esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Communication Technologies
### ZigBee and 868 MHz Radio Modules Using Groups  
The first of the proposed architectures for the monitoring system uses XBee ZB PRO S2 and 868 modules. Figure 7 shows an overview of this architecture. The nodes deployed in the railway are divided into small monitoring groups. The size of these groups is chosen based on factors such as distance between monitored slab tracks, radio antenna range, interferences. Each node of the monitoring group uses ZigBee to send the information to a single coordinator who manages the group. The coordinator stores this information temporarily until a train passes. As a train is detected, the 868 MHz module transmits the information to the train. ZigBee uses the 2.4 GHz band so that transmissions are faster and more reliable but at the expense of a lower range. On the other hand, the 868 MHz communication has a larger range and does not require an association protocol between nodes, which suits the communication requirements between the train and the sensor nodes.  
Note that each node with a ZigBee radio in the monitoring group, except the coordinator, is configured as \"end device\", as defined in the ZigBee standard, which allows them to sleep to preserve battery, but does not permit multi-hop routing. This is the reason why we have to communicate directly with the leader of the group and cannot use multi-hop routing (due to restrictions in those devices an end-device cannot route information). The group leader or coordinator must therefore be configured as ZigBee coordinator as a necessity. Each coordinator, and thus each group, has a different PANID (Personal Area Network Identifier). This way, the communication between nodes in different groups is not allowed and, what is more important, there is no interference between adjacent monitoring groups because they operate in different radio channels. The coordinator nodes are susceptible to interference between each other when they send information to the train and use the same channel.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **ZigBee and 868 MHz** & **868 MHz Radio** & **868 MHz Radio** & **RFID** \\  & **Radios Using Groups** & **Using Groups** & **(Independent)** & \\ \hline
**Group organization** & Yes & Yes & No & No \\ \hline
**Direct communication** & No (only by means of & & Yes & Yes (operator need to be \\
**(Installation mode)** & coordinator) & Yes & close to the RFID tag) \\ \hline
**Communication** & & & & \\
**interference node-train** & Medium & Medium & High & No \\ \hline
**Price** & Low & Medium & Medium & Medium (RFID readers \\  & & & & are expensive) \\ \hline
**Hardware** & Digi Xbee-PRO ZB & Digi Xbee & Digi Xbee & Not tested \\  & Digi Xbee 868 MHz (S5) & 868 MHz (S5) & 868 MHz(S5) & Not tested \\ \hline
**Power consumption** & Medium & Medium-High & High & Low \\ \hline
**Number of radio** & & & & \\
**modules in coordinator** & & & & \\ \hline
**Interference among** & No (different channels) & Low & & \\
**nodes (different groups)** & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Proposed communication architectures.
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 7 Conclusions and Future Work
### Client/Server Mode  
To integrate external scheduling algorithms made in other languages, we made new modifications to the Simulator. Firstly, we added a server mode that remotely helps control the simulation parameters and allows parallel simulation environments using a threaded model. After that, we created a RemoteScheduler class extending the base scheduler class. This RemoteScheduler does not do anything on its own and always waits for the instruction from an external entity. The flow begins with the server listening for connections, and when the client connects to the server, the server creates an entire Thread to attend the remote simulation. Next, the client sends the initial configuration using a defined communication protocol. Afterward, the server responds with an ACK response and begins the simulation until a scheduling decision needs to be made. When this scheduling decision arrives, the Simulator passes the event to the RemoteScheduler class. It sends the current Simulation state, the current Job to assign, and the list of connected devices and waits for the client's response. The client then sends the ID of the assigned device to perform the Job and receives an ACK signal. This process is repeated until the simulation is finished or until the client sends a reset command. When the simulation ends, the server sends the client the last Simulation state and waits for a restart or finish command from the client. We show in Figure A2 the protocol and the logic followed by the Simulator and the client.
# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 2 Related Work
### Instance Segmentation  
Instance segmentation is the combination of two tasks, semantic segmentation and object detection, and requires the classification of pixels and location of different instances. It includes two types of methods, two-stages methods and on-stage methods [29].  
