equipped with short-range wireless communication interfaces. So let us assume that 80% of the students live in-campus and other nodes besides portable handheld devices account for 40% of the total node population in the scenario. Then, the number of portable handheld devices and other nodes becomes 14,400 and 6000, respectively, making a total of 24,000 networking nodes. Taking \(X_{S}\) and \(X_{D}\) as 10 bytes (i.e., a string of 5 characters) and 8 bytes, respectively, a portable handheld device would eventually maintain about 0.432 MB of contact information (using Equation (1)). Hence, portable handheld devices would have to send and receive a total of about twice this amount (i.e., 0.864 MB) of contact information whenever they encounter each other.
# Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures
## 3 Results
### Hybrid Method Results  
Logistic regression analysis was used to model the relationship between the 18 seismic parameters and the binary output, representing whether the structure collapsed. More specifically, the response results obtained by time history analyses are divided into two categories, i.e., the nonoverturning and overturning classes. The equation of logistic regression was deduced in the following manner:  
\[\ln\left(\frac{\pi}{1-\pi}\right)=B_{0}+B_{1}X_{1}+B_{2}X_{2}+\ldots+B_{18}X_{ 18} \tag{25}\]  
The results of the model parameters are demonstrated in Table 14 in the Appendix A. For assessing the accuracy of the logistic regression model and quantifying the extent of errors, the log loss function was determined according to the following equation:  
\[\text{Log loss}=-\frac{1}{n}\overset{\text{n}}{\underset{i=1}{\sum}}(y_{i} \text{log}(\text{p}_{i})+(1-y_{i})\text{log}(1-\text{p}_{i})) \tag{26}\]  
in which \(y_{i}\) were the observed binary outputs and \(\text{p}_{i}\) were the probabilities of every case. The results of the log loss function are illustrated in Table 6.  
Then, based on the aforementioned logistic regression parameters, the probability \(\pi\) of every case was computed according to Equation (19). The results that emerged were subjected to three models of fuzzy linear regression analysis, in which the inputs were the 18 seismic parameters, and the output was the probability for every structure. This hybrid approach assessed the degree of similarity among the three structures. By employing this method, it became possible to integrate the principles of fuzzy logic with logistic regression, thereby facilitating their combined utilization. The results of the similarity ratio \(\lambda\) are presented in Table 7.  
Through the examination of the outcomes, it was determined that the second and third structural models exhibited the highest degree of similarity ratio value.  
The same procedure was followed to evaluate the influence of individual input variables on the similarity ratio of our model. For that purpose, 18 logistic regression models comprised of one input, were analyzed. The results of the log loss function are summarized in Table 8. The subscript notation in Log Loss signifies the structure for which the log loss function is applied.  
\begin{table}
\begin{tabular}{c c} \hline
**Structure** & **Log Loss** \\ \hline
1 & 0.008 \\
2 & 0.018 \\
3 & 0.019 \\ \hline \end{tabular}
\end{table}
Table 6: The results of the log loss function for logistic regression.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\lambda\) & **Value** \\ \hline \(\lambda_{1,2}\) & 0.108 \\ \(\lambda_{1,3}\) & 0.203 \\ \(\lambda_{2,3}\) & 0.533 \\ \hline \end{tabular}
\end{table}
Table 7: Estimated similarity ratios for hybrid analysis.  
Then, the probability \(\pi\) of every case was subjected to fuzzy linear regression. The outcomes pertaining to the triangular fuzzy coefficients are presented in Appendix A Table A4, and the values indicating the similarity ratio \(\lambda\) are displayed in Table 9.
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 2 Literature Review
### Real-Time Detection  
Detecting various types of objects is a crucial aspect in many real-world applications, especially in industries such as surveillance [20] or industrial production [32]. The two most renowned real-time inference-focused detectors are YOLO [9] and Single Shot Multibox Detector (SSD) [33]. YOLO is notable for the numerous updates it has received from the community through various versions [34; 35; 36; 37; 38; 39; 40]. Additionally, there are several modifications of specific versions that aim to improve efficiency [41; 42; 43], accuracy [44; 45], or both [46; 47; 48]. As for SSD, [PERSON] et al. [49] integrated a parallel network and a bi-directional FPN (BiFPN) to enhance SSD.  
