esa-sceva/satcom-chunk-collection huggingface dataset:
# Fracture Toughness of Hybrid Components with Selective Laser Melting 18 Ni300 Steel Parts
## 3 Results and Discussion
### Fracture Toughness  
Tables 3-5 present the fracture toughness K\({}_{\rm Q}\), the validity condition for K\({}_{\rm Q}\) = K\({}_{\rm lc}\) and J\({}_{\rm c}\), for entirely 3D-printed (SM) samples, and hybrid SS/SM and HS/SM samples, for the two different scan rates (200 and 400 mm/s) and the two thickness values (3 and 6 mm). The results show that the majority of tests lead to invalid fracture toughness tests, because the geometry of samples do not guarantee a plain strain state, leading to an excessive plastic deformation at the crack tip. However, K\({}_{\rm Q}\) increases at least 10% when thickness decreases from 6 to 3 mm and the increase of the scan speed from 200 to 400 mm/s reduces K\({}_{\rm Q}\) above 20%. Hybrid samples show a slightly lower toughness than fully 3D-printed specimens, reaching almost 10% for HS/SM specimens, which is caused by microstructural variations at interface. The values of fracture toughness obtained in this work for the samples SM, SS/SM and HS/SM 3D-printed at 200 mm/s scan speed rate and 3 mm of thickness, are similar to those K\({}_{\rm Q}\) values obtained for the steel H13 by [PERSON] et al. [3] in laser deposit welding Nd-YAG with 4 mm thick specimens (K\({}_{\rm Q}\) = 79.08 \(\pm\) 31.76 MPa.m\({}^{1/2}\)). The effects of thickness, substrate material and scan speed on the J\({}_{\rm c}\) parameter is similar to that observed for K\({}_{\rm Q}\), but with more pronounced trends. For the scan speed of 200 mm/s, J\({}_{\rm c}\) on 3 mm thickness samples decreases about 15% and 30% for SS/SM and HS/SM samples, respectively, when compared with entirely 3D-printed specimens. The same comparison on J\({}_{\rm c}\) for 6 mm thickness leads to toughness reductions of about 7% and 45% for SS/SM and HS/SM samples, respectively. The increasing of the scan speed from 200 to 400 mm/s reduces about 50% the J\({}_{\rm c}\) values for both 3D-printed and hybrid samples. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Valid** & **K\({}_{\rm Q}\)** & **Average K\({}_{\rm Q}\)** & **J\({}_{\rm c}\)** & **Average J\({}_{\rm c}\)** & **Crack to Fracture, a\({}_{\rm c}\)** \\
**Reference** & **K\({}_{\rm Q}\) = K\({}_{\rm lc}\)** & **(MPa.m\({}^{1/2}\))** & **(MPa.m\({}^{1/2}\))** & **J\({}_{\rm c}\)** & **(N/mm)** & **(N/mm)** & **(mm)** \\ \hline
2 SS/SM01\_3 & No & 83.22 & & 48.9 & & 32.03 \\
2 SS/SM02\_3 & No & 82.46 & & 48.5 & 55.82 \(\pm\) & 31.98 \\
2 SS/SM03\_3 & No & 85.15 & & 62.7 & 7.13 & 31.45 \\
2 SS/SM04\_3 & No & 84.75 & & 63.21 & & 31.67 \\ \hline
2 SS/SM01\_6 & No & 67.71 & & 41.14 & & 30.58 \\
2 SS/SM03\_6 & No & 71.24 & 70.97 \(\pm\) 2.55 & 37.59 & 39.02 \(\pm\) & 29.91 \\
2 SS/SM04\_6 & No & 73.95 & & 38.33 & & 31.06 \\ \hline
4 SS/SM01\_6 & No & 54.76 & & 17.08 & & 28.98 \\
4 SS/SM02\_6 & No & 57.84 & & 19.53 & & 29.05 \\
4 SS/SM03\_6 & No & 59.59 & & 23.64 & & 29.86 \\
4 SS/SM04\_6 & No & 53.69 & & 18.02 & & 30.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fracture toughness tests results for SS/SM specimens.
