esa-sceva/satcom-chunk-collection huggingface dataset:
# Dilated Filters for Edge-Detection Algorithms
## 4 Experimental Results
### Effect of Dilation
#### 4.1.1 Operators Based on First-Order Discrete Kernels
It is not the case for Canny, where the extension technique does not help anymore. For the evaluation using \(SFOM\), presented in Figure 8, we used the same parameter variation values in order to respect the same evaluation methodology. From the results presented in Figure 8a, we can observe a clear degradation of the obtained edge map when considering the \(7\times 7\) extended kernel, with a maximum \(SFoM\) value of 0.379. When we look over the max peaks obtained when using \(5\times 5\), \(7\times 7\) dilated and \(5\times 5\), we observe that they are very close. Figure 8: Best results on the synthetic dataset for classic, extended and dilated using different parameters for: (**a**) first-order derivative orthogonal gradient operators; (**b**) first-order derivative compass gradient operators; (**c**) Frei-Chen edge operator; (**d**) Canny operator; (**e**) ED edge operator. When looking over the results from the compass first-order operators, Figure 8b, we can observe similar max peaks but the median values increase directly with the dilatation factor. In case of Frei-Chen edge operator, Figure 8c, we can observe a clear increase in the median values and max peak when using dilated kernels. An interesting evolution can be observed in the case of the Canny algorithm, Figure 8d, where expanding the kernel to \(5\times 5\) brings benefits to the resulting edge map but expanding to \(7\times 7\) produces a significant degradation of the results. In case of dilating the kernels, improvements can be observed with the peak max values reaching up to 0.679 for the \(7\times 7\) kernel. The exception from the trend comes when talking about the ED algorithm, Figure 8e. In this case, expanding to \(5\times 5\) or dilating to \(5\times 5\) produces similar results(0.658 for expanding and 0.668 for dilating). The difference occurs when looking at dilated \(7\times 7\) where we actually have a degradation of the results. The best results were actually obtained using the classical \(3\times 3\) with a value of 0.673.
## 3 Finite Element Modeling (FEM) of Human-Seat Interactions
### _Material Modeling and Challenges_  
Polyurethane (PU), widely recognized for its exceptional flexibility and durability, is extensively used in the automotive industry, particularly for car-seat cushions. Despite its advantages, PU exhibits strong nonlinear behaviors in both physical and geometric aspects, presenting significant challenges for accurate simulation using finite element methods (FEM). As indicated in multiple studies ([16; 19; 20; 21]), hyperelastic material models are commonly employed to describe the complex constitutive relationships of PU.
## 2 Theoretical Analysis of Moon-Based Earth Observational Angles
### Orbital Elements  
To describe orbital variations for the Sun and Moon, the orbits of the Sun and Moon are described based on orbital elements [38]. Note that the orbital elements are defined using Earth-centered ecliptic coordinates [39].  
The solar orbital elements can be simply obtained by assuming unperturbed motion of the Earth around the Sun, which mainly includes five orbital elements [40]. In contrast to the Sun, the lunar orbit is strongly affected by solar and terrestrial perturbations, and thus, six orbital elements are required to describe the complex lunar motion [41]. Therefore, additional parameters are required to calculate perturbations of the lunar orbit. The solar and lunar orbital elements and their parameters are listed in Table 1.
