esa-sceva/satcom-chunk-collection huggingface dataset:
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
## 4 Problem Statement
### Linear Feedback Control Design  
In this study, a linear feedback control, which utilizes monitored voltages as control inputs and produces control actions as control outputs, is employed to develop a secondary voltage controller. Furthermore, it must be stressed that only pilot-bus voltage data are available to control devices. If there are sufficient measurement units at all load buses, voltage changes \(J_{1}\mathbf{q}\) will be the control inputs. In practical applications, however, only partial load buses are pilot buses. That is, the monitored voltage deviations \(\Delta\mathbf{V_{p}}\) can be expressed as  
\[\Delta\mathbf{V_{p}}=J_{p}\mathbf{q} \tag{11}\]  
where \(J_{p}\) is the rows of \(J_{1}\) relating to the selected pilot buses. Indeed, \(\Delta\mathbf{V_{p}}\) are only some entries of the vector \(J_{1}\mathbf{q}\). In this case, linear control vector \(\mathbf{u}\) is given by  
\[\mathbf{u}=K\Delta\mathbf{V_{p}}=K\left(J_{p}\mathbf{q}\right) \tag{12}\]  
where \(K\) denotes the control gain matrix that needs to be determined. Replacing the second term of Equation (9) with the control outputs \(\mathbf{u}\) in Equation (12) results in  
\[\begin{array}{ll}\Delta\mathbf{V_{L}}&=J_{1}\mathbf{q}-J_{2}K\Delta\mathbf{V _{p}}\\ &=J_{1}\mathbf{q}-J_{2}K(J_{p}\mathbf{q})\\ &=\left(J_{1}-J_{2}KJ_{p}\right)\mathbf{q}\end{array} \tag{13}\]
# Detecting Underground Geospatial Features for Protection of Rights in 3D Space: Korean Cases
## 5 Conclusions and Recommendation  
Information on underground spaces encompasses both the physical status information indicating the location or shape of the space and the right status information related to the ownership and use of the space. This study focused on public underpass shopping centers managed and operated by local governments in underground spaces. 3D underground space information was generated and used for each individual object as well as the use of structured public data. The underground spaces were registered based on this information.  
In this study, store information was generated in individual object units. A spatial information-based management system was implemented, and we proposed a method to utilize this system. As the figure and attribute information are together for each store, such information can be effectively used for actual management tasks such as rental contract information management, statistical status identification, and facility and safety management. This information can be updated by continuously accumulating data generated for each store, and combining data collected from various sensors, floating population information, sales information for each store, and various field surveys such as commercial area analysis, disaster, and environmental management.  
Moreover, a database was designed based on the store's graphic information and attribute (rental) information as the data and management system were experimentally obtained for one underpass shopping center. The findings of this study could be useful in the future if the date of establishment of classified land rights and the coordinates of the establishment location are included in the commercial building information database. This also considers the shared land rights for above-ground parcels, protection of personal property rights, prevention of disputes, and the use of rational rights space.  
This study provides the foundation for safeguarding the property rights of the state and citizens by generating 3D underground spatial information for individual objects and registering underground spaces by making the most of existing public data. A legal and institutional basis for development should be established as part of the national policy through further research to ensure a real-world application of the underground space registration plan proposed in the study.  
Conceptualization, S.K.B.; formal analysis, S.K.B., J.O.K.; investigation, S.K.B.; validation, J.O.K., writing, review, and editing, S.K.B., J.O.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Agency for Infrastructure Technology Advancement (KIAI) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 DCRU-B158151-01).  
Not applicable.  
The authors declare no conflict of interest.
