esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Microstructural Characterization  
The optical micrographs of the Fe-13 Cr-4 Al alloy are depicted in Figure 2. The alloy microstructure comprises equiaxed grains with an average grain size of roughly 40 \(\upmu\)m. [PERSON] et al. [9] employed various hot-working techniques to produce a series of model FeCrAl alloys, aiming to investigate the microstructural and mechanical characteristics of these alloys under diverse processing conditions. The optimal process identified for achieving a grain size ranging from 20 to 80 mm involved hot rolling at 700 \({}^{\circ}\)C with approximately 90% thickness reduction. Notably, this hot-working procedure and the resultant  
Figure 1: Dimensions of the tensile specimen (unit: mm).  
grain size closely resemble those obtained in the current study. The SEM images presented in Figure 3a,b distinctly reveal the presence of equiaxed grains and their boundaries within the microstructure. The EDS mapping images shown in Figure 3c further demonstrate a uniform distribution of constituent elements, including Fe, Cr, Al, Mo, Nb, and Ta, both at the grain boundaries and within the matrix. While certain studies have documented the existence of quadrangular-shaped AlN particles as the primary precipitate in the microstructure of FeCrAl alloys [33; 34], such precipitates are notably absent in the present Fe-13 Cr-4 Al alloy.
# An Intelligent Control Method for Servo Motor Based on Reinforcement Learning
## 5 Experiment and Analysis  
### Experimental Environment and Setting  
To verify the effectiveness of the proposed reinforcement learning control algorithm, the Python-based Gym Electric Motor was selected, which is an open-source simulation platform. It provides a simple algorithm interface for reinforcement learning agents like the OpenAI Gym. Therefore, the interaction between agents and the environment can be simulated conveniently, which provides favorable conditions for researchers to develop and test reinforcement learning control algorithms [30].  
As shown in Table 1, six common tasks were selected to verify the performance of our method.  
Aside from the proposed method, four other similar methods are selected to compare the performance. Firstly, the depth deterministic policy gradient (DDPG) and twin delay depth deterministic strategy gradient (TD3) of two deep reinforcement learning algorithms are selected. Secondly, the difference value input (DVI) and state input without special processing (SI) are also taken into account as feature extraction methods. Therefore, we combined the reinforcement learning algorithm and feature extraction method to obtain the four comparison methods, which are termed as DDPG-DVI, DDPG-SI, TD3-DVI, and TD3-SI, respectively.  
Both the strategy network and value network of each method contain two hidden layers, and each of them has 512 neurons. The ReLU activation function is used. The historical input length of the value network is 20. The chosen batch size is 256. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **State Dimension** & **Target** & **Duty Cycle Dimension** & **Motor** \\ \hline TC-ExtExDc & 7 & Torque control & 2 & Externally excited DC motor \\ SC-ExtExDc & 7 & Speed control & 2 & Externally excited DC motor \\ TC-PMSM & 14 & Torque control & 3 & Three-phase permanent magnet synchronous motor \\ SC-PMSM & 14 & Speed control & 3 & Three-phase permanent magnet synchronous motor \\ TC-SeriesDc & 5 & Torque control & 1 & Series DC motor \\ SC-SeriesDc & 5 & Speed control & 1 & Series DC motor \\ \hline \hline \end{tabular}
\end{table}
Table 1: The used tasks.  
Adam optimizer [31] is used to optimize network parameters for gradient descent, and the learning rate of the optimizer is set to 0.00003. The discount reward factor is 0.99.
