esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The OpenOrbiter mission seeks to implement the designs created by the Open Prototype for Educational NanoSats (OPEN) program. OPEN is producing a set of design documents, construction instructions and a testing plan to facilitate the creation of low-cost CubeSat-class satellites. Reducing the cost of spacecraft development in this form factor allows spacecraft development efforts to be funded from teaching funds, instead of requiring faculty to seek research funds to conduct this activity. Using teaching funds (instead of research funds) facilitates greater student leadership and involvement, as it reduces the risk of mission failure to the responsible faculty member. Additionally, as teaching funds are generally recurring, this approach facilitates incorporation of the small satellite program in curriculum on a year-upon-year basis.  
The OPEN design, which is implemented by OpenOrbiter, also incorporates technical advancements. It places the subsystem circuit boards on the four sides of the spacecraft and allows payload/mission-specific component placement in a 5 cm \(\times\) 5 cm \(\times\) 10 cm area in the middle of the spacecraft (facilitating the placement of a propellant tank at the spacecraft's center of mass and effective use of the overhang space included in the CubeSat specification).  
This paper describes, principally, the OpenOrbiter spacecraft. It briefly presents the solution space provided by the OPEN framework and details the specific choices made for OpenOrbiter. The objectives for the program (both from a university perspective and an OPEN implementation perspective) are discussed. The requirements and constraints that flow from these objectives are presented and the ensuring mission concept of operations, architecture and high-level design are presented.
## 2 Designation of Tooth Profile of Internal Gears with Curved Meshing Lines
### Derivation of Internal Gear Tooth Profiles  
As shown in Figure 3, coordinate systems \(S_{i}\) (\(i=1\), \(2\)) and \(S_{f}\) are established, where \(S_{i}\) (\(i=1\), \(2\)) is a coordinate system fixedly connected to the external gear and internal gear, respectively, and the coordinate origin \(O_{i}\) (\(i=1\), \(2\)) is located at the centers of the two gears. \(S_{f}\) is a fixed coordinate system, with the coordinate origin \(O_{f}\) located at the tangent point of the two gear pitch circles.  
Figure 3: Coordinate system of the internal meshing gears.  
Figure 2: Internal gear mesh line construction.  
According to the principle of gear internal meshing and coordinate transformation, the external gear tooth profile equation \(\overset{\rightharpoonup}{r}_{1}\) and the internal gear tooth profile equation \(\overset{\rightharpoonup}{r}_{2}\) can be expressed by the following equations, respectively:  
\[\overset{\rightharpoonup}{r}_{1}(u_{t})=\begin{bmatrix}x_{f}(u_{t})\cos\phi_{1 }(u_{t})-y_{f}(u_{t})\sin\phi_{1}(u_{t})+r_{1}\sin\phi_{1}(u_{t})\\ x_{f}(u_{t})\sin\phi_{1}(u_{t})+y_{f}(u_{t})\cos\phi_{1}(u_{t})-r_{1}\cos\phi_ {1}(u_{t})\\ 1\end{bmatrix} \tag{4}\]  
\[\overset{\rightharpoonup}{r}_{2}(u_{t})=\begin{bmatrix}x_{f}(u_{t})\cos\phi_ {2}(u_{t})-y_{f}(u_{t})\sin\phi_{2}(u_{t})+r_{2}\sin\phi_{2}(u_{t})\\ x_{f}(u_{t})\sin\phi_{2}(u_{t})+y_{f}(u_{t})\cos\phi_{2}(u_{t})-r_{2}\cos\phi_ {2}(u_{t})\\ 1\end{bmatrix} \tag{5}\]  
where the angle of the external gear \(\phi_{1}\) and the angle of the internal gear \(\phi_{2}\) are determined by the following equations:  
\[\phi_{1}(u_{t})=-\int_{0}^{u_{t}}\frac{x_{f}^{\prime}(u_{t})x_{f}(u_{t})+y_{f }^{\prime}(u_{t})y_{f}(u_{t})}{x_{f}(u_{t})r_{1}}du_{t} \tag{6}\]  
\[\phi_{2}(u_{t})=\frac{\phi_{1}(u_{t})}{m_{12}} \tag{7}\]  
\(N_{1}\) and \(N_{2}\) are the number of teeth of the external gear and the internal gear, respectively, and \(m_{12}\) is the transmission ratio of the internal meshing gear, which can be expressed by the following formula:  
\[m_{12}=\frac{N_{2}}{N_{1}} \tag{8}\]  
By substituting the meshing line expressions (1) and (2) into Equations (4) and (5), the tooth profile equations of the curved meshing line external gear and the internal gear can be obtained.