## 4 SkyDRL for Multi-Cloud Service Composition Problem
### SkyDRL
Before starting the training, the user's needs are decomposed into several subtasks, and a workflow is constructed accordingly. Next, a candidate service set is formed by retrieving all cloud services that meet the requirements of the workflow from the cloud service pool. When retrieving services, we need to consider services in multi-cloud environments in order to provide more comprehensive and high-quality choices. This step will establish a candidate set of cloud services for each subtask. The cloud services in the set may come from multiple clouds. To expedite deep reinforcement learning training and reduce its optimization difficulty and computational cost, we use a branch and bound skyline algorithm to pre-query the candidate service set. This helps narrow down the search space for the problem, enabling the model to focus on high-quality cloud services. During training, the state is passed into the neural network to calculate the Q values of all executable actions (i.e., Q values of all candidate cloud services). Similarly, these candidate cloud services may come from multiple different clouds. The \(\epsilon\)-greedy strategy is used to select an action based on the calculated Q values. This strategy selects the action with maximum Q value with probability \(\epsilon\) and selects a random action with probability 1-\(\epsilon\). This creates a balance between the exploration and utilization rates and prevents the model from getting stuck in local optima. The transition information shown in Figure 6 is stored in the replay buffer, and since the participating cloud services may not come from the same cloud and may come from different clouds, the stored sample information will include the information of the clouds to which the cloud services belong. This process is repeated until the buffer is full. Once the replay buffer is full, the neural network parameters are updated. A batch of samples is extracted from the buffer via the Prioritized Experience Replay, and the loss function mentioned earlier is used to update the network parameters. During updates, the Q-target parameters remain unchanged, and only in every \(C\) steps do the Q-network's parameters get assigned to Q-target. Finally, the priority of the samples needs to be recalculated after updating the neural network parameters. By carrying out the aforementioned steps iteratively, i.e., passing the state into the neural network, selecting an action based on the policy, storing samples in the replay buffer, calculating the loss function and updating the network parameters, and updating the priority tree, the neural network's parameters are updated continuously. Eventually, the system achieves convergence, and the optimal cloud service composition solution is determined. Algorithm 2 provides a detailed implementation of the SkyDRL method.
## 2 Numerical Model
### Time-Domain Numerical Simulation
The axial stiffness of the equivalent net is adjusted by modified Young's Modulus, as described in [PERSON] [26]. Time histories of random waves are generated by superposing 100 regular-wave components with randomly perturbed frequency interval. The following JONSWAP wave spectrum, which is given in Equation (4), is used to generate long-crested random waves. \[ \begin{split} S_{f}(\omega)=(1-0.287\ln(\gamma))\frac{5}{16}H_{ \text{s}}^{2}\omega_{p}^{4}\omega^{-5}\exp\left(-\frac{5}{4}\Big{(}\frac{ \omega}{\omega_{p}}\Big{)}^{-4}\right)\gamma^{\exp\left(-0.5\left(\frac{\omega -\omega_{p}}{\omega_{p}}\right)^{2}\right)\\ \sigma=0.07\quad\omega\leq\omega_{p}\\ \sigma=0.09\quad\omega>\omega_{p}\end{split}  \tag{4}\]  
where \(\omega\) is wave angular frequency (rad/sec), \(\omega_{p}\) (\(=2\pi/T_{p}\)) is peak angular frequency (rad/sec), \(T_{p}\) is peak period (sec), \(H_{\text{s}}\) is significant wave height (m), and \(\gamma\) is the enhancement parameter. Wheeler stretching method is further applied to Airy wave theory to represent wave kinematics more realistically above mean waver level [27; 28]. Current profile is generated by using the power-law method, which is defined as follows:  
\[V_{c}(z),=V_{cb}+\left[\left(V_{cf}-V_{cb}\right)\frac{z-z_{b}}{z_{f}-z_{b}} \right]^{1/po}, \tag{5}\]  
where \(V_{cf}\) and \(V_{cb}\) are current velocities at free surface and seafloor, respectively. \(z_{f}\) and \(z_{b}\) denote vertical coordinates of still water level and seafloor, respectively. \(po\) is the power law exponent. In this paper, waves and currents are linearly superposed, and the nonlinear interactions between them are not considered.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 3 System Model
### Mobility Model
As we observe from Figure 1, due to the users' mobility, the service coverage is limited; however, in our method, by defining the concept of sojourn time \(T_{s,t}\) and finish time \(FT_{k}\) of the tasks, users only choose a destination if the period of the coverage availability (\(T_{s,t}\)) is longer than the required time (\(FT_{k}\)) for the task execution. The validations in [63] indicate that PECNet is highly accurate. We assume that the PECNet predictions are correct, similar to related studies that assume perfectly correct mobility predictions, e.g., [48; 53]. Various mechanisms for mitigating any rarely occurring prediction errors can be examined in future research. One strategy for reducing the chance of prediction errors could be to impose \"safety margins\" on the predicted sojourn times. Another strategy could be to classify the tasks according to their level of criticality and to classify the helpers according to their reliability; then, tasks could be assigned under consideration of the task criticality and helper reliability to minimize potential offloading failures.
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques  
### Approaches  
SAR-based inundation mapping methods are usually more complex than those based on optical sensors because of the processes added to mitigate the error propagated from one or more of the error sources mentioned above. Consequently, one study may consist of several different approaches introduced in the following.  
Figure 2: (**a**) Vertical and (**b**) horizontal swipe demonstration of the geometric error (circulated tracks) between two Sentinel-1 SAR images georeferenced by Range-Doppler algorithm in Sentinel Application Platform (SNAP) 6.0 software using the Shuttle Radar Topography Mission (STRM) 3′′ DEM data. The background and foreground images were obtained, respectively, over George Bush International Airport, Texas.