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## 3 Results and Discussions
### Blood Flow and Platelets Migration in Aneurysms
#### 3.2.2 Bulging Saccular Aneurysm
Figure 9: (**a**) The distributions of the platelets concentration in the bulbous saccular aneurysm at \(\tau=5000\). (**b**) The distributions of the platelets concentration in the bulging saccular aneurysm at \(\tau=5000\). The unit of the scale bar is \(1\times 10^{14}/\mathrm{m}^{3}\). (**c**) The platelets concentration profiles along the lines \(A\)-\(A\) at \(\tau=500\) and \(\tau=5000\). According to the observations above, it can be observed that the concentration of platelets in the aneurysm tends to increase with a smaller aneurysm neck size or a larger Reynolds number. Specifically, the smaller neck promotes the circulation within the aneurysm and prevents platelets from escaping. The combination of higher concentration and dwell time of platelets increases the probability of activation, and thus coagulation. This, in turn, influences clot formation in the aneurysm, which is known to be one of the healing mechanisms [2; 3; 4] and may be helpful for preventing rupture. However, if a clot formed within the aneurysm embolizes, it may have a deleterious effect, such as stroke. A limitation of this study is the applied lift force model, see Equation (10), which was based exclusively on shear lift, derived from classical multicomponent theory [31; 41; 42]. Although this model has been effective in predicting moderate RBC depletion near the wall, it is not capable of predicting near-wall RBCs free layer. Therefore, an improvement would be to introduce the wall effect which is influenced by the particle deformability and the distance from the wall, as described by [PERSON] et al. (2012) [43] [PERSON] et al. (2013) [44], and [PERSON] et al. (2012) [45]. Another limitation is the use of 2D approximation of a spheroidal domain. This was done to economize computational time, inasmuch as each simulation required about 24 h to reach steady state using 4 CPUs at 2.7 GHz. This was due to the relatively slow process for development of the concentration fields (RBCs and platelets) in the  
Figure 11: Platelet concentration profiles along the lines \(A\)-\(A\) in Figure 9, at \(\tau=5000\), with different Reynolds number in the bulbous (**a**) and the bulging (**b**) saccular aneurysms. Figure 10: **(a**) The distributions of the platelets concentration in the bulbous saccular aneurysm at \(\tau=5000\), when Re = 1, 10 and 50. (**b**) The distributions of the platelets concentration in the bulging saccular aneurysm at \(\tau=5000\), when Re = 1, 10 and 50. The unit of the scale bar is \(1\times 10^{14}/\text{m}^{3}\). aneuryss. However, the key observations in this study are likely to reflect the 3-D case, since the secondary flow in the azimuthal direction is negligible. The reported results accentuate the importance of a multi-phase model on platelet transport, and motivate further study of thrombosis in aneurysms using this simulation model.
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 1 Introduction
This study utilized synthetic data generated from Aspen HYSYS simulations, replicating realistic pipeline conditions with features such as flow rate, temperature, pressure, gas specific gravity, and water content, to compute the minimum methanol required for hydrate inhibition. We proposed four ML models--extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and k-nearest neighbors (KNN)--to predict methanol quantities, assessing their efficacy using metrics such as the mean absolute error (MAE), root mean square error (RMSE), and adjusted R-squared (adj. R\({}^{2}\)). The primary objective was to identify the most accurate and efficient model for determining methanol requirements, addressing a novel gap in hydrate inhibition beyond the constraints of traditional simulation methods. By coupling ML with Aspen-HYSYS-generated data, this research sought to deliver a scalable, data-driven solution for optimizing methanol use, minimizing costs, and enhancing the operational reliability of gas transportation systems.
## 4 Systematic Review of the Research Pillars
### Sustainability in Urban Development
#### 4.3.2 Data Management and AI in Urban and Building Systems  
To improve efficiency, sustainability, and quality of life in smart cities, data management and artificial intelligence (AI) must be integrated into building and urban systems. Better decision-making and operational performance result from this synergy, which makes it possible to handle the massive amounts of data produced by building management systems and urban infrastructures. AI-Powered Data Administration optimizes data processing in Smart Cities as high data quality (95.12% score) and privacy preservation (85.23% rate) are guaranteed by the AI-based Data Management System (AI-DMS [83]. To improve nuanced data sharing while preserving security, it uses a Multi-Level Sensitive Model for data classification [83]. Automation and Management Systems for Building is greatly enhanced by AI big data analytics, which makes it possible to do operations like performance optimization and energy anomaly detection [84]. Case studies show how real-time data analysis can improve operational efficiency through effective applications in both residential and business settings [84]. AI and IoT integrate to optimize urban administration and improve citizen experiences by enabling real-time data collection and autonomous analysis [85]. Applications that successfully handle urban difficulties include intelligent transportation and predictive maintenance [85].
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 4 LoRaWAN Medium Access Control Protocols
### Polite Spectrum Access  
A polite medium access requires that the end-devices listen to the channel to sense if it is busy or not by running a CCA before transmitting. In particular, if the mean signal level detected at the CCA interval (\(\Delta_{CCA}\)) is below the CCA threshold, the end-device will start to send the message; otherwise, applying the AFA technique, the end-device switches to another channel and restarts the CCA before trying to transmit.  
