esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of the Yield Strength of RC Columns Using a PSO-LSSVM Model
## 2 Pseudo-Static Cyclic Test and Yield Point
### Yield Point  
It is well known that how to judge the yield point on the specific skeleton curve is still a complicated problem, and there is no unified standard in the related research field [7; 43; 48]. Here are three judgment methods commonly used to determine the yield point as follows:  
1. For the Geometric Graphic Method shown in Figure 2a, the yield displacement and yield point are defined as follows: Draw the tangent line to the curve at 'O'. The line is extended to the intersection with a horizontal line through 'D' at 'A', where the 'D' corresponds to the maximum applied shear \(P_{max}\) shown in Figure 2a. The perpendicular of line 'OA' intersects the curve at 'B'. Connect 'O' and 'B' and extend line 'OB' to meet the horizontal line 'DA' at 'C', and then project onto the horizontal axis to obtain the yield displacement \(\Delta_{y}\) and the yield point 'E' on the curve, where it corresponds to the yield applied shear \(P_{y}\).
2. For R. Park Method shown in Figure 2b [48], the yield displacement and yield point are defined as follows: A secant 'OB' is drawn to intersect the lateral load-displacement relation at a certain proportion of the maximum applied shear, i.e., the 'B' on the curve corresponding to \(\alpha P_{max}\) shown in Figure 2b. Similarly, this extension of line 'OB' and the horizontal line corresponding to the maximum applied shear \(P_{max}\) intersect at 'A', and then projects onto the horizontal axis to obtain the yield displacement \(\Delta_{y}\). The intersection point 'C' of the vertical and curve is defined as the yield point, which corresponds to the yield applied shear \(P_{y}\).
3. For Equivalent Elasto-Plastic Energy Method shown in Figure 2c, the yield displacement and yield point are defined as follows: Determine a point 'B' on the curve and draw secant 'OB' to intersect the curve for satisfying the principle that the energy absorbed by the ideal elastoplastic structure is equal to that absorbed by the actual structure, i.e., the areas of shaded area 'OAB' and 'BCD' are equal shown in Figure 2c. Similar to the R. Park Method in Figure 2b, this extension of line 'OB' and the horizontal line corresponding to the maximum applied shear \(P_{max}\) intersect at 'C', and is then projected onto the horizontal axis to obtain the yield displacement \(\Delta_{y}\). The intersection point 'E' of the vertical and curve is defined as the yield point, which corresponds to the yield applied shear \(P_{y}\).  
In this paper, the yield point of the skeleton curve of the load-displacement curve is calculated by using the Equivalent Elasto-Plastic Energy Method.  
Figure 1: The hysteretic curve and skeleton curve.
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 6 Conclusions
### Summary and Conclusions  
In the context of this work, protocols were studied and developed that can be applied to Delay Tolerant Networks, including the concept of the Age of Information. First, the FRESH protocol was described in detail, which is one of the most widespread protocols in DTNs, especially due to its simplicity in terms of implementation. More specifically, three variants of the protocol were analyzed, depending on the way each node chooses to find the next relay node for a specific packet. Then, it was explained that the original version of this protocol does not help to maintain a relatively young age of transmitted packets in the DTN network, resulting in packet freshness fading more and more over time. Subsequently, the modified FRESH protocol proposed in this work was analyzed, with the addition of the concept of the Age of Information. More specifically, in order for the packets not to be constantly transferred to the DTN network (thus wasting its available resources), as soon as they exceed a certain threshold value in their age domain, they are automatically dropped from the network. The results of the conducted simulations demonstrate the enormous benefit of applying the modified protocol proposed in the paper in terms of the average packet age metric. At the same time, it was experimentally proven that imposing the Bound on the age of the packets did not significantly affect the part of the system throughput. In fact, it was demonstrated that the value of the AoI barrier can be set quite low, without an appreciable change in the throughput and at the same time achieving a huge increase in the freshness of the transmitted packets.