## 5 Experimental Study
### Comparative Test on Constant Speed Loading  
The speed curves of the motor control system under the constant speed loading based on the traditional PI control and the fuzzy PI control are provided in Figures 23-26. The test target speed is 800 r/min, and the initial load is 1.5 N\(\cdot\)m. When the electric motor starts, the overshoot of the PI control and fuzzy PI control are 38 r/min and 19 r/min, respectively. Moreover, the times to steady state are 600 ms and 420 ms, respectively. Under the traditional PI control, when the load is added to 3.3 N\(\cdot\)m at 3 s, the speed jittered, and the fluctuation range is 17 r/min. Then it enters the steady state after 300 ms. Torque is released to 1.5 N\(\cdot\)m at 11 s, and the speed jittered with a fluctuation range of 12 r/min. After 140 ms, it enters the steady state again. While under the fuzzy PI control, the load is suddenly added to 3.3 N\(\cdot\)m at 3.4 s, and the speed fluctuation range is 12 r/min. Then it enters the steady state after 120 ms. Torque is released to 1.5 N\(\cdot\)m at 10 s, and the speed jittered with a fluctuation range of 8 r/min. After 80 ms, it enters the steady state again. It can be seen that during the loading process, when fuzzy PI control is adopted, the speed jitter is smaller and the response is faster. Therefore, the system adopting fuzzy PI control has stronger anti-load disturbance ability, better robustness, and anti-interference performance.  
Figure 23: Speed change curve of traditional PI under constant speed loading.  
Figure 22: Speed curve under no-load variable speed with a ramp signal input.  
The \(i_{d}\) and \(i_{q}\) curves of the motor control system under the constant speed loading based on the traditional PI control and the fuzzy PI control are provided in Figures 27 and 28, respectively. It can be seen that after loading, the current of quadrature axis and direct axis are smaller when the fuzzy PI control is adopted. After entering the steady state, the current fluctuation is smaller, and the current stability is also better.  
Figure 26: Partial amplification curve of fuzzy PI under constant speed loading. (**a**) Loaded; (**b**) unloaded.  
Figure 24: Partial amplification curve of traditional PI under constant speed loading. (**a**) Loaded; (**b**) unloaded.  
Figure 25: Speed change curve of fuzzy PI under constant speed loading.  
It can be seen from the test results that the fuzzy PI control can make the electric motor control system not only has a faster response performance, but also has a smaller overshoot. In loading and unloading conditions, when fuzzy PI control is used, the electric motor speed fluctuation and the time to the steady state are significantly smaller.
# Path Planning Based on NURBS for Hyper-Redundant Manipulator Used in Narrow Space
###### Abstract  
Hyper-redundant manipulators with multiple degrees of freedom have special application prospects in narrow spaces, such as detection in small spaces in aerospace, rescue on-site disaster relief, etc. In order to solve the problems of complex obstacle avoidance planning and inverse solution selection of a hyper-redundant robot in a narrow space, a cubic B-spline curve based on collision-free trajectory using environmental edge information is planned. Firstly, a hyper-redundant robot composed of four pairs of double UCR (Universal-Cylindrical-Revolute) parallel mechanisms (2R1T, 2 Rotational DOFs and 1 Translation DOF) in series to realize flexible obstacle avoidance motion in narrow space is designed. The trajectory point envelope of a single UCR and the workspace of a single pair of UCR in Cartesian space based on the motion constraint boundaries of each joint are obtained. Then, the constraint control points according to the edge information of the obstacle are obtained, and the obstacle avoidance trajectory in the constrained space is planned by combining the A* algorithm and cubic B-spline algorithm. Finally, a variety of test scenarios are built to verify the obstacle avoidance planning algorithm. The results show that the proposed algorithm reduces the computational complexity of the obstacle avoidance process and enables the robot to complete flexible obstacle avoidance movement in the complex narrow space.  
