esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Secured Access Control in an IoT System
## 2 Background of Access Control System in IoT
### Access Control Challenges in IoT  
The main challenges of applying existing access control mechanisms in the IoT environment are as follows:  
1. Reusability of existing solutions. Access control mechanisms have been extensively studied in the literature and successfully deployed but these solutions cannot be applied directly to an IoT infrastructure, as they are complex and do not fit IoT requirements. Building a solution from scratch takes time to implement, deploy and adopt [1].
2. Centralized vs. distributed access control mechanisms Centralized solutions provide control policies that are easily accessible but which possess a single point of failure. Centralized state-of-the-art security frameworks that are not suitable for the IoT environment are constrained by scalability issues [8]. In a centralized end-to-end mechanism, the user is not involved in the access control of their data. The distributed solution ensures privacy at a low cost and does not involve third-party trust. These distributed solutions are difficult to manage and require device-side access control policy updates.
3. Scalability Interconnected devices are increasing at a significant speed, which, in turn, increases their management workload. A decentralized and distributed access control mechanism must provide scalability in order to accommodate the ever-growing number of homogeneous and heterogeneous IoT devices [9].
4. Heterogeneity IoT infrastructure is distributed and incorporates multiple heterogeneous, interconnected devices with different underlying technologies, originating from multidisciplinary domains [1]. These technologies, under each domain, have different underlying authentication and authorization policies that make heterogeneity a major hindrance to providing a scalable, robust and secure IoT environment.
5. Resource constraints IoT devices are bound by computation and storage capabilities and are connected via low-power and loss-prone networks. The access control system must provide an easy solution to address the abovementioned challenges within the resource constraints [10]. Consequently, the IoT environment required additional requirements to be considered in the access control mechanism, particularly distribution, scalability, heterogeneity and lightweight architecture.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Access Control Models** & **Limitations with IoT Environment** \\ \hline \multirow{2}{*}{ACL} & Lack of granularity and scalability. \\  & Prone to a single point of failure due to its centralized infrastructure. \\ \hline \multirow{2}{*}{RBAC and ABAC} & Lack of complexity. \\  & Not supporting user-driven. \\  & Policy management becomes a critical issue. \\ \hline \multirow{2}{*}{CapAC} & Propagation issue. \\  & Lack of context consideration. \\ \end{tabular}
\end{table}
Table 1: Access control drawbacks with Internet of Things (IoT) environment.
# Development of a Social DTN for Message Communication between SNS Group Members
###### Abstract  
Smartphones have the ability to communicate with other terminals through ad hoc connections. A variety of applications have been developed to exploit this ability. The authors have developed an Android OS (operating system) application (called \"social DTN manager\") that builds a DTN (delay, disruption, disconnection tolerant networking) among members of a social networking service (SNS) community using a community token. The members can exchange messages over this network. Control messages for building a DTN are forwarded to only those nodes that use the same community token in order to reduce flooding of message copies. When a source node sends a communication request to its destination node, they exchange control messages to establish a communication route. Relay nodes use these messages to create and hold routing information for these nodes in their routing tables. Thereafter, relay nodes can forward data messages based on their routing tables. This again reduces flooding of message copies. The social DTN manager incorporates these functions, Facebook Graph API and Google Nearby Connections API. The authors have installed it in Android terminals and confirmed that a social DTN can successfully be built using this application and that data messages can be exchanged between terminals via reactive routes.  
D 2018, 10.32; doi:10.3390/610040032
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 5 Results  
### Experimental Set-Up And Methods  
A total of 45 flight test data samples were used to validate the proposed RTK technique with up to four UAVs. Figure 13 shows an example flight path of the four UAVs and one of our UAV testbed used for the flight test. Each UAV followed pre-programmed flight trajectories using a Pixhawk flight controller and a standalone GNSS receiver (3 DR, Chula Vista, CA, USA) for about 10 min in a test. The speed of the UAVs was between 1.0 and 3.5 m/s at an altitude of 10 m. During the flight tests, separate Ublox ZED-P9P GNSS receivers (Ublox AG, Thalwil, Switzerland) and Trimble AV17 GNSS antennas (Trimble, Sunnyvale, CA, USA) were mounted on the UAVs to capture dual-frequency code and carrier phase measurements of GPS, GAL, and BDS constellations at a 1 Hz rate. The dual-frequency raw measurements were stored on a memory card within a Raspberry Pi 4 mini computer (Raspberry Pi LTD, Cambridge, England) and subsequently processed to generate RTK position solutions in a post-processing mode using Matlab software version R2022a. To validate the proposed algorithms, the true relative position vectors between the UAVs were determined by subtracting RTK solutions between each UAV and a nearby continuously operating static reference station in Suwon, South Korea, using open-source RTKLIB software version 2.4.3 b34 and TRIMBLE TBC software version 1 [34; 35].  