# Stress and Fatigue Analysis of Picking Device Gears for a 2.6 kW Automatic Pepper Transplanter
## 2 Materials and Methods  
### Structure of the Automatic Pepper Transplanter under Development  
The overall structure of the automated pepper transplantation device under development, which includes a picking mechanism, is shown in Figure 1. The pepper transplanter consists of three main devices: a picking device (a), a conveying device (b), and a dibbling device (c). A two-row pepper seedling dibbling device is attached to the conveying unit to plant the seedlings. The picking device picks five seedlings simultaneously from the tray and transfers them to the conveying unit. The conveying unit drops seedlings into the two-row, hopper-type dibbling devices by rotating the sprocket and chain. A 2.6 kW engine (SUBARU Industrial Products Co. Ltd., Ota, Japan) was used as the prime mover of the transplanter. The picking mechanism uses two gear sets to move the pepper seedlings from the tray to the conveying unit. The two sets of spur gear trains are attached to the gear shaft with a 1:1 gear ratio for crank and 2:1 gear ratio for cam. The input power is provided from the engine, and the load is distributed on the gears. Gearing is the most efficient method for transmitting mechanical power from one shaft to another, with or without changing the speed [10; 12]. The gear load was assumed to be distributed along the contact line of the contact gears. The gear contact point position changes the load distribution level. For the gear mechanism, a stress and fatigue analysis of the gears can be used to predict stress condition and service life, in terms of the maximum load distribution and fluctuations of a certain number of loads.
# A Survey on Mobility Support in Wireless Body Area Networks
## 2 Mobility Support in WBANs  
There may be several ways to categorize the existing schemes for mobility support in WBANs. Among the possible ways of grouping, we focus on two principles: what is the research objective and how many nodes move in the same group. Currently, there are two major research topics to support mobility in WBAN. One is analytic (mathematical) modeling of mobility patterns and the other is networking technology to support mobility. The former is to extract relationships between moving nodes and then make them regular patterns. On the other hand, the latter is to define communication protocol and network architecture to handle topology change caused by human behavior.  
The other applicable rule to grouping is the number of moving nodes with the same pattern. By applying this rule, the mobility model can be categorized as individual and group. Individual mobility model represents a person's moving pattern such as walking, standing, lying and sitting. On the other hand, the group mobility model means the pattern that at least two sensor nodes belong to a group where every node follows a logical center (group leader) that determines the group's motion behavior. Furthermore, as one more step for categorization, each mobility pattern can be divided into general and specific purposes according to movement place. While a general purpose model does not target any specific application, specific ones aim to model group pattern depending on applications in places such as hospitals.  
Based on analysis for categorization, here we illustrate the result of possible categorization in both Figures 1 and 2, while research objectives are the primary rule for categorization in Figure 1 and the mobility model in Figure 2. In this paper, we describe each scheme by giving higher priority to human behavior according to mobility model than research objective since clear classification and comparison between schemes are available. In this categorization, mobility model and network technology are explained, respectively.  
Figure 1: Categorization of mobility support for research objectives in wireless body area networks.
## 3 Active Guidance Controller Design for IRWs Based on MARL
### MARL-Based Controller Design
#### 3.4.1 Local and Global Observation Space  
Each IRW observes only its local state without considering the dynamic states of the entire vehicle or other IRWs, which reduces the observation dimensionality of each agent, achieving local controllers and reducing communication costs. The observation state vector, based on the active guidance control objectives and vehicle dynamics model shown in Equations (1)-(7), is defined in Equation (14).  
\[O_{i}=\left[y_{wi}\quad\psi_{wi}\quad\dot{y}_{wi}\quad\dot{\psi}_{wi}\quad \omega_{Li}\quad\omega_{Ri}\quad\ddot{\beta}_{wi}\right] \tag{14}\]  
where the subscript \(i=1\) denotes the dynamic variables of the front IRW and \(i=2\) denotes those of the rear IRW. \(\omega_{Li}\) and \(\omega_{Ri}\) represent the angular velocities of the left and right wheels of each IRW, respectively.  
When using the PER-MADDPG algorithm, the local observation for each agent includes the lateral displacement and attack angle of the wheelset with respect to the centerline of the track, which are the primary control objectives. Since the rotational speeds of each IRW can derive both the speed difference between the wheels and the longitudinal running speed, they are also considered observation states. Additionally, the time derivative of the difference in speed between wheels is part of the state vector. Therefore, in the centralized training process, the global state is \(S=[O_{1}\ O_{2}]\).