In alternative approaches, some researchers strive to build more efficient backbones on top of existing ones [50; 51; 52; 53], while others prune the detector to enhance efficiency while maintaining accuracy through improvements [54; 53]. Since the introduction of vision transformers [55], researchers have proposed efficient transformers, such as the work of [PERSON] et al. [56] or the work of [PERSON] et al. [57]. [PERSON] et al. [58] suggested the use of partial dense blocks and partial transition layers, replacing the FPN with an Exact Fusion Model (EFM) to make existing methods more efficient. Similarly, [PERSON] et al. [59] implemented a BiFPN and a compound scaling method to improve both the accuracy and efficiency of EfficientNet [60]. On the other hand, [PERSON] et al. [61] propose their detector, called Sparse R-CNN, by replacing the RPN for a small set of proposal boxes and by removing Non-Maximum Suppression (NMS) in the post-process stage.  
Given that real-time detection is a key factor in our work, the well-known one-stage detector YOLO was utilised. YOLO has been widely employed in various areas and has undergone continuous improvements in recent years. Each new version of the architecture has been modified to strive for better performance while maintaining or reducing the inference time.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 1 Introduction  
Advertising has played a very significant role in the expansion of the digital media industry over the past few years. The ability to generate revenue through the publishing of adverts allows companies to offer a wide range of services without any cost for users while still making profit.  
Although digital advertising has been around for almost as long as the Internet itself, the latest financial studies suggest that the advertising market has experienced a rapid increase in recent years with a prime focus given to adverts that are specifically designed to target mobile devices. This shift towards mobile ads is driven by feedback from the online browsing habits of the consumers themselves [1]. Research analysis indicates that on average, an adult will spend more than 2.5 h a day on his/her smartphone [1]. In the U.K. alone, spending on mobile ads has increased by 45% in the year 2015. This accounts for an expenditure of 3.2 billion pounds, which is predicted to further increase by another 35% in the following years [2]. With such investments on mobile advertising, it should come as no surprise that a great deal of effort is focused towards effective advert targeting.  
Online Behavioural Advertising (OBA) is a highly effective method for distributing content-aware adverts that target consumers based on their individual interests [3]. From a business point of view, OBA is beneficial to both vendors and consumers as the former increase revenue and the latter are onlypresented with ads that are relevant to their needs. Regardless of the commercial benefits, targeted advertising raises serious privacy concerns.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.7 Autonomous UAV Landing I  
In [75], the authors developed a system that allows a UAV to land autonomously on a shifting base. The authors designed a framework using DDPG, defining state and action spaces, creating a reward function, and testing the trained agent in different scenarios. The authors successfully demonstrated the feasibility of training a complex robotic task in simulation and in transferring the learned policy to a real-world application.  
The DDPG algorithm is utilized for tasks that require continuous control. The UAV learns to adjust its actions according to its state by interacting with the environment. This work heavily utilizes an ROS to facilitate communication and integration between various components of the system. The ROS facilitates data exchange between the UAV, the environment, the velocity controller, and the Gazebo simulator throughout the training phase. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work represents a significant advancement in the field of UAV. This work solves the problem of transferring learned models from simulation to the real world and shows that RL with ROS integration can handle difficult UAV tasks. The framework can easily integrate with existing robotic systems due to its compatibility with an ROS and Gazebo. Overall, this research contributes valuable insights to the advancement of UAV technology.
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 3 Results
### Distribution of Operating Cost Components
#### 3.1.1 Drilling  
Figure 2 visually represents the cost distribution across the various components contributing to the total cost associated with the drilling operation.  