Figure 12: Ratio values from the flight test #34: main-KF and temp-KF.  
Figure 13: (**a**) The flight path of four UAVs during the flight tests conducted to collect GNSS data. (**b**) A hexacopter UAV used for the flight test. The colors of the lines were employed to differentiate the flight paths of each UAV.
## 2 Related Work
### LLM-Based Exam Question Generation  
Several studies have explored the automated generation of exam questions using LLMs. The authors in [5] provided one of the initial insights into automated exam content creation, implying that the research on question generation predominantly employed syntax-based or template-driven methods. More recent studies have leveraged state-of-the-art LLMs--such as GPT-3, GPT-4, and T5--to generate a wide array of question types. For instance, the authors in [17] demonstrated that few-shot prompt engineering with Chat-GPT4 models can be effective for generating reading comprehension questions, while the authors in [18] demonstrated the application of LLMs to create multiple-choice questions for computing courses. Although these studies showcase the technical feasibility of automated question generation, they often evaluate the generated questions on isolated quality metrics without a deeper analysis of alignment with course objectives or the provision of human-understandable explanations. Our work distinguishes itself by systematically assessing alignment and improvement tasks and by quantifying performance variations across different course types.  
Other research efforts have sought to refine question generation by integrating domain-specific strategies. The authors in [19] introduced a method that fine-tunes LLMs in accordance with Bloom's Taxonomy to generate questions across various cognitive levels, and the authors in [20] incorporated knowledge graphs to improve domain specificity in cybersecurity education. Specialized approaches, such as domain-adapted training on scientific texts, demonstrate the scalability of QG methods [7,21], and modular QG pipelines that separate question stem generation from distractor formulation further enhance reliability [22,23]. Furthermore, an AI-educator workflow for generating math multiple-choice questions was presented in [24], while distractor quality through topic modeling was presented in [25] orontology-based methods presented in [26]. Some recent work evaluates ChatGPT-generated assessments in probability and engineering statistics, noting mismatches in scoring schemes and inadequate coverage of higher-level cognitive domains [27]. Another study validates multi-task learning techniques for producing high-quality question-answer pairs in diverse subjects [28]. Yet, these studies typically consider question generation as a stand-alone task, rather than as part of an integrated framework that includes post-generation evaluation and iterative improvement.
## 4 Conclusions  
In this study it has been proven that by combining various procedures and exploiting their advantages, it is possible to develop a high-efficiency chromium recovery methodology from tannery sludge. As a first step, the application of thermal treatment can reduce the volume of waste almost by half, albeit oxidizing also the initially present Cr(III) to its toxic Cr(VI) hexavalent form. However, by applying a simple hydrometallurgical acid treatment in the resulting ashes, over 90% of the total Cr content can be extracted, due to Cr(VI) high solubility (the process also being highly selective). Finally, the electrochemical treatment using porous carbon cathodes (RVC), under appropriate sulfuric acid concentration and applied current/potential conditions, can result in a Cr(VI)-to-Cr(III) conversion (reduction) above 95%. The presented methodology highlights the benefits of the circular economy, both environmental and economical, as chromium circulates in a tannery-based route, through its recovery, with minimum losses. As future research, according to the present work, we recommend the clarification of other ions' effect, contained in the leachate,  
Figure 10: Concentration of Cr(VI) vs. time plots during the electrolysis of 4.1 g\(\cdot\)L\({}^{-1}\), 800 mL leachate samples in a batch-recirculation type reactor with a 33 L\(\cdot\)h\({}^{-1}\) flow rate, using an RVC cathode electrode and a constant potential of 3 V.  