hyper-redundant manipulator; narrow space; obstacle avoidance planning; UCR parallel mechanism; cubic B-spline curve +
Footnote †: journal: Applied sciences
# Data-Driven Convolutional Model for Digital Color Image Demosaicing
## 3 Methods
### Chrominance Reconstruction Network  
The reconstruction of the Chrominance vector has to be dealt with a different set of assumptions in mind. Specifically, as the Chrominance vector has mostly low-frequency components, blurred reconstructed images can still be valid estimations as they are compensated when combined with the Luminance channel in the final merging step. Therefore, the mean squared error can be used as an optimization function. Additionally, Chrominance channels have high inter-channel correlation, therefore it is possible that there exists an underlying nonlinear projection that is able to fully encode the correlations between channels. Based on these assumptions, we propose the Chrominance reconstruction model to be designed as a multi-layer encoder-decoder network.  
The Chrominance Reconstruction Network is described in Figure 8. Similarly to the Luminance Reconstruction Networks, it receives a 15 \(\times\) 15 \(\times\) 3 patch and outputs a 15 \(\times\) 15 \(\times\) 3 patch. Internally, data is reduced to a lower-dimensional network by multiple layers of an Encoder Network and reconstructed by a Decoder Network to recover a full 15 \(\times\) 15 \(\times\) 3 image. We primarily used ReLU activation functions in hidden layers and a sigmoidal activation function for the output layer.
## 4 Numerical Analysis and Results  
Firstly, to evaluate the best network selection, we will consider the Auckland public transport network to choose among three network alternatives. The reason for choosing Auckland as a case study is that Auckland has a vision to be the world's most liveable city with smart citizens and a smart infrastructure. Auckland is a city with innovative technologies to improve the quality of life. Auckland, as a smart city, can think of smart and innovative devices to make decisions based on real-time data analysis. Seven New Zealand projects have been short-listed in IDC's Asia Pacific Smart Cities Awards [40]. Considering all the facts, Auckland Transport was a good example to validate our proposed system. Normally, the urban area is covered by heterogeneous wireless networks, including WLAN, UMTS, and Public Vehicles/buses. All these networks bear different characteristics, as described above. For vehicular network selection, the vehicle must be in the range of the network to consider it a selection option, based upon the user's preference. For simplification, we make the following assumptions:  
**Assumption 1**.: _We consider three types of networks: WLAN, UMTS, and vehicular networks. For further information related to the vehicular network, only scheduled public transport vehicles are involved. WLAN and UMTS networks covers the whole region, while VANET covers partially, only within a specified range of bus stops. Additionally, vehicle-to-vehicle communication is not considered;_  
**Assumption 2**.: _For any of the network selections, there is a predefined bandwidth and range-defined network selection is only possible if those conditions are met. Every user has different preferences based on their requirements. We will use the AHP method to assess each user's requirements and preferences._
## 3 Analysis Model for Cracking and Flexural Behavior of HPFRCC Beams
### Determination of Segment Length  
In order to calculate crack width from tensile strain in a segment, the length of the segment, \(s\), should be carefully selected. [PERSON] _et al._[30] reported that the best agreement with the results from a numerical model was obtained when the segment length of conventional concrete beams was one half the height of the beam (\(s=0.5\ h\)). In addition, the same ratio for the segment length was successfully applied to fiber-reinforced concrete [16]. Different from conventional normal or fiber-reinforced concrete, which usually shows a single crack in a small specimen, several cracks occurred in a flexural specimen with HPFRCC. In this study, it is assumed that each segment has one crack so as to simulate every crack in the specimen individually. The length of a segment is assumed to be the average crack spacing at the failure, and this spacing was measured in the experiment. For the specimens of \(100\ \text{mm}\times 100\ \text{mm}\times 400\ \text{mm}\), the length of the segment has been assumed to be 14.29 mm, since the number of cracks was about 7 (10, 5, and 6 cracks for the 3 specimens).