# Landslide-Tunnel Interactions and Control Countermeasures under an Orthogonal System
## 2 Evaluation of Landslide Effects on Tunnels
### Tunnel and Landslide Interactions  
The above analysis shows that the tunnel and landslide are both sources of disturbance and are mutually coupled. The interaction process of the two is as follows: The landslide gradually deforms and becomes unstable under various factors. The tunnel's structure is directly affected by the landslide's thrust, causing this structure to crack and break. Tunnel excavation relaxes deformations in the surrounding rock, reduces the strength of the surrounding rock mass, and induces landslide deformation. When the stable state of the landslide mass is broken, the landslide causes extrusion deformation in the tunnel's lining structure in the process of sliding down.  
In the current engineering investigation, when the tunnel intersects with the sliding surface, the stress on the tunnel's lining is the most complex of the three scenarios. When the tunnel intersects with the sliding surface, the deformation at the junction of the sliding surface and the tunnel is the most obvious. At the same time, the deformation of the tunnel lining near the mountainside is significantly larger than that of the tunnel lining far from the mountainside. Therefore, it is important to study tunnel deformation characteristics and control countermeasures in orthogonal tunnel-landslide systems. The following content will focus on studying the damage conditions for when a tunnel orthogonally crosses a sliding surface.
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 2 Related Work
### The Soundlapse Project and 3D Audio Research  
The research presented here is part of the activities of the Soundlapse project, which explores the acoustical features of natural soundscapes in southern Chile [24]. Among the project's research lines, there is a focus on multichannel audio reproduction and its applications in diverse contexts such as arts, education, and VR. Previous studies on this topic have emphasized the need for accurate 3D Ambisonics reproduction across various applications. These studies included designing and implementing listening tests to assess the quality of Ambisonics in sound localization and immersion [15; 25].  
The first attempt to deal with this issue was carried out by [PERSON], who investigated a spatial audio design over a binaural system by conducting a sound localization test [25]. An original algorithm was developed using the software Max 8 and _Spat_ libraries [26]. Following this study, [PERSON] assessed the performance of an optimized version of the Ambisonics algorithm by conducting a localization test in different architectural conditions [15]. A key finding of this study was that substantial changes in the acoustic attributes of the rooms where the experiments took place had a minimal impact onparticipants' sound source localization performance. Results also showed the importance of the flexibility of the designed Max tool for research and artistic purposes and the limitations of the system for sound source spatialization, particularly in the vertical plane.
## 5 Experiment
### Experimental Verification and Analysis
#### 5.2.2 Continuous Interruption Test
In contrast, the eastward and northward RMSEs of LSTM-FGO are reduced to 0.45 m and 0.88 m. CNN-GRU-FGO achieves even more remarkable precision improvements, with eastward and northward RMSEs of just 0.16 m and 0.26 m, reducing the position root mean square errors by 99% and 69% compared to FGO and LSTM-FGO, respectively. Overall, in environments with continuous GNSS interruptions, the combination of neural networks with traditional factor graph optimization methods significantly enhances the algorithm's navigation and positioning performance in complex settings.