The distribution of operating cost components associated with drilling is predominantly driven by consumables, which account for 49% of the total cost. Fuel is another major contributor, making up 41%. These two components (i.e., consumables and fuel) constitute most of the expenses relating to drilling. On the other hand, maintenance and repairs, labor, and equipment depreciation are the least costly components, representing a combined 9% of the total drilling cost. These three cost components are deemed small since they collectively have a minimal impact on drilling costs compared to the consumables and fuel.
## 2 Project Overview
### Construction Safety Control Technology
#### 3.6.3. Grouting Control Technology  
(1) Synchronous grouting  
The excavation sequence of shield tunneling is from the left line to the right line, and synchronous grouting is carried out during excavation. Cement mortar is selected as the synchronous grouting slurry, mainly composed of cement, hydrated lime, fly ash, bentonite, sand, and water. The setting time is controlled within 4-8 h, with high strength in the early and late stages. According to the geological conditions and excavation speed, the setting time is adjusted by adding accelerators and changing the ratio through on-site testing. For areas with strong permeability and high early strength requirements for grouting, the ratio and early strength agent added are further adjusted through on-site testing to shorten the setting time and ensure a good grouting effect. The grouting pressure is set at 0.3 MPa, and the filling coefficient is set at 1.3-2.5, which can be adjusted according to the actual situation on the construction site. According to shield tunneling construction experience, the grouting amount is taken as 130% to 250% of the gap in the shield tail building. The synchronous grouting speed should match the excavation speed, and the average grouting speed should be determined based on the amount of grouting completed within the 1.2-m excavation time of the shield machine in order to achieve uniform grouting.  
(2) Secondary grouting  
Secondary grouting is an effective auxiliary method to reduce surface subsidence. After completing the assembly of about 10 rings in the shield tunnel, inject secondary (or multiple) grouting into the lining ring, as shown in Figure 8, to compensate for the shortcomings of synchronous grouting. On the basis of synchronous grouting, cement water glass dual liquid slurry needs to be injected to control ground settlement. For this purpose, cement water glass dual liquid grouting was carried out through ground drilling holes with a secondary grouting slurry ratio of water/water glass = 3:1 (weight ratio); cement slurry/water cement ratio = 1:1 (weight ratio); and cement slurry/water glass slurry ratio = 1:1 (volume ratio). The grouting pressure is 0.3-0.4 MPa, and secondary grouting is carried out after 5-10 rings of shield tail detachment. The grouting amount is dynamically adjusted based on on-site settlement monitoring data.
## 4 Performance and Collaboration Analyses Results
### Analysis of Trend in Articles  
The annual distribution of articles is displayed in Figure 1. The annual number of articles had increased from four in 1999 to 53 in 2017.  
Until 2005, the number was around seven by year, which is quite low. From 2006 until 2011, the number increased to triple, which was around 20. Furthermore, from 2012 until 2017, it was around 45. Thus, the research area had received growing interest and attention from academia during all theseyears. However, the articles collected with the publication year of 2018 had experienced a sudden drop to 24, which might have partially resulted from time-lag for academic papers to be indexed by academic databases. Thus, there was a great possibility that some articles published in 2018 had not been indexed in the retrieval databases.  
Polynomial regression analysis was further conducted to fit the article trend with the _year_ as an independent variable. For obtaining a better fitting efficiency, we utilized the period 1999-2017 for regression fitting. The fitted polynomial regression curve with a positive coefficient of \(x^{2}\) (\(R^{2}\) = 0.879) demonstrated a growing interest and enthusiasm for the research topic.
# An Efficient GPS Algorithm for Maximizing Electric Vehicle Range
## 3 Mathematical Modeling and Proposed Algorithm
* Repeat the process for alternative routes: The flowchart indicates that the previous steps are then repeated for alternative routes. This allows for a comparison of the energy consumed on each route. * Select the optimum route for the selected EV model: Finally, after calculating and comparing the energy consumption of different routes, the flowchart concludes by selecting the optimum route for the selected EV model. The selection is likely based on the route that consumes the least amount of power or offers the best energy efficiency. Overall, the flowchart provides a systematic approach to evaluating and comparing the consumption of different routes to determine the most energy-efficient option for a particular EV model [36]. Figure 4: Flowchart for comparing two suggested routes and their energy calculation method.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 4 Simulation and Analysis
### Resolution Comparison  
In order to investigate the imaging resolution, an experiment of resolution comparison is carried out. The simulation parameters are the same as Section 4.1, and the scattering model of an extended target in the 2D plane is illustrated in Figure 5.  