The two-stages methods can be further divided into two categories, top-down methods based on detection and bottom-up methods based on semantic segmentation. Top-down methods first locate the bounding box of an instance using object detection, and then perform semantic segmentation for each detection box. One such method, Mask RCNN [30], extends Faster-RCNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding-box recognition. Bottom-up methods first perform semantic segmentation at the pixel level, and then distinguish different instances using clustering, metric learning, or other methods. In [31], masks for all objects are obtained through semantic segmentation and training is performed using a discriminative loss function, which makes it easy to cluster the image into instances.  
The one-stage instance-segmentation methods include anchor-based methods inspired by YOLO [32] and RetinaNet [33], and anchor-free methods inspired by FCOS [34]. In anchor-based methods, the main idea is to classify and regress candidate target regions called anchors, produced by sliding windows. YOLACT [35] generates a dictionary of prototype masks and predicts per-instance linear combination coefficients. YOLACT++ [36] optimizes the prediction head and adds a novel, fast mask re-scoring branch. SOLO [29] reformulates the instance segmentation as two sub-tasks: category prediction and instance-mask generation problems. SOLOv2 [23] uses the matrix non-maximum suppression (NMS) technique and object mask generation is decoupled into a mask kernel prediction and mask feature learning. The main idea behind anchor-free methods is to transform them into keypoint-based methods by locating the keypoint, or into region-based methods by locating the center of an object and predicting the contours of the object. PolarMask [37] predicts the contour of an instance through instance-center classification and dense distance regression in a polar coordinate.  
As one-stage methods usually have a quicker inference speed than two-stage methods, they are more suitable for real-time scenarios involving guiding the autonomous flight of UAVs. While the segmentation accuracy of one-stage methods for small targets may be less satisfactory, the railways in overhead-view images captured by UAVs are not considered small objects. Therefore, in this paper, we propose using SOLOv2 as the base architecture for the instance-segmentation branch, considering both accuracy and speed.
## 4 Performance Evaluation and Comparison
### Evaluation Metrics
#### 4.1.4 F1-Score  
The F1-score, serving as the harmonic mean of precision and recall, presents a balanced evaluation of the two metrics. It plays a vital role, especially when there is an imbalance between the number of positive and negative instances in the dataset. The F1-score is mathematically computed through the following formula:  
\[\text{F1}-Score=2*(Precision*Recall)/(Precision+Recall)\]
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 4 Abstract Conceptual Structure of Organizational Models
### Dialogical Dimension
#### 4.3.1 Hypergraphs of Scenes  
On a macro level, both in ISLANDER and in OPERA, the direct interactions by message exchanging are partitioned into _scenes_. Scenes are structured and coordinated by means of directed _hypergraphs_ (directed graphs where some edges, called _hyperedges_, can connect any number of sources and target nodes)in which:  
* Nodes correspond to the definitions of _scenes_;
* hiperedges represent partial ordering and/or synchronization relationships from many source scenes to many target scenes, giving rise to the concept of _scene transitions_. In a well formed hypergraph of scenes, there is:
* An _initial scene_, i.e., the node from which agents playing roles have access to the other scenes of the hypergraph. Starting in the initial scene and following the transitions, all the scenes that make up a hypergraph should be achievable;
* a _final scene_, i.e., the node in which the dialogic participation of agents within an organization ends successfully. As the dual of the initial scene, the final scene has to be achievable from any scene in a hypergraph, otherwise the hypergraph of scenes is not well formed.  
In ISLANDER, the hypergraphs of scenes are named \"performance structures\", and in OPERA they are called \"interaction structures\".  
On a micro level, the interactions within each scene are governed by one or more predefined _dialogue scripts_. These scripts correspond to the concepts of _scene protocol_ in ISLANDER and _interaction pattern_ in OPERA. Dialogue scripts are not detailed in Figure 4. The reason is that the intra-scene (micro level) dialogical specifications have distinct natures both in ISLANDER and in OPERA, as will be discussed in the sequel.  