Figure 9: Concentration of Cr(VI) vs. time plots during the electrolysis of a 5.1 g\(\cdot\)L\({}^{-1}\) leachate samples in a batch-recirculation type reactor, treating 800 mL with a 33 L\(\cdot\)h\({}^{-1}\) flow rate, using an RVC cathode electrode and a constant current of 0.5 A.  
on the electrochemical treatment and the suitability of reusing the recovered chromium in tanning process.  
Conceptualization, A.Z. and S.S.; methodology, E.K. and S.S.; software, E.K.; validation, A.Z., S.S. and E.K.; formal analysis, E.K.; investigation, E.K., A.B., I.M. and A.T.; resources, A.Z. and S.S.; data curation, E.K., A.B., I.M. and A.T.; writing--original draft preparation, A.Z., S.S., E.K., A.B., I.M. and A.T.; writing--review and editing, A.Z., S.S. and E.K.; visualization, E.K.; supervision, A.Z. and S.S.; project administration, A.Z.; funding acquisition, A.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project \"INVALOR: Research Infrastructure for Waste Valorization and Sustainable Management\" (MIS 5002495) which is implemented under the Action \"Reinforcement of the Research and Innovation Infrastructure\", funded by the Operational Program \"Competitiveness, Entrepreneurship and Innovation\" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).  
Not applicable.  
The authors declare no conflict of interest.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 1 Introduction  
### Research Background  
Initially, the use of mobile robots was limited to manufacturing. However, with the development of technology and innovation, mobile robots are now commonly used in various industries such as medical [1], mining, rescue, military, education, agriculture [2] and service industries [3].  
From the perspective of mobile robots, the real point is to move from a designed starting point to another target point and the need to smoothly avoid obstacles in a path-optimal manner [4]. Therefore, the navigation of mobile robots is crucial for mobile robots [5]. [PERSON] and [PERSON] pointed out that path planning is the main problem of mobile robots and an important component of navigation [6]. The result of path planning will intuitively have an impact on how well the robot completes the task in real time and how well the result is achieved.  
From the present point of view, path planning algorithms are favored by many researchers with the rise of mobile robots, UAVs [7, 8, 9], the unmanned field [10, 11], and the video game field [12]. However, the study of path planning for mobile robots, especially the trajectory optimization of generated paths, remains a great challenge. For example, Figure 1 shows a robot and its target location. The solid path is a path consisting of straight lines and sharp turns at points A, B, C, and D. It is also the path generated for the discrete map state due to the fixed pathfinding direction. This path is not ideal for the movement of a mobile robot. First, mobile robots cannot make sudden sharp turns and need to slow down. For example, for driverless cars, sudden turns between path nodes and discontinuities in speed and acceleration are very dangerous behaviors for passengers [13; 14]. For intelligent wheelchair robots, there are interruptions and sharp turns among the paths, which may cause secondary injuries for the patient [15; 16]. Second, fixing the robot's search direction is not suitable for finding the shortest or optimal path, which makes many possible paths in the map ignored by the robot. As shown by the dashed line in Figure 1, a shorter path means higher efficiency and less energy consumption while ensuring the safety of the mobile robot. For example, in unmanned warehousing as well as in the workshop transportation process, the efficiency of the transport robot will directly affect the overall operational efficiency of the warehouse and workshop [17].  
To solve these problems, any-angle path planning [18] and path smoothing techniques [19] need to be introduced. Path smoothing is an important issue in path finding for mobile robots. Smooth paths need to satisfy the constraints of continuity such as path position, robot movement speed, and acceleration. At the same time, in order to ensure that the mobile robot finds the shortest path in the path finding process, the any-angle path planning is needed to no longer restrict the search direction of the mobile robot.