## 1 Introduction
The Zambezi River has two of the biggest dams in the world, the Kariba and Cahora Bassa, which both will be affected substantially by increased evapotranspiration and decreased precipitation. The Kariba dam forms Lake Kariba, which extends for 280 km and holds 185 km\({}^{3}\) of water [8; 14; 15]. The Cahora Bassa dam forms the Cahora Bassa Lake, which extends for 292 km and holds 56 km\({}^{3}\) of water. The flow of the Zambezi River will decrease by 40% or more due to climate changes, according to [5; 6; 14]. It is estimated that precipitation will be reduced by 15% and the evaporative losses will increase up to 25%. The runoff could be reduced by 30-40%. This may to a large extent be applicable for all the major river basins in Mozambique. A decrease in runoff reduces the economic benefits of a hydropower plant fed by the river, increasing the need for careful investment analysis of new hydropower plants. The climate change projections show that average temperatures in Mozambique will rise up to 4.6 \({}^{\circ}\)C between 2010 and 2090 [6; 9]. According to [1; 2; 16], the demand for electricity in Mozambique, where 15% more households have been connected to the grid the last 5 years, increases rapidly. In addition, the need for electricity in industry and public works further increases each year in Mozambique, meaning that the demand for electricity will continue to increase in the coming 30 years. According to [6; 14; 16] this implies a need for policy development in the energy sector, not only in Mozambique but within the entire region of southern Africa. A combination of excessive use of reservoirs and low precipitation directly reduces the electricity output from hydropower [8]. Future changes in climate could reduce the performance of power plants and reduce the economic output, as well as the power production [8; 10; 16]. This also decreases the economic growth in southern Africa, particularly in hydropower dependent countries like Zambia and Mozambique [5; 6; 13; 14]. To mediate these risks from climate variability and long-term climate change, there is a need for strong and cooperative governance arrangements to manage shared water resources with the help of integrated water resource management tools. The probable expansion of irrigation and construction of new dams and hydropower stations will intensify the need for cooperative governance between the countries sharing the trans-boundary rivers. Not least of concern is an economically viable structure which can cover costs and support additional investments, such as larger storage of water and additional alternative power supply sources to guarantee the future  
Figure 4: Projected increase in temperature and precipitation in Mozambique by 2030-2070, from [3]. delivery of power. Until 2070, the electricity demand in Mozambique is forecasted to increase by 5-7% per year from 10.9 TWh in 2010 [7; 8; 11; 12; 13; 14].
## 2 Related Works  
We can find few proposals in the literature that tackle this issue. All of them follow an approach similar to ours: they employ natural language processing, manual annotation, and machine learning to automatically tag privacy policies. However, they also use ad-hoc annotation categories that do not match or just partially cover the data protection goals stated in the GDPR.  
Specifically, in [5], the authors annotate policy statements according to items such as \"Collection of Personal Info\" or \"Personal Info is stored for Unlimited Time\". In [6], the authors tag statements according to categories such as \"First Party Collection/Use\", \"Third Party Sharing/Collection\", \"Policy Change\" or \"Do Not Track\". The system presented in [7] answers questions such as \"Does this website's privacy policy disclose whether data it collects about you is used in ways other than you would reasonably expect given the site's service?\" or \"Does this website's privacy policy disclose whether the site or service tracks a user's actual geolocation?\". Finally, the system proposed in [8] also provides answers to 10 questions regarding data management such as \"How does this site handle your email address?\" or \"Does this site track or share your location?\".  
In [9], the authors annotate policies with regard to 10 privacy aspects derived from pre-GDPR works. Even though some of these are similar or partially overlap with the privacy goals we consider in our work, the annotation criteria are not detailed and a filtering step is introduced to consider only the sentences containing a predefined set of keywords. Even though the latter simplifies the classification, it may also omit a significant number of sentences that could contribute towards a more accurate characterization. In [10], the authors present a dataset of privacy policy snippets annotated according to five handpicked privacy requirements. However, since the work focuses on measuring the performance of different classifiers, no privacy scores were derived from such annotations.  
Guard ([[https://useguard.com/](https://useguard.com/)]([https://useguard.com/](https://useguard.com/)) (accessed on 16 February 2021)), being a commercial product, is opaque regarding how statements are annotated: it simply grades privacy policies with a mark (being A+ the most privacy friendly, and F the worst) calculated from the number of generic privacy threats found in the policy and the number of scandals in which the company behind the policy has been involved.  
The results offered by these approaches either offer a partial view of the privacy goals stated in the GDPR or merely provide aggregated scores; even though they may allow ranking and comparison of policies, they do not explain the reasons and threats underlying to such scores.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 5 Conclusions  
This paper analyzes the typical tasks in the precise formation of distributed spacecraft. At present, the development of related mission requirements is diversified, which is reflected in the formation's shape, formation trajectory, detection performance, etc. In addition, laser ultra-long-distance precision formation and visual short-range precision formation are hot topics in mission development. Formation autonomy and robustness requirements are gradually increasing. The orbital distribution of formations is also gradually developing, from near-Earth orbit to deep space, which poses greater technical challengesin precision spacecraft relative state measurement technology, relative navigation and control technology, and measurement sensor hardware manufacturing.  