In Figure 4, the conceptual pattern identified in the dialogical specifications of ISLANDER and OPERA is captured by means of an Ecore metamodel. This metamodel is described in details in Tables 5 and 6.
## 2 Related Work
### Tracking  
Positional or motion tracking is the ability of a device to estimate its position in relation to the environment around it or the world coordinate system. As described by [PERSON] et al. (2018), there are two fundamental solutions for positional tracking, marker- and non-marker-based methods. Marker-based methods require the use of standard tags such as ARTags and QR codes for the coupling between an agent's camera and the tracked element. In our application both approaches are available since each one has certain pros and cons: Our current non-marker-based approach does not require the use of tags, however, it requires a two-step calibration process. Marker-based approaches are used in time-critical tasks or in hostile environments where there is no sufficient time for calibration, however, they can only be performed with drones that are already equipped with the compatible tags. Regardless of the approach used, the tracking process is prone to the accumulation of errors and latency, and is in general computationally intensive. Several methods attempt to overcome these challenges. [PERSON] et al. (2019) introduced an indoor tracking system with a rotary-laser base station and photo-diode sensors on tracking objects. The base station scans the space and detects the UAV sufficiently. The error is almost negligible as the estimation in 5 m is only 13 cm. [PERSON] et al. (2019) proposed a position estimation method using a low-cost GPS and optical flows provided by UAV's camera. This approach is available only outdoors since the system uses GPS measurements. In a hybrid case, [PERSON] and [PERSON] (2019) adapted optical flow and ultrasonic sensors on the drone and achieved better results than GPS positioning. A similar approach was implemented for real-time pose estimation by [PERSON] et al. (2019), combining the Cubature Kalman Filter on IMU readings with an optical-flow-based correction to minimize positioning error.
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
### The Node Disambiguation Problem  
We address the problem of NDA in the most basic setting: given a network, unweighted, unlabeled, and undirected, the task considered is to identify nodes that correspond to multiple distinct real-life entities. We formulate this as an inverse problem, where we use the given _ambiguous network_ (which contains ambiguous nodes) in order to retrieve the _unambiguous network_ (in which all nodes are unambiguous). Clearly, this inverse problem is ill-posed, making it impossible to solve without additional information (which we do not want to assume) or an inductive bias.  
The key insight in this paper is that such an inductive bias can be provided by the network embedding (NE) literature. This literature has produced embedding-based models that are capable of accurately modeling the connectivity of _real-life_ networks down to the node-level, while being unable to accurately model _random_ networks [4; 5]. Inspired by this research, we propose to use as an inductive bias the fact that the unambiguous network must be easy to model using a NE. Thus, we introduce FONDUE-NDA, a method that identifies nodes as ambiguous if, after splitting, they maximally improve the quality of the resulting NE.  
**Example 1**.: _Figure 1a illustrates the idea of FONDUE for NDA applied on a single node. In this example, node \(i\) with embedding \(\mathbf{x}_{i}\) corresponds to two real-life entities that belong to two separate communities, visualized by either full or dashed lines, to highlight the distinction. Because node \(i\) is connected to two different communities, most NE methods would locate its embedding \(\mathbf{x}_{i}\) between the embeddings of the nodes from both communities. Figure 1b shows a split of node \(i\) into nodes \(i^{\prime}\) and \(i^{\prime\prime}\), each with connections only to one of both communities. The resulting network is easy to embed by most NE methods, with embeddings \(\mathbf{x}_{i^{\prime}}\) and \(\mathbf{x}_{i^{\prime\prime}}\) close to their own respective communities. In contrast, Figure 1c shows a split where the two resulting nodes are harder to embed. Most NE methods would embed them between both communities, but substantial tension would remain, resulting in a worse value of the NE objective function.