## 6 Security Solutions
### Service Security
#### 6.3.4 Capability-Based Access Control
The Delegation Server rejects a connection request from an authenticated but unauthorized user. The Delegation Server does not even notify the IoT device about the unauthorized request. Thus, the proposed framework eliminates request processing and resource allocation overheads from an IoT device. However, the proposed framework only deals with the connection setup authorization and does not address the application layer authorization issues, such as a user's right to access to certain services. **Context-Aware CapBAC:** A context-aware CapBAC for a federated IoT network is presented in [142]. The authorization framework provides a unified authorization framework for the IoT system, which forms a network federation by integrating multiple IoT domains, such as a smart home, car, and medical service. In the proposed system, a Federation Manager (IoT-FM) enables authorization delegation in a federated IoT network as well as ensures authentication and authorization for each domain included in the federation. The IoT-FM authorizes a delegation request from a delegator, such as a service consumer in domain A, and grants it to a delegatee, such as a consumer in domain B, who acts on behalf of the consumer in A domain. Although the proposed model can be useful for federated IoT systems, its usability cannot be validated as the details on the implementation and evaluation of the authorization model are not provided. **OAuth Compliant CapBAC:** [PERSON] et al. [143] utilized the OAuth technology [169] to propose an authorization service (IoT-OAS) for the IoT. In IoT-OAS, IoT devices do not implement authorization logics; instead, the authorization functionalities are delegated to an OAuth-based cloud service. Service devices receive an access request from the users or client devices and invoke the authorization service to make decision on the request. [PERSON] et al. [144] proposed a delegation-based authorization framework (DCAF) for the IoT. The architecture and operation model of the DCAF is very similar to the IoT-OAS. The DCAF architecture introduces external authorization servers to reduce the overhead for storing authorization logics and credentials on the device storage. Smart devices do not have to store a large amount of information because information is stored in the servers. The DCAF also reduces the overhead for making authorization decisions by delegating the authorization tasks to an external entity. The DCAF is lightweight compared to the IoT-OAS as it adopts a delegation-based architecture to make authorization decisions.
## 3 JET Design
### Electrical System Design  
Figure 4 shows the overall electrical system of JET. A mini-ITX PC is used as the computing system, with the computer board being placed at the front of the robot's chest. The computer CPU is an Intel Core i7-4790 Processor (3.6 GHz) to provide sufficient computation performance for robot control. The computer is equipped with a SENSORAY 826 DAQ board for acquiring analog sensor data and a DIAMOND DS-MPE-OPT4485 4-Port RS-485 MiniCard Module for actuator communication. Using a PCIe-based communication interface, JET can be controlled with a 200 Hz command rate. In contrast, the THORMANG command rate is 100 Hz [11].  
To supply power to the robot, we designed a power system including regulators and a battery. To connect the battery, electronics, and regulators, wire connectors were unified with the same actuator connectors. Parts unification is a well-known method of reducing both manufacturing cost and maintenance. In the JET system, one LiPo (22,000 mAh, 22.2 V) battery supplies power to all actuators and voltage transformer modules. A voltage transformer module is composed of three DC-DC converters. To match the computer ground level with the actuators, two 12 V converters are nonisolated. However, the converter for the FT sensor interface is isolated to reduce the FT sensor power source noise. The sensor ground level is matched with the main ground via the DAQ board. The LiPo battery has excellent discharge performance and small voltage fluctuation, even under high load. On standby, JET consumes 5 A (0.2 C). The peak current while walking is 9 A (0.4 C); this is a small current compared to the LiPo battery discharge rate. Thus, the battery voltage fluctuations due to the discharge rate are sufficiently small to integrate the actuator power source with the computer source. With a fully charged LiPo battery, JET can stand for 4 h.  
The sensory system required for humanoids includes an environment perception sensor and a measurement sensor of the robot state. Three sensor types are used for the perception system: one LiDAR, one IMU, and four FT sensors. A HOKUYO UTM-30 LX-EW sensor, which is a 2D LiDAR, is mounted at the head to acquire 3D point cloud data of the robot surroundings; this is achieved by continuously rotating the sensor about its vertical axis. To facilitate continuous LiDAR rotation, a slip ring is used to connect the electric wires. A MicroStrain 3 DM-GX4-25 IMU is placed at the robot coordinate origin in the pelvis. Finally, ATI Mini 58 and Mini 45 FT sensors are mounted on both feet and hands to measure the forces acting on the feet and wrists. Further system integration and software development details are given in our previous paper [23]. Using a robot operating system (ROS)-based system framework, JET can be controlled stably with many sensors and actuators.  
Figure 4: Electrical system diagram of JET.