The field of distributed spacecraft formation measurement can be divided into non-autonomous measurement technology, autonomous measurement technology, and new composite relative measurement technology. This paper summarizes the specific working principles, effective range, accuracy, advantages, and disadvantages of GPS relative measurement, laser measurement, infrared measurement, radio frequency measurement, visual measurement, and new composite measurement. In response to the trend of overall space mission optimization, a multidisciplinary optimization architecture is proposed, to address the selection and optimization of measurement loads for distributed spacecraft formation flying. This can effectively resolve the competition relationship between the subsystems of the formation spacecraft. According to the mission requirements, the optimization of the relative measurement load is completed on the premise of ensuring the optimization of the spacecraft platform, and a balance between indicators and resource allocation is achieved. This completes the coordinated optimization of the platform and the payload while considering the rationalization of the weight, volume, and resource allocation of the satellite platform. Finally, an overview of potential development directions is outlined: spacecraft relative state measurement sensor fusion, distributed spacecraft formation measurement intelligence, robust relative measurement under partial faults, and software-defined distributed spacecraft formation measurement.  
Conceptualization, Z.Z. and L.D.; methodology, Z.Z. and L.D. software, n/a; validation, n/a; formal analysis, Z.Z., Y.Q. and D.L.; investigation, Z.Z., J.F., L.C. and Y.Q.; resources, Z.Z., L.D., L.C. and D.L.; data curation, Y.Q.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z.; visualization, Z.Z. and J.F.; supervision, L.C.; project administration, L.D.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Formation Flying Focusing Optics Hard X-ray Solar Telescope, grant number XDA15016800.  
Not applicable.  
The authors declare no conflict of interest.
# Long-Range Wireless Radio Technologies: A Survey
## 3 Discussion
### On Which Technology to Use
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Module** & **Connectivity** & **Infrastructure** \\ \hline
**LTE-M** & \$10–15 & \$3–5 / mo for 1 Mb \\ \hline
**NB-IoT** & \$7–12 & \textless{}$1 / mo for 100 kb \\ \hline
**Sigfox** & \$5–10 & \textless{}$1 / mo \\ \hline
**Ingenu** & \$10–15 & Unknown \\ \hline
**LoRaWAN Public** & \$9–12 & \$1–2 / mo \\ \hline
**LoRaWAN Private** & \$9–12 & \$0.25 / mo & \$500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cost to connect [47]. Figure 6: Hologram coverage [46]. be used (licensed spectrum) for this kind of \"local LPWAN\", another LPWAN technology based on the sub-GHz unlicensed bands such as the ones previously introduced could be used instead. To address this, we need an LPWAN technology which could enable FotA redistribution by the gateway once it has acquired the firmware via mobile communications and which costs less in terms of energy at the same time. Among the ones presented before, LoRa and Weightless-P are the best candidates for this. As mentioned previously, Weightless-P already support FotA updates. However, because of the modulation it uses, it offers a shorter range than LoRa. A combination of multiple LPWAN technologies (e.g., Weightless-P and Weightless-N or LoRa) could be used depending on the type of traffic to share data on a local scale, which may allow further energy savings. Considering all the above, we can see a simplified decision diagram in Figure 7. It can be followed to find which technology to use depending on the application requirements. We will introduce it with simple use case examples. First, let us consider an agriculture use case, where static devices remotely monitor weather metrics in fields. For this use case, we assume that the monitored fields are covered by an already deployed technology and that we do not want to operate our own infrastructure. Static monitoring of the weather does not generate an important volume of data (data rate \(<50\) kbps), but may need long-range communications as fields may be isolated (range \(>10\) km). Thus, the Sigfox technology is a choice worth considering. As a second example, let us consider a tracking system for patients in hospitals. Here, we may need to deploy our own base stations to cover special areas, e.g., basements, and be independent from any operator. We do not need a high data rate (data rate \(<70\) kbps), thus Ingenu and LoRa are interesting choices. As LoRa communications are known to be resilient against low speed mobility, LoRa may be the best choice for this use case.