# Using A Rotary Spring-Driven Gripper to Manipulate Objects of Diverse Sizes and Shapes
## 1 Introduction
The integration of machine learning techniques in vision-based grasping has opened new avenues for data-driven approaches, particularly in the realm of tactile sensing [12]. Also, tactile sensing stands to benefit greatly from machine learning, as demonstrated by designs such as the Utah/MIT multi-fingered robotic hand and other innovative solutions [13; 14; 15; 16]. Furthermore, the design and fabrication of soft robotic grippers have garnered significant attention from researchers in recent years, owing to their lightweight nature, cost effectiveness, ease of fabrication, and control [17]. These grippers are actuated using pneumatic pressure, allowing for the flexible adjustment of pressure levels to accommodate different grasping tasks [17]. While most of the research is heavily focused on soft gripping materials and their structures [18; 19; 20; 21; 22], researchers have also proposed innovative mechanical gripper designs tailored for specific applications, such as capturing noncooperative satellites' aluminum honeycomb panels, drawing inspiration from ancient pole-/tree-climbing techniques, and so on [23; 24; 25; 26]. In this research, a novel rotary pin array gripper [27] was designed and studied. This mechanism acts as a universal gripper with the ability to safely grasp a wide range of objects. The overall flow of this paper begins with detailed information about the parts and mechanism of the design, followed by the analytical studies conducted to improve the design, and finally, the grasping test and validation.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 6 Mechanical Model
### Fully Restrained Beam without Axial Force  
The natural frequencies of a fully restrained beam without axial force can be calculated according to [24; 25]:  
\[\cos(\alpha_{n}L)\,\cosh(\alpha_{n}L)=1 \tag{10}\]  
The first five solutions are: \(\alpha_{1}L=4.730\), \(\alpha_{2}L=7.853\), \(\alpha_{3}L=10.996\), \(\alpha_{4}L=14.137,\alpha_{5}L=17.279\), where \(\alpha=\sqrt[4]{\frac{m\omega^{2}}{EI}}\), and \(\omega\) is the circular frequency.
# Dynamic Response of Slender Vertical Structures Subjected to Thunderstorm Outflows
## 4 Comparison between Thunderstorm and Synoptic Dynamic Response
### Velocity Profile Ratio \(R_{a}\)  
The calculation of parameter \(a_{1}\) (Equation (17)) is pursued based on the vertical profiles of the mean wind speed, as outlined in Appendix A (Equation (A2)), and by defining the drag coefficients of the two structures according to [40]. Analyses are carried out for different values of the roughness length \(z_{0}=0.01,\ 0.05,\ 0.10,\ 0.30,\ 0.70\) m. It is worth pointing out that, with the aim of inspecting different varying wind profiles \(z_{0}\), the wind velocities in Table 2 should be scaled, in principle, according to the fixed roughness length. However, since there is no established relation between \(\overline{v}_{\max}\) and \(z_{0}\) for thunderstorms, in order to secure consistency between the thunderstorm and synoptic wind velocities recorded by the same anemometer, this scaling is disregarded. Hence, the effects of the roughness length are only accounted for in the definition of the shape of the vertical profile according to Equation (A2), while for the thunderstorm outflow, different shapes are investigated by varying the nose tip through parameter \(z_{m}\) according to Equation (A1). The drag coefficients due to the synoptic wind for the telecommunications tower are reported in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(h\) [m] & \(\overset{\boldsymbol{\kappa}}{\boldsymbol{v_{\max}}}(h)\) [m/s] & \(\overset{\boldsymbol{\kappa}}{\boldsymbol{v_{S}}}(h)\) [m/s] & \(\boldsymbol{G_{v,T}}\) & \(\boldsymbol{G_{v,S}}\) & \(\overline{v}_{\max}(h)\) [m/s] & \(\overline{v}_{S}(h)\) [m/s] & \(\boldsymbol{R_{v}}\) \\ \hlineFigure 10 plots the velocity profile ratio on varying \(z_{m}\) and \(z_{0}\), for the lighting pole (Figure 10a) and the telecommunications tower (Figure 10b), respectively.  