## 3 Data Acquisition and Processing
### UAV Photogrammetry Data Acquisition and Pre-Processing  
The data were collected using a DJI Genie 4 Pro 20-megapixel drone, but due to the high altitude difference and wind direction and speed, a manual flight mode was used and the number of aerials collected was 1204. The data processing was performed by ContextCapture Center software for processing operations such as image element point judging and aerial triangulation to obtain the rock LAS point cloud and 3D model, as shown in Figure 6. However, in the manual flight mode, due to the aerial film camera focusing on the center of the aerial film, which will cause serious distortion of the photos of the high and steep cliff at the bottom, we could not acquire a precise measurement of the steep cliff at the bottom (Figure 7). For this, point cloud fusion was needed.  
Figure 4: 3D laser data acquisition: (**a**) acquisition of data by ground-based 3D laser scanner; (**b**) scanning area field map; (**c**) scanning point cloud alignment map; (**d**) preprocessing result of point cloud in scanning area.  
Figure 5: TLS point cloud local cavity map.  
Figure 6: UAV photogrammetry data acquisition: (**a**) 3D model of UAV; (**b**) 3D LAS point cloud of UAV.  
Figure 7: Distortion of the drone 3D model in the depressed area.
# The Effect of Energy Loss Straggling on SEUs Induced by Low-Energy Protons in 28 nm FDSOI SRAMs
## 2 Methods  
### Experimental Method  
A commercial 160 kb SRAM fabricated with the ST28 FDSOI CMOS technology was irradiated by protons in 2-8 MeV energy range using the EN tandem accelerator in Peking University (PKU). It consisted of classical six-transistors bit-cells, as shown in Figure 1.  
The SRAM under test was initialized with a checkerboard pattern beforehand. During its exposure to protons, a connected field-programmable gate array (FPGA) read data that was stored in all memory cells periodically. The FPGA recorded upsets once upsets were generated and then wrote correct data to the error bits. After that, a new read cycle started. Furthermore, real-time error message could be received by the monitor computer. To guarantee that protons arrived at the surface of devices were monoenergetic, no degrader was used; all tests were conducted in vacuum. Proton energy was regulated by changing the high voltage of the Van de Graaf generator when using the EN tandem accelerator and it had desirable accuracy (error within \(\pm\) 2%). Table 1 gives details about experimental setup in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Proton Energy [MeV]** & **Incident Direction** & **Proton Fluence [ions/cm\({}^{2}\)]** \\ \hline
2 & Vertical & \(6.38\times 10^{11}\) \\ \hline
3 & Vertical & \(3.93\times 10^{11}\) \\ \hline
5 & Vertical & \(2.93\times 10^{11}\) \\ \hline
8 & Vertical & \(3.25\times 10^{12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental Setup.  
Figure 1: A bit-cell in the ST28 FDSOI SRAM with detailed size of transistors.  
To avoid the harmful influence of total ionizing dose (TID) effects on SEU results [11], a SRAM circuit was only tested by one single energy point. Operating voltages and currents of irradiated circuits were carefully monitored because electrical characteristics can effectively reflect the device degradation resulted from TID. Irradiation was terminated as long as significant changes were observed for electrical characteristics. Though TID effects may not have been ruled out thoroughly, it has been minimized in the present experiment.
## 3 Results
### Performance Testing: Proton Resistance/Methanol Diffusivity
Introducing some crosslinking (sPEEK/DCX and sPEEK/DCX-s90\({}^{\circ}\)C) lowers the methanol permeability, but competitive values are not reached. Additionally, sPEEK/DCX-s90\({}^{\circ}\)C has a slightly higher methanol permeability compared to sPEEK/DCX, as sPEEK/DCX-s90\({}^{\circ}\)C has a lower crosslinking degree due to a higher degree of sulfonation. However, sPEEK/DCX-CX24h and sPEEK/DCX-CX190\({}^{\circ}\)C are the two best-performing membranes. They both fall within the rectangular area, meaning that they have an improved proton resistance and methanol rejection compared to the commercial benchmark. The lower methanol permeability can be explained by the high crosslinking degree, which makes the membrane dense and less permeable for methanol. In addition, the presence of DCX creates hydrophobic parts, rejecting methanol. Those effects were also consistent with the lower water and methanol uptake values [34].