## Appendix B  
From Equation (19), \(\lambda\) can be expressed as the following function of \(\gamma_{n,m,k}^{D}\).  
\[\lambda=D_{k}\cdot\frac{1+\gamma_{n,m,k}^{D}}{\left(H_{k,B}^{CB}H_{m,n}^{DD}- \widehat{H}_{m,B}^{DB}\widehat{H}_{k,m,n}^{CD}\Gamma_{k}^{C}\gamma_{n,m,k}^{D} \right)^{2}} \tag{18}\]  
Based on Equation (18), we can easily prove that \(\lambda\) is monotonically decreasing in \(\gamma_{n,m,k}^{D}\), as shown in Equation (19)  
\[\begin{array}{ll}\frac{\partial\lambda}{\partial\gamma_{n,m,k}^{D}}&=D_{k} \cdot\frac{H_{k,B}^{CB}H_{m,n}^{DD}+\widehat{H}_{m,B}^{DB}\widehat{H}_{k,m,n}^ {CD}\Gamma_{k}^{C}\cdot\left(\gamma_{n,m,k}^{D}+2\right)}{\left(H_{k,B}^{CB}H_{ m,n}^{DD}-\widehat{H}_{m,B}^{DB}\widehat{H}_{k,m,n}^{CD}\Gamma_{k}^{C}\gamma_{n,m,k}^{D} \right)^{3}}\\ &>0\end{array} \tag{19}\]  
Thus, the value range of \(\lambda\) can be obtained by substituting \(\gamma_{n,m,k}^{D}\in\left[0,\gamma_{n,m,k,\max}^{D}\right]\) into Equation (18).
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 2 Current Technologies for Railway Track Maintenance
### Road-Rail Vehicle  
Road-rail vehicles (RRV) are a unique type of maintenance equipment with both pneumatic and retractable rail wheels, as shown in Figure 3. These multipurpose maintenance machines can be used for transporting material or persons or as an inspection device with a proper sensor attachment. The Rail Industry Safety and Standards Board (RISSB) classifies RRVs into three main categories based on power transmission mechanism: self-powered, friction drive and direct drive RRVs [33]. Moreover, braking power, safety procedures during road-rail conversion and seating arrangements are also standardized by the Rail Safety and Standard Board (RSSB) to reduce accidents and maintain safety [34].  
Figure 3: Road–rail vehicles for track maintenance: (**a**) Unimog road rail vehicle for overhead line maintenance [35]; (**b**) mobile asset management RRV [34].  
Figure 2: Track geometry inspection trolley: (**a**) track geometry validation trolley [31]; (**b**) track geometry inspection trolley with GPS [32].  
High-load-carrying RRVs are usually used for repair or construction tasks, while smaller RRVs are used for inspection. For example, Unimog, shown in Figure 2(a), from Mercedes-Benz [35] can tow up to 1000 t, and has served the railway industry for a long time. A range of instruments can be installed in to the RRVs to convert them into a mobile asset management platform for surveying and inspecting the railway environment to assess the condition of road-rail access points, vegetation, and track infrastructure. Figure 2(b) shows a mobile asset management platform which can provide LiDAR point cloud and images at a maximum speed of 20 mph [36]. RRVs are much faster than manual inspection trolleys and can carry more weight, as demonstrated by Runner Wizard V2R [37]. The localization of defects and payload deployment are still challenging and control of RRVs remains in the hand of a human operator.
# Vertex-Weighted Consensus-Based Formation Control with Area Constraints and Collision Avoidance
## 2 Problem Formulation  
### Kinematic Model Formulation  
The unicycle kinematics model is given by  
\[\begin{split}\dot{x}_{i}&=v_{i}\cos(\theta_{i})\\ \dot{y}_{i}&=v_{i}\sin(\theta_{i})\\ \dot{\theta}_{i}&=\omega_{i}.\end{split} \tag{1}\]  
Non-holonomic constraints make the design of \(v_{i}\) and \(\omega_{i}\) difficult, such as \((x_{i},y_{i})\) approaching a zero state. For this, we define  
\[\begin{split} x_{i}^{L}&=x_{i}+L_{i}\cos(\theta_{i}) \\ y_{i}^{L}&=y_{i}+L_{i}\sin(\theta_{i}),\end{split} \tag{2}\]  
where \(L_{i}\
eq 0\). After evaluating (1) in the time derivative of (2), we obtain  
\[\begin{bmatrix}\dot{x}_{i}^{L}\\ \dot{y}_{i}^{L}\end{bmatrix}=\begin{bmatrix}\cos(\theta_{i})&-L_{i}\sin( \theta_{i})\\ \sin(\theta_{i})&L_{i}\cos(\theta_{i})\end{bmatrix}\begin{bmatrix}v_{i}\\ \omega_{i}\end{bmatrix}. \tag{3}\]  
The following input transformation can be used to drive the robot  
\[\begin{bmatrix}v_{i}\\ \omega_{i}\end{bmatrix}=\begin{bmatrix}\cos(\theta_{i})&-L_{i}\sin(\theta_{i} )\\ \sin(\theta_{i})&L_{i}\cos(\theta_{i})\end{bmatrix}^{-1}\begin{bmatrix}\dot{x}_ {i}^{L}\\ \dot{y}_{i}^{L}\end{bmatrix}, \tag{4}\]  
where \((\dot{x}_{i}^{L},\dot{x}_{i}^{L})\) will be designed for formation control purposes.  