Figure 10a shows that for the lighting pole, \(R_{a}\) is always higher than unity, except for the low roughness length (\(z_{0}=0.01\) m) and high nose tip (\(z_{m}=75\) m and \(z_{m}=100\) m), showing an increasing trend with \(z_{0}\) while decreasing with \(z_{m}\). Conversely, for the telecommunications tower (Figure 10b), the trends are opposite. Indeed, it can be noticed that \(R_{a}\) is always lower than unity, and it reduces upon increasing \(z_{0}\), while it increases upon increasing \(z_{m}\). The different behaviors observed for the two structures with \(z_{m}\) are due to the different positions of the tip of the nose with respect to the reference height, and therefore the different shapes of the vertical mean wind profiles. It can be argued that the thunderstorm provides a greater wind loading for the pole and a smaller one for the tower compared with the wind loading provided by the synoptic case.  
Overall, the results are in accordance with the wind velocity profiles reported in Figure 5, and emphasize the greater sensitivity of low-rise structures (such as the lighting pole) to thunderstorm outflows.
# The Effects of Functional Ankle Taping on Postural Stability in Elite Judo Players
## 4 Discussion
In biomechanical research, non-linear postural analysis has been used to supplement linear analysis to assess postural stability and flexibility. This study used COP-SWAY/COPVEL as well as ApEn, both of which are objective measures of body posture stability. Basically, COP movement quantifies how an erect posture is maintained, utilizing measures for postural stability between the surface and the body by assessing postural sway and velocity. Ultimately, COP is considered a definitive value that provides information on how ankle's sensory receptors directly affect postural sway and stability. Traditionally, smaller COP fluctuation was considered to reflect stability and COP fluctuation being affected by aging or physical environments [27]. However, using only one quantifiable value to assess the overall bodily stability may not be sufficient because controlling bodily movements is a result of dynamic interactions between musculoskeletal and neurological systems [28]. Thus, it should be noted that the effectiveness of a functional ankle taping intervention to enhance body posture system derived from a simple quantitative analytic method is limited. Despite the limitation, this study has several strengths. There are scarce biomechanical literature that have examined the effects of ankle taping on postural stability in elite Judo players. This study is the first study that utilized both linear and nonlinear measures to explore how ankle taping affects Judo player's postural control. Through linear analysis, we quantified how ankle taping affects stability during a static posture; through nonlinear analysis, we sought to understand how ankle taping affects flexibility (that influences postural balance) by quantifying postural complexity. Most importantly, this study suggests that ankle taping purported to increase ankle stability may ironically negatively affect posture maintenance. Of course, this study does not contradict previous research suggesting that ankle taping reduces the possibility of ankle injury during dynamic movements through strengthening fibula and increasing proprioceptive sensibility [3].
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
###### Abstract  
In today's industry, H\({}_{2}\) is mostly produced from fossil fuels such as natural gas (NG), oil, and coal through various processes. However, all these processes produce both carbon dioxide (CO\({}_{2}\)) as well as H\({}_{2}\), making them questionable in terms of climate change mitigation efforts. In addition to efforts to increase the conversion efficiency of green H\({}_{2}\) technologies, work is also underway to make H\({}_{2}\) production from fossil fuels more environmentally friendly by reducing/avoiding CO\({}_{2}\) emissions. In this framework, these technologies are combined with geologic carbon storage. In a further step, the use of depleted hydrocarbon reservoirs for in situ H\({}_{2}\) production is being investigated, with the co-generated CO\({}_{2}\) remaining permanently in the reservoir. The objective of this paper is to provide a brief overview of the technologies that can be used to produce H\({}_{2}\) from depleted and depleting hydrocarbon reservoirs (DHRs) in various ways. We evaluate the required processes from a reservoir engineering perspective, highlighting their potential for H\({}_{2}\) generation and their technology readiness level (TRL) for applications. We also investigate the possibility of permanently storing the co-produced CO\({}_{2}\) in the reservoir as a means of mitigating emissions. In addition, we provide a preliminary cost analysis to compare these methods with conventional hydrogen production techniques, as well as an assessment of operational risks and associated cost estimates.  
renewable hydrogen; depleted hydrocarbon reservoirs; reservoir engineering; microbial H\({}_{2}\) generation; in situ combustion; geologic carbon storage +
Footnote †: journal: Applied Sciences