# Monitoring and Calibrating Building Materials Drying Kinetics with Capacitance Sensors
###### Abstract  
Building materials' moisture content defines the overall durability and serviceability of buildings and infrastructures. Conventionally, moisture content is estimated by weighing the materials before and after drying. In this work, the results of capacitance wired sensors for measuring moisture content were investigated. These sensors facilitate in situ measurement of capacitance values, with the aid of specialized installation equipment, and the results are accessible via a cloud-based software application. In practice, the main drawback associated with the application of these sensors is related to their calibration. Thus, it is important to investigate different algorithms which convert capacitance readings into moisture content values. To address this, various specimens covering a diverse set of building materials were prepared in the laboratory. This study focused on examining two bricks, two sedimentary stones with low and high porosity values. and four different kinds of mortars (with different binders, namely hydrated lime putty, cement, natural hydraulic lime, and hydrated lime powder and pozzolan). The results indicated that a linear model can be recommended for calibrating sensor capacitance to moisture content measurements. This model can be used for the prediction of building material moisture content with high accuracy, from saturated to the dry state, covering the full range of drying kinetics.  
gravimetric method; stone; cement; lime; hydraulic lime; brick +
Footnote †: journal: Applied Sciences  
11 December 2023  
1
## 3 Methods
### Preparation of Virtual Ground Using DEM
#### 3.1.2 DEM Calculation Conditions  
In this study, the following material properties were used: Young's modulus of 355 MPa, shear modulus of 120 MPa (equivalent to a Poisson's ratio of 0.48), material density of 2650 kg/m\({}^{3}\), and restitution coefficient of 0.2. These values reflect the general physical properties of soil. DEM was applied in order to simulate the geometric positions of the void space of porous media created with certain conditions having different particle sizes and shapes. Since the purpose is only to create a pore structure, the effects of friction were not considered. Namely, the static friction coefficient and the rotational friction coefficient were set to zero.  
The purpose of this study is to relate porous structures to the flow paths within them. DEM is used as a means to simulate porous structures. Since it is not necessary to create a strictly porous structure for this purpose, classical mechanical models, general model parameters, and zero friction are used to reduce the computational effort. The most influential factor is the particle size distribution and particle shape, which is parametrically changed in the DEM simulation for preparing the virtual ground.  
The dimensions of the analysis space were 240 mm wide, 410 mm high, and 50 mm deep, and a filling simulation was conducted by three types of freefalling samples, which have different particle sizes and shapes, from the top of the analysis space. In Case 1, only spherical elements with a uniform diameter of 10 mm were used. In Case 2, in order to simulate the ground, the particle size distribution was considered. Spherical elements with diameters of 10 mm, 6 mm, and 2 mm were mixed in a volume ratio of 2:3:5. In Case 3, assuming a waste landfill site, spherical elements with a diameter of 10 mm were mixed with thin plate elements with a width of 8 mm, depth of 8 mm, and thickness of 5 mm, and bar elements with a diameter of 5 mm and length of 15 mm in a volume ratio of 1:1:1. The thin plate element and the bar element were modeled with 5 mm diameter rigid particles at fixed intervals. The thin plate consists of 16 spherical particles arranged in a grid pattern with a particle pitch of 1 mm, and the rod-shaped particle consists of 10 spherical particles arranged in a single row. The particle pitch is 1.11 mm (see Figure 2). The calculation algorithm of DEM was referred from the OpenFOAM code documentation.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
###### Abstract  
Fifth-generation (5G) mobile networks aim to be qualified as the core connectivity infrastructures to address connected automated mobility (CAM), both from a technological and from a business perspective, for the higher automation levels defined by the automotive industry. Specifically, in some territories such as the European Union the cross-border corridors have relevance, as they are the cohesive paths for terrestrial transport. Therefore, 5G for CAM applications is planned to be deployed there first. However, cross-border contexts imply paramount communication challenges, such as seamless roaming, not addressed by current technology. This paper identifies relevant future 5G enhancements, specifically those specified by Third-Generation Partnership Project (3 GPP) releases beyond Release 15, and outlines how they will support the ambitions of highly automated driving in cross-border corridors. In order to conduct this study, a set of representative use cases and the related communication requirements were identified. Then, for each use case, the most relevant 5G features were proposed. Some open issues are described at the end.  