Figure 4: Position estimation of six scatterers using two frequency sets.  
Figure 6a corresponds to the reconstructed passive radar image obtained through the polar format algorithm (PFA) [10], and Figure 6b shows the passive radar imaging result reconstructed by the proposed method. We note that the passive radar image produced by our proposed method has better reconstruction performance than that of the PFA algorithm. Moreover, the proposed method can obtain higher resolution in the \(x\)- and \(y\)-direction than the PFA algorithm. This implies that our proposed method can achieve super-resolution. In addition, we find that when these scatterers are not on the grid, the proposed CS method can still obtain the focused image.
## 3 Additional Damping Controller Design
### Gain  
The effectiveness of the additional damping control on the SSO does not depend solely on the choice of the feedback signal of the access point and the design of the filter, but also on the design of a well-designed gain link. By appropriately setting the gain coefficient, the control system can promptly and precisely output the corresponding voltage signal upon detecting the SSO, thereby effectively curbing the oscillation's progression and enhancing the overall stability and reliability of the power system. Within a certain parameter range, increasing the gain K leads to a more effective suppression of the SSO. However, the limiting link needs to be considered, so it should not be set too large.
# Optical Helicity and Chirality: Conservation and Sources
## 4 Macroscopic Sources
### Helicity in Bi-Isotropic Media
We find:  
\[\mathbf{\
abla}\cdot\mathbf{v} =\frac{1}{2}\left(\sqrt{\frac{\epsilon}{\mu}}\left(\mathbf{A} \cdot\left(\mathbf{\
abla}\times\mathbf{E}\right)-\mathbf{E}\cdot\left(\mathbf{\
abla }\times\mathbf{A}\right)\right)-\sqrt{\frac{\mu}{\epsilon}}\left(\mathbf{C} \cdot\left(\mathbf{\
abla}\times\mathbf{H}\right)-\mathbf{H}\cdot\left(\mathbf{\
abla }\times\mathbf{C}\right)\right)\right)\] \[=\frac{1}{2}\left(\sqrt{\frac{\epsilon}{\mu}}\left(-\mathbf{A} \cdot\dot{\mathbf{B}}-\mathbf{E}\cdot\mathbf{B}\right)-\sqrt{\frac{\mu}{ \epsilon}}\left(\mathbf{C}\cdot\dot{\mathbf{D}}+\mathbf{H}\cdot\mathbf{D} \right)\right), \tag{31}\]  
and use the product rule to write this as:  
\[\mathbf{\
abla}\cdot\mathbf{v}=-\partial_{t}\frac{1}{2}\left(\sqrt{ \frac{\epsilon}{\mu}}\mathbf{A}\cdot\mathbf{B}-\sqrt{\frac{\mu}{\epsilon}} \mathbf{C}\cdot\mathbf{D}\right)-\left(\sqrt{\frac{\epsilon}{\mu}}\mathbf{E} \cdot\mathbf{B}-\sqrt{\frac{\mu}{\epsilon}}\mathbf{H}\cdot\mathbf{D}\right). \tag{32}\]  
Identifying the time derivative as \(\partial_{t}h\) and inserting the constitutive relations (28) with \(\alpha=0\) leads to:  
\[\mathbf{\
abla}\cdot\mathbf{v}=-\partial_{t}h-\sqrt{\epsilon\mu}\beta \left[\mathbf{E}\cdot\dot{\mathbf{D}}+\mathbf{H}\cdot\dot{\mathbf{B}}\right], \tag{33}\]  
which is rearranged to produce:  
\[\partial_{t}\left(h+\sqrt{\epsilon\mu}\beta w\right)+\mathbf{\
abla} \cdot\mathbf{v}=0, \tag{34}\]  