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 3 Reliability Model
### Reliability Framework  
It may be summed up as the likelihood that two vehicles will be able to continuously communicate for a specified amount of time. The link between any two vehicles at given time has an availability interval of \(T_{p}\); then, following relation is used to explain the term reliability:  
\[r(l)=P\{Availability\ until\ (t\ +\ T_{p})\ |\ available\ at\ t\} \tag{4}\]The calculations of the connection's reliability requires the vehicles' speed to be in standard distribution [31]. In this case, \(G(v)\) would be the probability distribution and \(g(v)\) would be the probability density function:  
\[g(v)=\frac{1}{\sigma\sqrt{2\pi}}e^{\frac{-(v-v)^{2}}{2\alpha^{2}}} \tag{5}\]  
\[G(v\leq V_{0})=\frac{1}{\sigma\sqrt{2\pi}}\int_{0}^{V_{0}}e^{-\frac{(v-v)^{2} }{2\alpha^{2}}}dv \tag{6}\]  
where \(\mu\) and \(\sigma_{2}\) depicts the average and variance of velocity, respectively, [32]. The relative velocity \(\Delta v\) and the time duration \(T\) is utilized to calculate the distance. Because the two arbitrary variables \(v_{1}\) and \(v_{2}\) are normally distributed, the difference between those two will be normally distributed as well. The greatest distance upon which two vehicles may still communicate is \(2H\), while each vehicle's communication range is given as \(H\). The following equation may be used to compute the probability density function for the communication duration \(T\):  
\[f(T)=\frac{4H}{\sigma_{\Delta v}\sqrt{2\pi}\times T^{2}}e^{\frac{-(\frac{2H}{ 2\alpha}-v_{\Delta v})^{2}}{2\sigma_{\Delta v}^{2}}}forT\geq 0 \tag{7}\]  
where \(\mu_{\Delta v}\) and \(\sigma_{\Delta v}^{2}\) depict the average and variance of relative velocity, respectively. Each vehicle should be fitted with a GPS device that can provide the speed, direction, and position information. Equation (8) provides a mathematical representation of the specific link's continuity between two vehicles:  
\[T_{p}=\frac{H-L_{ij}}{v_{ij}}=\frac{H-Euclidean_{dist}}{\mid v_{i}-v_{j}\mid} \tag{8}\]  
Euclidean distance formula is used to calculate the distance between vehicle \(i\) and \(j\). The function \(f(T)\) can be integrated from time \(t\) to \(t+T_{p}\) to calculate the possibility of a connection, reachable at \(t\) for a duration of \(T_{p}\). The following Equation (9) can be used to determine the reliability of a link at a specific point in time:  
\[r_{t}(l)=\begin{cases}\int_{t}^{t+T_{p}}f(T)dt&\text{if }T_{p}>0\\ 0&\text{otherwise}\end{cases} \tag{9}\]  
The Gauss error function can be used to derive the integral in Equation (9) [33],  
\[r_{t}(l)=Erf\frac{(\frac{2H}{l}-\mu_{\Delta v})}{\sigma_{\Delta v}\sqrt{2}}- Erf\frac{(\frac{2H}{l+T_{p}}-\mu_{\Delta v})}{\sigma_{\Delta v}\sqrt{2}}\qquad when \quad T_{p}>0 \tag{10}\]  
where \(Erf\) stands for;  
\[Erf(T)=\frac{2}{\sqrt{\pi}}\int_{0}^{T}e^{-t^{2}}dt,-\infty<T<+\infty \tag{11}\]
## 3 Spectrum Management Issues
### A. Carrier Aggregation
#### 3.1.2 Energy Efficiency  
The authors in [95] have highly focused on the energy efficiency of a system alongside capacity and coverage fairness for users. This study has analyzed the outage capacity of a system and proposed a relaying scheme on energy-efficient methods to maximize the capacity, coverage, and fairness of CA-based networks. This relaying method also enhances low-frequency CCs for greater capacity fairness among all users in a cell. Conversely, an advanced and efficient algorithm is still in demand for user mobility. Another energy-efficient multi-stream Carrier Aggregation for HetNet is proposed in [96]. It utilizes a Bisection Method for Energy Minimization (BIMEM) algorithm to minimize energy consumption and capacity maximization by analyzing the problem as multi-objective optimization. The results have proved that the trade-off curve between energy minimization and capacity maximization delivers a large amount of energy savings by reducing the network capacity.