5G; connected and automated mobility; V2X; multi-PLMN; cross-border; roaming Article
# Flying Watchdog-Based Guard Patrol with Check Point Data Verification
## 3 Proposed Flying Watchdog Architecture
### Flying Watchdog Model  
The initial phase of this research is to develop a drone with a 680 mm wheelbase frame and PGP design. This study employs a meticulously designed drone measuring 48 cm in both length and width, featuring a robust 400 kV rotor, 13-inch propeller, and a high-capacity 6500 mAh 80C 14.8-volt Lithium Polymer (LiPo) battery, affording it an impressive flight duration of 45 min. The mechanical structure of the drone is seamlessly integrated with a flight controller boasting an array of sensors, including an accelerometer, gyroscope, digital compass, GPS, and lidar. This amalgamation equips the drone with the capability to operate semi-autonomously. Crucially, the drone establishes a vital connection with a monitoring station via a 915 MHz, 100 mW radio telemetry system, offering a transmission  
Figure 1: The illustration of the proposed method.  
range of up to 300 m. A comprehensive illustration of the drone's configuration is presented in Figure 2, providing valuable insights into its intricate design and componentry.  
The architectural framework of the aerial watchdog system is meticulously organized into three distinct modes: the flying mode, the searching mode, and the communication mode. The flying mode serves as the platform for executing predefined flight commands and overseeing the autonomous maneuvering of the drone en route to its destination. In the searching mode, a high-level processing computer, the 4 GB 64-bit NVIDIA Jetson Nano, is engaged. It is seamlessly connected to a flight controller via the universal asynchronous receiver transmitter (UART) communication protocol, operating at speeds of up to 921,600 bps (bits per second). In this configuration, hardware and telemetry compatibility are optimized at 115,200 bps.  
Furthermore, the searching mode is integrated with a depth camera D435, serving as a sensory device to detect PGP based on color and shape criteria. Given the continuous monitoring requirements of both the PGP and flying mode, a third microcontroller is introduced to facilitate communication between the drone and the PGP. This microcontroller is also equipped with a laser for data transmission purposes. The communication protocol employed in this system utilizes half-duplex serial communication, whereby signals are transmitted from the NVIDIA Jetson to the Arduino microcontroller, initiating data transmission via the laser for a 20 s duration. A comprehensive visualization of the configuration of these three operational modes is thoughtfully depicted in Figure 3, providing valuable insights into the system's intricate design and functional interplay. Communication between the flight controller and NVIDIA Jetson is carried out using MAVROS, which bridges the MAVLink communication protocol from the flight controller with robot operating systems on NVIDIA Jetson. The drone configuration, monitoring, and predetermined flight pattern are carried out using the open-source Mission Planner platform.
## 4 Numerical Examples  
In the present study, four explicit performance function and two practical engineering cases were employed to evaluate the effectiveness and robustness of the proposed candidate samples generating algorithm. Four explicit performance functions, including two two-dimensional and two three-dimensional problems, are used to observe the characteristics of the proposed method through the visualization of the whole sampling process and resultsof the algorithm. Two practical engineering cases includes a two-layered saturated clay slope with two state variables and a two-layered slope with three state variables.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 2 Background
### Measuring Oxygen in Packaging
#### 2.2.3 Fluorescence Detectors  
A technology based on fluorescence is employed an alternative to the previous techniques. It is non-destructive, highly sensitive and can be used in real conditions [75; 76; 77]. The method employs a small sticker to be glued on the inside of the package. It contains metallic complexes (mostly Ru or Pt) that will fluoresce when excited by certain wavelengths. Light is both emitted and subsequently absorbed again by a fluorescence-detector that can be placed on the outside of the package. The fluorescence of the complex is quenched as the excited energy of the complex is transferred to molecular oxygen in the formation of the singlet state. When in contact with an oxygen-rich environment, the fluorescence signal will therefore exhibit a long apparent fluorescence time and a low emission intensity. This can be related accurately even to low concentrations of oxygen present in the package [62].  