## 3 Effect of Damping Layers in ABH Array on Energy Dissipation and Sound Radiation of Plate  
### FE Model of Plate Embedded with ABH Array with Damping Layers  
In this section, two types of damping layers are introduced to investigate the energy dissipation effect of damping materials coated on an ABH plate. Inspired by the symmetrical one-dimensional ABH wedge [3] shown in Figure 13a, the damping layer with a power-law varying thickness is introduced, as shown in Figures 13b and 14b. In addition, damping layers in the same mass, but with uniform thickness, are also investigated, as shown in Figure 14c. The results in Section 2.4 indicate that the energy focalization effect is more significant in the area within a radius of 30 mm. Therefore, the profile of damping layers is designed to be symmetrical with that of the ABH and with a radius of 30 mm. The parameters of the damping layers are shown in Table 3. The FE model of the plate structure with the two-dimensional ABH array and damping layers is illustrated in Figure 14a. The weight of the plate with the ABH array and damping layers is 6.88 kg, while that of the uniform plate with only damping layers is 7.81 kg. That is, the acoustic black holes result in about 11.9% weight saving.  
Figure 14: A schematic diagram of the FE model: **(a)** FE model of plate structure with ABHs and damping layers; **(b)** A two-dimensional ABH indentation with varying thickness damping layers; **(c)** A two-dimensional ABH indentation with uniform thickness damping layers.  
Figure 13: **(a)** A symmetrical one-dimensional ABH structure, **(b)** A two-dimensional ABH indentation with varying thickness damping layers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(\rho_{p}\) [kg/m\({}^{3}\)] & \(E_{p}\) [GPa] & \(\
u\) & **Loss Factor** \\ \hline Value & 950 & 5.0 & 0.3 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the damping layer.
# Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components
## 3 Solution Method
### Application of Quadratic Programming  
As defined in (1)-(5), the operation scheduling problem has both discrete and continuous optimization variables. The former is \(u\), and the latter consists of \(g\), \(s\), \(v\) and \(e\). With a view to improve compatibility between the target problem and its solution method, the authors redefined the optimization variables as  
\[u_{h,t}^{\prime}\in[0,\text{ }1],\text{ for }\forall h,\text{ }\forall t, \tag{18}\]  
\[g_{h,t}^{\prime}\in\Big{[}G_{h}^{min},G_{h}^{max}\Big{]},\text{ for }\forall h,\text{ } \forall t, \tag{19}\]  
where \(h\) is the number of controllable components (\(h=1,\cdots,NH\); \(NH=NG+NS+NV+1\)); \(u_{h,t}^{\prime}\) is the ON/OFF state variable of controllable components (ON: 1, OFF: 0), which is an element of vector \(u_{h}^{\prime}\) and \(u^{\prime}\); \(g_{h,t}^{\prime}\) is the output of controllable components, which is an element of vector \(g_{t}^{\prime}\) and \(g^{\prime}\); and \(G_{h}^{max}\) and \(G_{h}^{min}\) are the maximum and the minimum outputs of controllable components.  
In (18) and (19), all controllable components are aggregated. The \((NG+1)\)-th to the \((NG+NS)\)-th components are the ESSs, the \((NG+NS+1)\)-th to the \((NH-1)\)-th components mean the CLs and the \(NH\)-th components represent the trading of electricity. By (7), the sets of coefficients, \((A_{h},B_{h},\ C_{h})\), for both the ESS and the CL (\(NG<h<NH\)) are \((0,\ 0,\ 0)\), and for the trading electricity (\(h=NH\)) they become \((0,\ M,\ 0)\). \(SC_{h}\) (\(NG<h\)) can be simply set to \(0\) because there are no start-up costs in each component.  
Now, if we fix the states of controllable components on each time, \(u^{\prime}_{h,t}\), the target problem can be regarded as a special type of optimization problem that has a quadratic objective function and several variables subject to linear constraints, called the quadratic optimization problem. In this case, QP solvers can be applied; therefore, there is no need to concern the difficulty in determining continuous variables after creating \(u^{\prime}\). In other words, the dimensions of the solution space are reduced from \((u,\ g,s,v,e)\) to \(u^{\prime}\), and as a result, we can expect to improve the searching ability of the applied intelligent algorithms.