If the transmitter detects that all channels are busy, the transmission is delayed for a deferral interval (\(\Delta_{def}\)), i.e., the backoff delay. In this way, message collisions are prevented. Note that an end-device is not allowed to try to access the same channel before a random time interval (longer than a specific value) expires.  
Table 2[40] shows the restrictions that the ETSI regulations impose on the usage of the MAC protocol based on LBT AFA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sub-Band**} & \multirow{2}{*}{
\begin{tabular}{c} **Frequency Band** \\ **(MHz)** \\ \end{tabular} } & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{\(\mathcal{D}\mathcal{C}^{max}\)} & \multirow{2}{*}{\(\mathcal{T}\mathit{xPow}^{max}\) (dBm)**} \\  & & & & \\ \hline h1.4 & 868.00–868.60 & 3 & 1\% & 14 \\ h1.5 & 868.70–869.20 & 2 & 0.1\% & 14 \\ h1.6 & 869.40–869.65 & 1 & 10\% & 27 \\ h1.7 & 869.70–870.00 & 1 & 1\% & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Duty-cycle limitations of the sub-bands of the EU863-870 MHz ISM band that match the ERC Recommendation 70-03.  
As shown in Table 2, the minimum CCA interval (\(\Delta_{CCA}^{min}\)) shall be 160 \(\upmu\)s. If the target channel is busy, \(\Delta_{def}\) shall be larger than or equal to a specific value, i.e., \(\Delta_{def}^{min}\) that corresponds to the CCA length. The minimum time between two consecutive deferral intervals, here called \(\psi_{A_{def}}^{min}\), is declared by the manufacturer. Once the CCA is performed, the end-devices must start the message transmission within a value \(\Theta_{CCA\_I,xx}^{max}\) declared by the manufacturer, which is lower than or equal to 5 ms. A single transmission shall last less than or equal to a \(ToA_{rx}^{max}\), i.e., 1 s. The maximum duration of a sequence of messages, called a transmission dialogue, shall end within \(\Lambda_{rx}^{max}\). According to the ETSI regulations, this value is equal to 4 s. Moreover, after each message transmission, an end-device shall be silent for a time interval, here called \(\mathcal{T}_{Off\_Time}^{min}\), higher than or equal to 100 ms.  
Finally, each end-device is allowed to transmit up to 100 s per hour over a 200 kHz spectrum. As a result, the duty cycle per 200 kHz spectrum (\(\mathcal{DC}_{200\mathrm{kHz}}^{max}\)) is equal to approximately 2.78%.
###### Abstract  
Shadow often results in difficulties for subsequent image applications of multispectral satellite remote sensing images, like object recognition and change detection. With continuous improvement in both spatial and spectral resolutions of satellite remote sensing images, a more serious impact occurs on satellite remote sensing image interpretation due to the existence of shadow. Though various shadow detection methods have been developed, problems of both shadow omission and nonshadow misclassification still exist for detecting shadow well in high-resolution multispectral satellite remote sensing images. These shadow detection problems mainly include high small shadow omission and typical nonsmadow misclassification (like bluish and greenish nonsmadow misclassification, and large dark nonshadow misclassification). For further resolving these problems, a new shadow index is developed based on the analysis of the property difference between shadow and the corresponding nonshadow with several multispectral band components (i.e., near-infrared, red, green and blue components) and hue and intensity components in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ), respectively. The shadow mask is further acquired by applying an optimal threshold determined automatically on the shadow index image. The final shadow image is further optimized with a definite morphological operation of opening and closing. The proposed algorithm is verified with many images from WorldView-3 and WorldView-2 acquired at different times and sites. The proposed algorithm performance is particularly evaluated by qualitative visual sense comparison and quantitative assessment of shadow detection results in comparative experiments with two WorldView-3 test images of Tripoli, Libya. Both the better visual sense and the higher overall accuracy (over 92% for the test image Tripoli-1 and approximately 91% for the test image Tripoli-2) of the experimental results together deliver the excellent performance and robustness of the proposed shadow detection approach for shadow detection of high-resolution multispectral satellite remote sensing images. The proposed shadow detection approach is promised to further alleviate typical shadow detection problems of high small shadow omission and typical nonsmadow misclassification for high-resolution multispectral satellite remote sensing images.  