Figure 1: Differential-drive robot model.
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 2 5G System  
The so-called 5G system (5 GS) encompasses the 5G core network (5 GC), a new radio interface known as 5G New Radio, and User Equipment (UE) specifically designed for 5G [17, 26]. Fifth-generation systems are in the process of development to establish connections between UE and the 5G network infrastructure [17].  
In Release 16, 3 rd Generation Partnership Project (3 GPP) focused on a comprehensive expansion of the 5 GS. This expansion includes satellite-based 5G access, vehicle-to-everything (V2X) communication services, wireless and wired networks integration with 5G, and other advancements [27]. Additionally, functionalities like using NR technology in unlicensed frequencies, integrated access backhaul (IAB) approaches, large-scale multiple-input multiple-output (MIMO) enhancements, segmented resource allocation, and new capabilities for URLLC and industrial IoT, as well as support for non-public network (NPN)implementations, are incorporated [27]. These features are considered crucial to ensure that networks meet essential requirements.
## 5 Performance Evaluation
### Total Energy Consumption in Scenario  
Energy consumption of the mobile device and fog nodes is measured in terms of a kilowatt (kW) while executing the benchmark applications on the mobile cloud system; each mobile device has two interfaces such as local execution interface and network interface. Moreover, a previously mentioned interface directly affects the mobile device energy consumption during local execution and offloading application tasks. Thus, the tradeoff balance between local energy consumption and offloaded energy consumption in the proposed mobile fog environment is achieved. Whereas, the aim of a proposed joint energy efficient algorithm allows applications to execute application tasks among multiple resources such as local resource, network resource and fog resources in such way that total energy consumption is minimized.  
Figure 7 shows that JETA outperforms and minimizes the tradeoff energy consumption for all application tasks to the baseline approaches such as MAUI, CloneCloud, and Think Air. These approaches are just on minimizing the energy consumption of the mobile device by the offloading technique in the mobile cloud environment. They ignored the fog nodes or server energy consumption during execution. JETA technique can optimize both user and provider end energy efficiency instead of partial optimization, which is done in existing studies.  
Figure 5: Workflow application: Number of tasks completed within a given deadline.  
Figure 6: Workflow application number of tasks completed within a given deadline.  
Figure 8 illustrates that; total energy consumption and mobile energy consumption can be saved efficiently by proposed JETA algorithm as compared to the prior studies. This idea is unique, to optimize the energy consumption from both user and providers for a similar application which will be executed partially among a mobile, a network, and a 231 cloud resource simultaneously. Previous research on offloading mechanisms is based on the code development, app performance increment, resource discovery and allocation, and Elastic execution between mobile devices and clouds [36]. While adapting the application partitioning, JETA model is based on data flow and task assignment with less energy consumption on the same number of tasks as compared to fog nodes and offloaded mobile nodes.
## 4 Proposed Algorithm  
In this paper, an algorithm is proposed to detect movement noise and mitigate the impact of mobility on SDPPG measurement by using multipath signals and the wavelet transform. First, SDPPG signals are measured at different locations on a body, such as fingers and ears, and background noise is removed using a low-pass filter. The algorithm determines a reference signal using the measured SDPPG signals. Time offsets between received signals are aligned in order to compare the measured signals. To determine whether the measured signals include movement noise, the wavelet transform is applied to each signal. Based on the composed signals in the frequency domain, the movement noise is detected and removed. The algorithm procedure is summarized in Figure 7 and the details of each step will be explained in the following subsections.  
Figure 6: Wavelet packet decomposition.  
Figure 7: The overall procedure of the proposed algorithm.