Measuring oxygen through such a sensor inside the package can deliver the real conditions over an entire shelf life and produce very realistic results. However, as shelf lives of certain bottlescan extend over several years, such samples require monitoring over the full period and storage in a controlled environment to dismiss seasonal changes. Additionally, colored bottles may often interfere with the measurements as it can absorb both the emitted or the fluoresced light. Potentiometric methods will always need to be performed for these bottles. Still, this is the best quantification method for clear active packaging solutions, being able to plot oxygen concentration of a certain bottle in time. The sensor dot can also be used in a flushed system comparable to the second potentiometric method to measure quickly the OTR of a package, but again no liquids are compatible with this method [78].
# A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
## 3 Theoretical Background of Deep Learning Methods Used for Self-Driving Cars
### Deep Reinforcement Learning (DRL)  
In recent years, the deep reinforcement learning method has been actively adopted to deal with various control problems in the unmanned vehicle field [37]. In reinforcement learning (RL), agents (like self-driving cars) can learn to modify their actions according to the rewards or punishments when interacting with environments (see Figure 8). The work process of RL can be described as the Markov Decision Process (MDP). In general, a MDP consists of a set of states \(s\in S\), a set of actions \(a\in A\), a reward function \(R\) and a transition model \(P\)[38].  
A self-driving car (agent) perceives the current environment with state \(s_{t}\) and performs an action \(a_{t}\), then receives a reward \(r\left(s_{t},a_{t}\right)\) by the environment and transits to the next state \(s_{t+1}\) according to the transition function \(P\left(s_{t+1}\left|s_{t},a_{t}\right.\right)\). The self-driving car is able to obtain a policy \(\pi\), which maps every state \(s_{t}\) to action \(a_{t}\). The objective in RL is to maximize the accumulated discounted reward \(R_{t}\), which is calculated by  
\[R_{t}=\sum_{i=t}^{T}\gamma^{i-t}r\left(s_{i},a_{i}\right), \tag{5}\]  
where \(\gamma\) is the discount factor that controls the importance of immediate and future rewards.  
Figure 7: The structure of the traditional Auto-Encoder network (AE).  
Q-learning is a model-free method of RL, where the action-value function \(Q\left(s,a\right)=\underset{\pi}{\max}\,\mathrm{E}[R_{t}\left|s_{t},a_{t},\pi\right| ]\) is used to represent the expectation of \(R_{t}\) for every action-state pair \(\left(s_{t},a_{t}\right)\) and the function \(\mathrm{E}[\cdot]\) denotes the expected value of a random variable [39,40]. Q-learning approach is based on the optimization of the action-value function, which is calculated as follows:  
\[Q^{*}\left(s_{t},a_{t}\right)=\mathrm{E}\left[r\left(s_{t},a_{t}\right)+\gamma \max Q\left(s_{t+1},a_{t+1}\right)\right], \tag{6}\]  
where \(Q^{*}\) is the optimal value of \(Q\). The optimal policy \(\pi^{*}\) is to maximize the value function, namely  
\[\pi^{*}=\underset{\pi}{\arg\max}\,Q^{*}\left(s,a\right). \tag{7}\]  
Deep reinforcement learning (DRL) combines deep learning and reinforcement learning to solve the problem of capacity limitation and sample correlation. DRL has both the perceptive ability of deep learning and the decision-making ability of RL in a general form. DRL can learn a mapping from the original input to the action output [41]. One of the DRL methods is Deep Q-Network (DQN), which can utilize a deep neural network to map the relationships of actions and states, which is similar to the Q-learning method [42]. The DQN uses a CNN as the function approximator with weights \(\theta\) as a Q-network, which is shown as follows:  
\[y_{i}=Q_{i}^{*}\left(s_{t},a_{t}\right)=\mathrm{E}\left[r\left(s_{t},a_{t} \right)+\gamma\max_{a_{t+1}}Q\left(s_{t+1},a_{t+1};\theta_{i-1}\right)\left|s _{t},a_