shadow detection; invariant color space; high-resolution multispectral satellite remote sensing image; threshold; WorldView-3; WorldView-2
# Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programability
## 4 Architecture Design and Methodology
### Dynamic QoS Methodology
#### 4.2.2 Bandwidth Allocation Mechanism  
Dynamic queues' management may not be sufficient to combat future challenges in high-demand networks. A higher level of intelligence is required to observe both service requirements and link capabilities before making the right decisions for max-rates and queue sizes. Therefore, we propose a link bandwidth allocation mechanism that uses information stored in the database and calculates max-rates for each user's download and upload directions and slices. For each user \(i\) and slice \(j\), the queue size (max-rate) is calculated using Formula (1):  
\[q_{ij}\ =\ \left\{\begin{array}{ll}c_{i}w_{ij},&t_{ij}\ \leq\ c_{i}w_{ij}\ \wedge\ s_{ij}\ +\ c_{i}w_{ij}\ \leq\ c_{i}\\ \\ m_{ij},&t_{ij}\ >\ c_{i}w_{ij}\ \wedge\ s_{ij}\ +\ m_{ij}\ \leq\ c_{i}\\ \\ c_{i}\ -\ s_{ij},&(t_{ij}\ \leq\ c_{i}w_{ij}\ \wedge\ s_{ij}\ +\ c_{i}w_{ij}\ >\ c_{i})\\ &\vee\ (t_{ij}\ >\ c_{i}w_{ij}\ \wedge\ s_{ij}\ +\ m_{ij}\ >\ c_{i})\\ \end{array}\right. \tag{1}\]  
where \(t_{ij}\) is the minimal download/upload rate for user \(i\) and slice \(j\); \(c_{i}\) is the download/upload capacity of the link for user \(i\); \(w_{ij}\) is the link capacity portion (percentage); \(p_{ij}\) is the priority of slice \(i\), user \(j\); finally, \(m_{ij}\) and \(s_{ij}\) are calculated according to Formulas (2) and (3).  
\[m_{ij}\ =\ \min\{t_{ij},\ c_{i}\ -\ \sum_{k,l}t_{kl}\ |\ p_{kl}\ <\ p_{ij}\} \tag{2}\]  
\[s_{ij}\ =\ \sum_{k,l}\max\{c_{k}w_{kl},\ t_{kl}\}\ |\ p_{kl}\ <\ p_{ij} \tag{3}\]  
According to Formula (1), we calculate queue size based on three criteria:  
* Link capacity is sufficient so that all slice requirements can be satisfied;
* Proportional (percentile) bandwidth allocation is less than the minimum rate required by the slice (2);
* Slice requirement could not be satisfied due to reduced link capacity and bandwidth allocation by higher-priority slices (3).  
In cases when the proportional allocation is less than the minimum rate, a minimum required rate is used. However, if higher-priority slices have already allocated bandwidth by the first two criteria, the slice could only use the remaining unallocated rate.  
We calculate the link bandwidth allocation in descending order of priority. Therefore, we allocate bandwidth for slices with lower priority before those with a higher priority number.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Reliability  
Pertains to the dependability and consistency of data in transmission. It focuses on ensuring that data is delivered accurately and consistently without loss, errors, or disruptions. Ensuring packet delivery reliability depends on multiple factors. The utilisation of a retransmission-based recovery mechanism and packet loss awareness leads to congestion and routing failure awareness protocols to guarantee packet reception [38]. These reliability-centric initiatives in protocol communications are pivotal in achieving accurate and effective data transmission, contributing to overall performance and reliability in V2I interactions and communication networks in general.
## 3 Proposed Control Design
### Simulation Description  
For simulation, a dual-3 DOF manipulator with the dynamics and its parameters presented in [18] was simulated on MATLAB Simulink with a sampling time of 10 ms and the type of auto solver. The simulation period was divided into different scenarios for 40 s. The **first scenario** takes place in the first 10 s when only the modeling errors in the dual arm exist. Each robot has a different modeling error. The **second scenario** happens in the second 10 s when an external force is applied at the end-effector of the second robotic arm. The **third scenario** is the third 10 s when the first actuator in the first arm raises the wrong control direction. The **last scenario** is the final 10 s when the wrong control direction occurs in the first actuator at the second manipulator.  