## 3 Results and Discussion  
### Model Validation  
The generator rotor system analyzed in this paper is shown in Figure 1. This generator rotor system is a simplified model from a 600-MW supercritical steam turbine generator. The generator rotor has a length of \(L=13.84\) m. The Young's modulus, density, and Poisson's ratio are \(E=210\) GPa, \(\rho=7800\) kg/m\({}^{3}\), and \(\
u=0.3\) respectively. The generator rotor is supported by two isotropic bearings with a direct stiffness coefficient \(k=5\times 10^{8}\) N/m and a direct damping coefficient \(c=200\) Ns/m. The shaft with the largest radius is slotted and asymmetric, and the rest of the shafts are symmetric. The basic circle of the slotted shaft section has a radius of \(r=0.7\) m. The horizontal and vertical second moments of area of the asymmetric shaft section are \(I_{xx}=0.172\) m\({}^{4}\) and \(I_{yy}=0.153\) m\({}^{4}\).  
To validate the accuracy and efficiency of the ROM technique, the ROM and full FEM are used to solve the dynamics of the generator rotor system. Figure 2 shows the whirl speed of the generator rotor system in the rotating frame versus the rotational speed. The full FEM contains 38,408 DOFs. The ROM contains 102 DOFs, which are composed 100 modal coordinates and two physical coordinates for obtaining the gravity response. The number of DOFs is 0.27% of the full FEM model, which means that the DOFs are reduced effectively. As shown in Figure 2, the results of the ROM and full FEM are in good agreement with each other. At the rotational speed of about 800 rpm, 1630 rpm, and 2830 rpm, there are three speed ranges within which the whirl speeds are equal to zero. These speed ranges are corresponding to the first three critical speeds, which is an interesting phenomenon of whirl speed analysis in the rotational speed.  
The ROM and full FEM are also used to evaluate the gravity response of the generator rotor system in the fixed frame. The gravity response amplitude of left disk node is investigated. The results obtained by the two models are shown in Figure 3a, and the relative errors of the ROM are shown in Figure 3b. The relative error is calculated by dividing the amplitude of ROM by that of the full FEM. A total of 100 normal modes are retained in the ROM. As shown in Figure 3, the results of the ROM are in good agreement with these of full FEM. As a whole, the relative errors of the ROM are all lower than 0.1%, which validates the accuracy of the ROM. Thus, in the following analysis, the ROM with 100 normal modes retained is used to maintain the balance between calculation speed and accuracy. The resonance speeds of the gravity response are 399 rpm, 833 rpm, and 1413 rpm, respectively, which are corresponding to half of the first three critical speeds. This resonance peak of double frequency vibration is caused by the unequal principal second moments of area, which makes the vertical deflection change twice in every rotation.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 5 Energy-Saving Features and AI-Assisted Radio Resource Management
### Energy-Saving Features  
Of the many ways to save energy, in addition to sleep modes, this work focus on two resource-adapting energy-saving features that operate on different time scales, i.e., features having different timing constraints for their activation. They are:  
1. **Carrier adaptation:** Activating this energy-saving feature involves turning on/off capacity layers or bands at each of the sites based on traffic demand, user distribution, and other network conditions. This process takes a longer time than antenna or bandwidth adaptation since it requires re-associating connected users to the same/different site and (or) band, which could take a variable time depending on the network scenario and conditions. Additionally, this process must be executed in a way that does not degrade the quality of service when there is an increase in traffic demand.