###### Abstract  
This paper presents a novel method for inner profile measurement and geometric parameter evaluation, such as the radius of the bottom, steepness and straightness of the steep sidewall of a high aspect ratio aspheric workpiece, by utilizing a two-probe measuring system, which includes a lateral displacement gauge for the inner steep sidewall profile measurement and an axial displacement gauge for the inner deep underside profile measurement. To qualify the measurement accuracy, the systematic errors associated with the measurement procedure, including the miscalibration, misalignment and the roundness error of the gauge probes, as well as the slide motion error of the four-axis motion platform, are all evaluated and separated from the measurement results. A point cloud registration algorithm is employed to stitch the evaluated inner sidewall profile and the inner underside profile to form an entire inner profile of the workpiece. To verify the performance of the newly proposed method, the inner profile of a high aspect ratio aspheric workpiece, which has a tapered cone shape with a maximum inner radius of 40 mm, a maximum inner depth of 140 mm and a steep sidewall angle approaching 85\({}^{\circ}\), is measured in experiments. The measurement result is compared with that of a coordinate measuring machine (CMM), and the comparison verifies the feasibility of the proposed measurement system.  
high aspect ratio aspheric workpiece; inner profile measurement; error separation; error compensation; profile stitching +
Footnote †: journal: Medical Imaging  
0000-0001-8998-333X][PERSON] \({}^{*}\), [PERSON], [PERSON] and [PERSON]
## 3 Related Work
### System Architecture of Proposed Model  
The system under study uses an innovative architecture for medical data communications with the use of a blockchain, as shown in Figure 5. The suggested design consists of a service provider in a home station, a Shibboleth identity provider, and a blockchain ledger. The service provider must be registered in the home station, and provides the information to remote stations when required. The home station properly investigates the information, and only provides the information after verifying the role and authentication level of the medical person demanding the information. The home station prohibits access to the information if the request is not appropriate to the level of the role asking for the information.  
In this study, Shibboleth federated identity management was included as the service provider due to its open-source nature and its adaptability in the domain. Using Shibboleth, each medical person is given a unique id and is authorized according to their role. Depending on their role, it is decided whether the healthcare personal should be given SSO or complete login credentials for that role, and for how long these credentials remain active for the particular medical person.  
The blockchain maintains all records using CouchDB, according to the standards provided by HL7, thus ensuring the interoperability between different systems. This study used a permission blockchain, so no proof-of-work concept is required; rather, consensus is achieved using an automated procedure to maintain the efficiency of the system. A well-known Hyperledger algorithm, RAFT, was used as the consensus algorithm. RAFT is a fault-tolerant and easily understandable consensus algorithm that enables clients to create distributed systems as a single system. Randomized election, log replication, fault  
Figure 5: Proposed architecture.  
tolerance, and ease of use are the distinguishing properties that motivated the use of RAFT in this study.
## 3 Application Protocol for Wireless Intelligent Sensor Networks
### Sensor Multi-Agent Structure
#### 3.1.2 Application Control Agent  
Executed by the management agent, the application control agent allows the sensor nodes to control the execution of different applications (take measurements of sensors such as temperature or humidity, actuate diverse actuators, infer an output in a FRBS, infer and collaborate in FRBS, _etc._). The main functions of this agent are the following:  
1. Control the execution of different applications.
2. Control the interval between consecutive application executions. This application interval may be different from the sleep mode interval.
3. Control the schedule and the number of application executions.
4. Update KBs in FRBS applications incrementally or completely.
5. Schedule the way in which the sensor nodes notify their measurements or inferences (instantaneous values, average value in a period, alarms or values out of a range defined by thresholds, periodic values, _etc._).
6. Provide the agent state.
7. Manage the persistent storage for other agents and applications.