## 1 Introduction
For testing purposes, the proposed ontology model is materialized by means of an RDF (Resource Description Framework) repository (RDF in W3C [[https://www.w3.org/RDF/](https://www.w3.org/RDF/)]([https://www.w3.org/RDF/](https://www.w3.org/RDF/)) (visited on 24 October 2019)), which is gradually populated throughout mapping procedures of multiple data sources. These data are collected from heterogeneous sensorized data and computed measurements from different geographic zones, in a real-world water management network. The resulting RDF repository can be accessed by high level queries using federated SPARQL sentences. The main contributions of this paper are outlined as follows:  
* A water supply network oriented ontology is proposed, which allows for modelling, generating, integrating, publishing and exploiting a dataset, enabling general users to interact with the data. This ontology has been developed in OWL 2 and considers a large set of concepts, attributes and relationships to contextualize water management supply networks field. * Our approach is tested on real-world data from a water management supply network in the Mediterranean region of Valencia (Valencian Community, Spain). It is a southeastern zone of Spain where autumn storm episodes are quite common, with flooding of urban areas, but with usual annual droughts. Different cities of the Valencian region such as Alicante or Valencia have developed an integral and sustainable water management plan, including flood prevention and supply network deep management among their priorities. Reported results allow us to support domain experts in the decision-making process. * A semantic model has been implemented for materialization of all the involved concepts and measures from the data sources, as well as those processes and components required. The concepts are integrated according to the ontology scheme and integrated in the RDF repository. On top of this, a series of SPARQL queries have been formulated for federated querying. * In this regard, the links to external repositories have been used to enrich the original data in order to facilitate data reuse and interoperability. Thus, in our use case, the links to GeoNames and Wikidata have allowed to add contextual information to original data. The remainder of this article is organized as follows: Section 2 contains background concepts and overview of related literature. In Section 3, the general framework is described with details of the RDF repository and mapping functions. Section 4 presents a real-world use case of the water supply network for validation describing the proposed ontology and discussion extracted from the experimentation. The main conclusions and future lines of research are drawn in Section 5.
## 6 Simulation Result and Analysis
### Experiments Establishment
#### 6.1.3 Comparative Methods  
To evaluate the performance of the proposed DRL-QoS-RA algorithm, this section compares it with the following methods,  
1. Genetic algorithm: In the GA-based [25; 26] online resource allocation method, the normalized weighted objective. \(\alpha_{1}\mathcal{P}_{1}+\alpha_{2}\mathcal{P}_{2}\) is adopted as the optimization objective with the number of parents \(N_{p}\) = 200, probability of variation \(p_{M}\) = 0.005, crossover probability \(p_{C}\) = 0.05, and number of iterations \(N_{l}\) = 800.
2. DRL-EERA: DRL-based Energy-efficient resource allocation method. DRL-EERA improves the method proposed in [3] by taking power control into consideration, as [3] only considers the channel allocation problem. More specifically, DRL_EERA adopts the state representation and instant reward in [3], while adding power control in action space to allocate transmission power and channel simultaneously. The training parameters of DRL are consistent with the DRL-QoS-RA method, where the deep neural network includes four layers, namely two convolution layers and two fully connected layers.
3. DRL-RA: DRL-based resource allocation method. Similar to DRL-EERA, the action design was modified to simultaneously allocate power and channel based on [9]. Moreover, the training parameters and network structure are consistent with DRL-QoS-RA.
4. Random method: Power and channel of the current terminal are randomly allocated.
## 3 Network Background
### Ultra-Dense 5G/6G Cellular Networks
#### 3.3.1 Type of Cell Coverage/Base Stations
They serve as a centralized densification alternative, differing from the distributed densification provided by picocells and femtocells. By expanding macrocell coverage, RRHs enhance network capacity and improve connectivity in areas where deploying traditional base stations is impractical [38]. * Relay  
Relays are operator-installed access points designed to enhance the macrocell edge performance and provide coverage in dead zones [39]. They function by transmitting and receiving user data between macrocells and end-users using wireless backhaul [5]. While relays and picocells share similar coverage areas and transmission power, they differ in three key aspects: (1) relays act as extensions of macrocells, whereas picocells function as independent base stations; (2) picocells are deployed primarily for capacity enhancement, whereas relays are used for coverage expansion; (3) picocells use an ideal fiber or microwave backhaul, whereas relays rely on wireless in-band or out-of-band backhaul.
# Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control
## 6 Experiment  
### Environment  
#### 6.1.1 RL Environment  
In previous work, the definitions of state, action, and reward under the traffic signal control task have varied. Ref. [44] proposed, and demonstrated with reasoning and experiments, that the queue length in the lane is proportional to the delay time with vehicles under the traffic signal control task. Moreover, the number of queued vehicles on the lane, as well as the traffic signal, is sufficient to describe the dynamics of the environment. The experimental results of [21] demonstrate that the performance of the agent under traffic signal control is insensitive to the time interval of signal control. Considering that the longer the time interval, the more drastic the environmental transitions, we chose 10 s in order to reduce the complexity of the traffic inference model to learn the environmental state transitions. Therefore, the reinforcement learning environment we used is defined as follows:  
* State: the number of queued vehicles \(S_{v}\) in each lane and the corresponding traffic signal \(S_{i}\).
* Reward: Negative value of the average number of vehicles in queue on the lane. Specifically, \[R_{t}=-(\sum_{i=1...N_{l}}n_{i})\ /\ N_{l},\] (34) where \(N_{l}\) is the number of lanes and \(n\) is the number of vehicles in the lane.
* Action: Signal phase is selected every 10 s.
# A Review on Upper Limb Rehabilitation Robots
## 3 Rehabilitation Robots and Their Classifications  
The World Health Organization reported that around 15 million people suffer from stroke yearly; around six million of them become disabled. The disabilities vary form fully paralyzed limbs to weakness [2; 24].  
Scientists have found that particular activities of the brain can be transferred to a different location in the brain, and this is known as neuroplasticity [25]. Studies showed that repetitive motions for the impaired limbs allow the brain to develop new neural pathways and, ultimately, restore full or partial control of motor functions [24].  
Impaired functions can be fully or partially restored by investigating neuroplasticity [18]. Using a rehabilitation robot could trigger neuroplasticity by providing a repetitive exercise for the impaired functions. In this respect, rehabilitation robots are defined as an operated machine that is designed to perform specific movements [24].  
The importance of using a rehabilitation robot comes from its ability to provide quantitative and continuous monitoring for patient performances during the training, which allows us to understand the recovery mechanism during the repeated tasks [7].  
Rehabilitation robots are classified either by their treatment approaches or by their structures [8]. Each type is further classified into two types, as follows:
# Towards Incidence Management in 5G Based on Situal Awareness
## 3 Incidence Management in 5G  
The new 5G design principles are intended to support an exponential increase of connected devices and, consequently, the data traffic moving through the network. In contrast to traditional mobile architectures, 5G requests a clear separation between data and control planes, a global vision of the network and a dynamic/customizable control of the mobile network operations. For this purpose, innovative technologies, such as SDN and NFV, have been extended to wireless and mobile platforms. In this way, the operators are not limited by the use of Command Line Interface (CLI) for individual and remote access. Instead, the administrator can create software or \"network applications\" to dynamically control the network behavior. However, the use of autonomous incident management systems that take advantage of these new paradigms is limited. In this context, the main challenge is the coordination between the virtual monitoring elements, allocated in different nodes in the infrastructure, and the response or mitigation procedures through the execution of actions in virtual functions. Similarly, mobile SDN/NFV-enabled architectures are limited by the lack of integrated schemes capable of analyzing large volumes of data, detecting potential risks and diagnosing their causes. Furthermore, the management systems should enable the definition, organization and handling of the different risks, assets and priorities without compromise the security and quality of service.  
Figure 1: Endsley model for Situational Awareness.
# Multidisciplinary Design Optimization of Cooling Turbine Blade: An Integrated Approach with R/ICSM
## 3 Multidisciplinary Design and Optimization of Turbine Stage Blade
### Parameterized Modeling Method of Cooling Turbine Blade  
#### 3.1.1 Blade Shape Parameterization  
The planar cascade design forms the fundamental basis of the turbine blade design. Its objective is to shape the aerodynamics of the blade across various radial cross-sections and provide blade profile data essential for the radial stacking of the blade. Typically, turbine blade cascades comprise four profiles: the leading edge, trailing edge, blade suction surface, and blade pressure surface. While the leading and trailing edge profiles are commonly represented by circular arc curves, the blade suction and pressure surface curves tend to be more complex. To ensure the smoothness of the blade profiles, this study employs a direct analytical approach for shaping the flat blade grid. Figure 3 illustrates the geometric parameters and key control points necessary for the adopted planar cascade modeling method. Based on 11 primary parameters--including the leading edge radius \(R_{q}\), trailing edge radius \(R_{h}\), inlet geometric angles \(\beta_{q}\), outlet geometric angles \(\beta_{h}\), inlet wedge angles \(W_{q}\), outlet wedge angles \(W_{h}\), turning angle \(\delta\), blade stagger angle \(\gamma\), axial chord length \(S\), throat width \(a\), and blade pitch \(t\)--the positions of the five blade control points and their tangent directions can be uniquely determined (as shown in Figure 3). With the positions of control points and tangent directions established, a third-order Bezier curve is employed to delineate the entire blade bowl profile and the frontal section profile at the back of the blade. Additionally, a fourth-order polynomial curve is utilized to depict the rear section profile, ensuring curvature continuity between the two curves at the throat point and mitigating aerodynamic losses arising from curve connections.  