2. **Antenna and bandwidth adaptation:** Antenna adaptation involves turning on/off the number of active antennas at the base station, and the bandwidth adaptation results in varying the instantaneous bandwidth allocated to each user dynamically.  
Figure 12: Simplistic power consumption variation.  
Figure 13: Components of a logical RAN instance.  
These adaptations operate on a much shorter time scale compared to carrier adaptation. Both antenna and bandwidth adaptation lead to a change in the instantaneous throughput experienced by the user as seen in Equation (2) and described in Section 4. The change in the user throughput impacts the base station's activity factor (see Equation (8)) and its energy consumption (see Equation (17)).  
We implemented these energy-saving features as a hierarchy of reinforcement learning agents described in Section 5.3.
## 3 Single-Objective Parameter Tuning
### The Tuning Algorithm
#### 3.1.1 Problem Statement  
The control parameter configuration problem can be stated as follows:  
* \(\Theta\), a possibly infinite set of \(d\)-dimensional parameter configurations;
* \(I\), a possibly infinite set of problem instances;
* \(O\), a set of performance objectives;
* \(c(\theta)=c(\theta,i,o)\), a random variable that represents the performance of the algorithm given a configuration \(\theta\in\Theta\) on problem instance \(i\in I\) based on performance objective \(o\in O\);
* \(\mathcal{C}(\theta)=E[c(i|\theta,o)]\), the expectation of the performance of parameter configuration \(\theta\in\Theta\) over all instances \(i\in I\) given a performance objective \(o\in O\).  
The goal of a tuning algorithm is then to find the optimal configuration,  
\[\tilde{\theta}=\arg\min\limits_{\theta\in\Theta}\mathcal{C}(\theta) \tag{1}\]
## 4 Implementation of Smart City Services
### Cross-Domain Applications  
To showcase the potential of the DT solution for the efficient management of cross-domain city applications, the following test concepts have been implemented:  
* Lighting control depending on parking occupation: streetlight dimming level can be controlled based on parking occupancy to optimize energy usage and enhance safety in urban environments. When the IoT sensors deployed in parking lots detect occupancy, nearby streetlights may automatically adjust to higher illumination levels to enhance visibility and security for pedestrians and drivers. Conversely, when the parking lot is no longer occupied, streetlights may dim to conserve energy while maintaining safety standards. This cross-domain application (street lighting and mobility services) not only reduces energy consumption and carbon emissions but also ensures that lighting levels are tailored to actual usage, contributing to more efficient and sustainable urban infrastructure.
* Watering management triggered by presence detection: green areas irrigation systems can make use of data coming from the motion detection sensors equipped at streetlights near green spaces. When presence is detected, the irrigation system may temporarily pause or reduce water flow to prevent bothering passers-by and prevent water waste. On the contrary, when no presence is detected, the system resumes or adjusts irrigation schedules to ensure adequate moisture levels for plant growth. This cross-domain application (watering and street lighting services) not only conserves water but also minimizes runoff and contributes to the creation of greener and friendlier urban spaces.
* Garbage container status signaled by streetlights blinking: streetlights can serve as effective indicators for filling levels or fire risk of garbage container, enhancing waste management efficiency in urban areas. When a garbage container reaches a predefined filling threshold or detects an abnormal temperature value, it sends a signal triggering nearby streetlights to start blinking, signaling to waste collection teams the need for emptying to fire risk situation. This cross-domain application (waste management and street lighting services) promotes timely waste removal, preventing overflow and littering, and improving overall cleanliness and hygiene in the city.