The reference trajectories in the joint space of each manipulator are given as  
\[\begin{array}{c}q_{1d}(t)=30\sin(0.2\pi t)+90(\text{deg.})\\ q_{2d}(t)=30\sin(0.2\pi t)(\text{deg})\\ q_{3d}(t)=30\sin(0.2\pi t)(\text{deg})\end{array} \tag{24}\]  
The modeling error is the unknown friction, including viscous and Coulomb frictions. Its equation is expressed as follows:  
\[\boldsymbol{\tau}_{fric}(t)=\mathbf{v}\dot{\mathbf{q}}+\mathbf{c}sign\big{(} \dot{\mathbf{q}}\big{)}\left(Nm\right) \tag{25}\]  
where \(\mathbf{v}=0.2\mathbf{I}_{6\times 6}\) is viscous friction gain and \(\mathbf{c}=0.3\mathbf{I}_{6\times 6}\) is the Coulomb parameter.  
The external disturbance is an external force applied along the \(z\)-axis at the end-effector of the second robot with an amplitude of 10N.  
\[F_{ext1}=\left[0,0,10\right]^{T}(N) \tag{26}\]  
The efficiency of the actuators in normal cases is presented with respect to \(\boldsymbol{\Pi}=\mathbf{I}_{6\times 6}\). The wrong control direction of the first actuator in the second robot occurs and the input matrix \(\boldsymbol{\Pi}=diag(\left[1,1,1,-1,1,1\right])\in R^{6\times 6}\). In the fourth scenario, when the wrong control direction happens in the first actuator of the first robot, the input matrix is presented as \(\boldsymbol{\Pi}=diag(\left[-1,1,1,-1,1,1\right])\in R^{6\times 6}\).
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 3 Background
### Motivation and Contribution of the Paper  
Combination of both the network layer and the MAC layer creates the cross-layer approach for the network's better performance and is mainly focused on increasing the efficiency of the VANETs by finding the optimal path and best neighbor selection.  
Originally, the MAC-based neighbor discovery and HELLO packet processing for finding the best neighbors in VANETs were introduced.  
In order to overcome the delay and overhead optimal path finding, using the updated genetic algorithm is proposed.  
Analyzing with serious network scenarios, better performance is gained.
## 2 Geodetic Data Sets and Processing
### GPS Measurements  
The GPS network in Taiwan was installed in 1989 and has been surveyed since 1990 covering the whole island. Figure 2 is showing benchmarks and an antenna of the Taiwan network. Specifically for the coseismic study (Figure 1A), the displacements were extracted from Reference [9]. 41 permanent stations are available (operated by the Central Weather Bureau (CWB); the Satellite Survey Division and Land Survey Bureau, Ministry of Interior (MOI) and the Institute of Earth Sciences, Academia Sinica (IESAS)) and 101 campaign-surveyed stations (conducted by three institutions (the Central Geological Survey, Ministry of Economic Affairs (MOEA, CGS); MOI and IESAS)) covering 1992 to 1999. They separated pre- and post-seismic deformation to estimate the coseismic displacement due to the Chi-Chi earthquake. They were processed using Bernese software v. 4.0 and corrected with precise ephemerides, and residual tropospheric zenith delays were estimated by least squares. The reference station selected is Kinmen, located on the east coast of mainland China (\(\sim\)257 km from the epicenter) which did not show any coseismic movement. Furthermore, the pre-seismic secular motions were corrected by interpolating the velocity of 24 stations observed from 1992 to 1999. Finally, post-seismic displacements were corrected at the stations surveyed during the three months following the earthquake by interpolation from the nearby stations [17]. GPS, benchmarks and levelling are well correlated with each other.  
In addition to GPS data, benchmarks of urban planning [18] situated at the north part of the fault, in Fengyuan city, were used (locations shown by the purple dashed box in Figure 1A and the displacements on the footwall and hanging wall in Figure 1B,C respectively). The pre-earthquake benchmarks were measured by total-station and the survey after the earthquake is a mixture of total station trilateration/triangulation and GPS campaigns. The horizontal positions were estimated by total station trilateration/triangulation. The data were provided with respect to the origin of TWD67 System (Transverse Mercator Two-degree Grid system) that we converted to WGS84 (World Geodetic System 1984) to be consistent with the rest of the data sets. The densely spaced distribution of the city-planning benchmarks provides an accurate location of constructions. Before the earthquake, a city map was created giving accurate position of human constructions. As many constructions were damaged during the earthquake, a second survey was run to remap the city after the event, the new map contains all the old city-planning benchmarks as well as Chi-Chi earthquake surface ruptures. The coseismic horizontal displacement data set was extracted from the comparison of these two maps. The horizontal displacements present an uncertainty of 1-2 cm against a mean displacement of 5 m in this area for the coseismic displacements.