Radial stacking of the blade aims to interpolate blade profiles across various radial sections, utilizing the design output from the planar cascade shape. This interpolation is used to perform radial stacking according to the chosen stacking law, thereby achieving a smoothly contoured blade body. Given that typical aerodynamic parameters of the turbine blade exhibit linear deviation along the blade height, a parabolic stacking law was employed to shape the blade body after obtaining profile data at the root, middle, and tip sections, as illustrated in Figure 4.  
Figure 3: Geometric parameters and crucial control points required for the selected cascade profiling method.
## 4 Results  
### In Situ Stress Measurement  
Triaxial compression tests reveal that the static Young's modulus of the Fengcheng Formation ranges from 31.62 GPa to 56.42 GPa, and the static Poisson's ratio ranges from 0.21 to 0.31. Dynamic geomechanical parameters at the same burial depth were calculated using Equations (1) and (2). These dynamic parameters, derived from logging data, generally exceed their static counterparts obtained through experiments (Figure 8).  
Conducting rock mechanics tests is costly and time consuming, despite the accuracy of the data obtained. Moreover, this method collects scattered points that may not fully represent the overall variation in geomechanical parameters. In contrast, calculating geomechanical parameters via logging is simpler and more efficient. Considering the applicability of static geomechanical parameters and the continuity of dynamic geomechanical parameters, linear regression analysis was conducted in the research area to obtain precise and continuous data (Figure 8). Consequently, trend lines were fitted, from which conversion formulas were derived as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data** & **Utilization** \\ \hline Post-stack seismic data & Extraction of ant tracking from post-stack seismic data for constructing 3D geological models. \\  & P-wave time difference, S-wave time difference, and density were primarily used to calculate dynamic mechanical parameters and in situ stress. \\  & \\ Formation MicroScanner Image (FMI) & To determine the orientation of the in situ stress field. \\  & To calibrate the static mechanical parameters, which can be used to adjust dynamic mechanical parameters. \\  & To determine the maximum and minimum horizontal \\ Acoustic emission & principal stress. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comprehensive data used in the numerical simulation.  
Figure 7: Methodology and workflow of 3D in situ stress modeling.  
\[E_{\rm s} =1.1996\times E_{d}-43.964 \tag{8}\] \[\mu_{\rm s} =0.314\times\mu_{d}+0.1208 \tag{9}\]  
where \(\rm E_{\rm s}\) is the static [PERSON]'s modulus, GPa; \(\rm E_{d}\) is the dynamic [PERSON]'s modulus, GPa; \(\rm\mu_{\rm s}\) is the static [PERSON]'s ratio; \(\rm\mu_{d}\) is the dynamic [PERSON]'s ratio.  
Acoustic emission experiments indicate that \(\rm\sigma_{\rm hmax}\), \(\rm\sigma_{\rm hmin}\) and the horizontal stress difference in the study area are approximately 90 MPa, 80 MPa, and 8 MPa, respectively (Table 3). According to Figure 9, the \(\rm\sigma_{\rm hmax}\) and \(\rm\sigma_{\rm hmin}\) increase gradually with depth. Indeed, there is a weak positive correlation between horizontal principal stress and burial depth. The weak correlation between burial depth and horizontal principal stress does not imply that the test results are incorrect and therefore does not affect the accuracy of the numerical results and conclusions. Since the magnitudes of \(\rm\sigma_{\rm hmax}\) and \(\rm