where \(w=1/2\left(\mathbf{D}\cdot\mathbf{E}+\mathbf{B}\cdot\mathbf{H}\right)\) is the energy density in an achiral medium. Equation (34) is correct to first order in the chirality parameter, denoted \(\mathcal{O}(\beta)\), by which we mean that we neglect terms multiplied by \(\beta^{2}\), or higher powers. This is incorporated into the definition of the helicity density \(h\) to form [46]:  
\[\mathbf{B} =\mu\left(\mathbf{H}+\beta\mathbf{\
abla}\times\mathbf{H}\right)+ \alpha\mathbf{E}, \tag{28}\]0  
It is further shown in [46] that this indeed produces a helicity to energy density ratio of \(\pm 1/\omega\) for right- and left-circularly polarised light, leading to a helicity of \(\pm\hbar\) per photon [47]. In addition, it can be shown that higher order terms in \(\beta\) of the helicity and energy densities retain this correspondence between the definitions, i.e. :  
\[\mathbf{B} =\mu\left(\mathbf{H}+\beta\mathbf{\
abla}\times\mathbf{H}\right)+ \alpha\mathbf{E}, \tag{28}\]1  
such that \(\partial_{t}w_{n}+\mathbf{\
abla}\cdot\mathbf{S}  
We can examine the general form of a source of helicity in lossless non-reciprocal media by inserting the constitutive relations (28) with \(\alpha\
eq 0\) into \(h_{1}\) and \(\mathbf{v}\), producing:  
\[\mathbf{B} =\mu\left(\mathbf{H}+\beta\mathbf{\
abla}\times\mathbf{H}\right)+ \alpha\mathbf{E}, \tag{28}\]2  
where we have imposed \(\mathbf{\
abla}(\epsilon/\mu)=0\). As the energy is conserved [47], it follows that the helicity per photon of the light within a Tellegen material must differ from the familiar free-space values.
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 3 System Modeling
### Stochastic Load  
The equations of the stochastic voltage-dependent load model are proposed in [32] as follows:  
\[\begin{split} p_{L}(t)&=(-p_{L0}+\eta_{p}(t))(v(t)/ v_{0})^{\gamma}\\ q_{L}(t)&=(-q_{L0}+\eta_{q}(t))(v(t)/v_{0})^{ \gamma}\\ \dot{\eta}_{p}(t)&=\alpha_{p}(\mu_{p}-\eta_{p}(t))+ b_{p}\xi_{p}\\ \dot{\eta}_{q}(t)&=\alpha_{q}(\mu_{q}-\eta_{q}(t))+ b_{q}\xi_{q},\end{split} \tag{9}\]  
where \(p_{L0}\) and \(q_{L0}\) are the active and reactive powers at \(t=0\); \(v(t)\) is the voltage magnitude of the bus where the load is connected; \(v_{0}\) the voltage magnitude at \(t=0\); and \(\gamma\) is the power exponent where \(\gamma=0\) means constant power loads; the \(\alpha\) terms are the mean-reversion speed at which the stochastic variables \(\eta\) are dragged towards the mean values \(\mu\); and the \(b\) terms represent the volatility of the processes.
## 4 Experiment and Discussion
### Worldwide Availability Coverage Test
#### 4.1.3 Results under Depleted Scenario
However, the availability results under ARAIM with four constellations and ARAIM with constellation dynamic selection are much better than those of ARAIM with two fixed constellations. All values in these two modes exceeded 99% and the results of the latter mode are much close to those of the former one. The mean values are as high as 99.81% and 99.62%, respectively. The proposed constellation dynamic selection method shows strong applicability and generality under the current dynamic developing GNSS constellations. Besides the aerospace application, it is of great help to promote the application of ARAIM in autonomous vehicles and intelligent transportation system.