esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, a novel method was introduced to facilitate the selection process of the generators in a ship power plant. It uses many parameters related to all aspects of the life-cycle cost of the engines and to the actual operating routine of the ship; however, computational time is significantly low. This way, the designers can have a complete idea of the cost involved in their selection and its return on investment.  
This method may be used for different operation scenarios simply by changing the numerical data. It can also be used for applications other than shipping, since industrial installations have similar needs. Even more exotic applications may also benefit from this, by calibrating the indicators used here, or simply adding new ones.  
An idea for future work could be performing a sensitivity analysis to determine how much each the examined factors affect the outcome.  
Another probably useful addition might be the consideration of the minimum running time, the minimum time between shutting down and starting up, the power-increase rate, and the starting cost.  
Finally, it might prove advantageous to expand this method by testing combinations of different gensets and possible exploitation of renewable energy onboard. This way, ships with shaft generators and electric propulsion, but also terrestrial power factories, may be examined.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**No Redundancy**} & \multicolumn{2}{c}{**Full Redundancy**} \\  & **w/o Reliability** & **w/ Reliability** & **w/o Reliability** & **w/ Reliability** \\ \cline{2-5}
**Selected power plant** & 1 \(\times\) 1500 kW & 3 \(\times\) 500 kW & 2 \(\times\) 1500 kW & 4 \(\times\) 750 kW \\ \cline{2-5}
**duty cycle 1 engine** & 100\% & 64\% & 100\% & 70\% \\
**duty cycle 2 engines** & - & 33\% & 0\% & 30\% \\
**duty cycle 3 engines** & - & 3\% & - & 0\% \\
**duty cycle 4 engines** & - & - & - & 0\% \\
**min optimal annual cost** & \(\epsilon\)572,000 & \(\epsilon\)655,000 & \(\epsilon\)666,000 & \(\epsilon\)804,000 \\
**(20 annuities with 1\% interest rate)** & \(\epsilon\)596,000 & \(\epsilon\)691,000 & \(\epsilon\)713,000 & \(\epsilon\)865,000 \\ \hline \end{tabular}
\end{table}
Table 8: Results summary.  
**Author Contributions:** Conceptualization, P.M., G.J.T., F.D.K. and J.M.P.; methodology, P.M. and G.J.T.; software, P.M. and G.J.T.; validation, P.M., G.J.T. and F.D.K.; formal analysis, P.M. and G.J.T.; investigation, P.M. and G.J.T.; resources, P.M., G.J.T., F.D.K. and J.M.P.; data curation, P.M. and G.J.T.; writing--original draft preparation, P.M.; writing--review and editing, P.M., G.J.T. and F.D.K.; supervision, J.M.P.; project administration, J.M.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Appendix A**  
The diesel generator data used are shown in detail below.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### Max-Min Threshold Scheduler
#### 3.1.1 Max-Min Scheduling  
For one RB, the achievable data transfer rate can be calculated by base station when the RB is assigned to a UE. In general, the base station will allocate an RB to the UE with the best channel quality while considering the issues of intercell resource allocation and the UE's QoS requirement. We assumed that there are N RBs within a time slot and \(M\) UEs connected to the base station. Based on UE's CQI, the base station constructs an NxM matrix \(A\) as shown in Figure 3. The max-min scheduling is then used to assign RBs to UEs to satisfy QoS of UEs as much as possible.  
The max-min scheduling consists of five steps:  
1. Find the maximum (max) of the CQI value in the matrix \(A\), and assume it is \(A(i,j)\). It assigns the \(i\)-th RB to the \(j\)-th UE so that the UE can receive the highest data rate with this RB.
2. When there are more than one maximum CQIs in the matrix \(A\), it selects the RB with minimum (min) impact to the other UEs in the same row. For example, if \(A(i1,j1)\) and \(A(i2,j2)\) are the same maximum in the matrix \(A\), then it calculates \(s(i1)=\sum_{j=1}^{M}A(i1,\ j)\) and \(s(i2)=\sum_{j=1}^{M}A(i2,\ j)\), respectively. It then assigns the \(i1\)-th RB to the \(j1\)-th UE first if \(s(i1)<s(i2)\).  
3. If \(s(i1)=s(i2)\), it sorts the vector \(A(i1,*)\) and \(A(i2,*)\) in descending order, compares the elements one by one until it finds the one with less value, say it is \(A^{s}(i1,j)\). It then assigns the \(i1\)-th RB to the \(j\)-th UE (where \(A^{s}(i1,*)\) is the sorted vector).
4. If all the elements are still the same, it randomly selects one, say \(A(i1,j1)\), and assigns the RB to a UE accordingly.
5. After allocating the \(i\)-th RB, the \(i\)-th row of the matrix \(A\) is cleared to 0 (indicating that the RB had been allocated).  
The max-min scheduling repeats the step from (1) to (5) until all of the UEs have satisfied QoS requirement or all of the RBs had been allocated.  
Once the RB allocation is completed, the max-min scheduling then evaluates whether the QoS requirements of the currently assigned UEs are satisfied. In LTE-A, the transport block (TB) is used to calculate the throughput of the downlink. A TB consists of several RBs, and all RBs must be transmitted using unified CQI encoding. Given the number of available RBs that can be assigned to the UE, it calculates the maximum data rate it can receive from the base station using following equation:  
\[TB=\ N_{symbol}*RB_{assigned}*CQI_{efficiency}*T_{slot}-CRC \tag{6}\]  
where \(N_{symbols}\) is the number of symbols per RB, \(RB_{assigned}\) is the number of RBs which the UE can be assigned, \(CQI_{efficiency}\) is the bits per RB, \(T_{slots}\) is the number of two time slots per millisecond, and \(CRC\) is the cyclic redundancy check in 24 bits.
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 3 Results and Discussion
### Interface Bond-Slip Model and Recommended Value for Stiffness  
As revealed by the experiments, the interface slip was very small before the interface bond stress reached the peak value; after reaching the peak value, the load suddenly and sharply decreased as the slip increased, and the failure mode of the specimen was sudden brittle failure. From the point of view of safety, the bearing capacity at the descending stage could be ignored. Except for the two groups of specimens, Z-C35-T and Z-C35-S, the initial stage (most areas) of the rising phase of the interface bond-slip curves could be represented by a linear regression line, that is, the ascending segment was linear growth. For the late stage of the ascending segment (close to the ultimate bond strength), although it was a curve growth, the proportion of linear growth was mostly above 90%. Thus, the ascending segment could be simplified by approximately assuming that the bond stress increased linearly to the ultimate bond strength. Given these reasons, by referring to References [21; 22] and considering that the critical bond stress of all specimens before interface slippage did not exceed 11.02% of the linear segment bond strength, this study posits that the interfacial bond-slip model of CGM-reinforced existing concrete structures adopts the form shown in Figure 10, and the equation is  
\[\tau= \begin{cases}\overline{K}s&(s_{u}\geq s\geq 0)\\ 0&(s\geq s_{u})\end{cases} , \tag{4}\]  
where \(\overline{K}\) is the average bonding stiffness of the interface, i.e., \(\overline{K}=\tau_{u}/s_{u}\); \(\tau\) and \(\tau_{u}\) are the bond stress corresponding to any slip value, \(s\), of the adhesive interface and linear segment bond strength, respectively; and \(s_{u}\) is the slip value corresponding to linear segment bond strength, \(\tau_{u}\). The values of \(\overline{K}\), \(\tau_{u}\), and \(s_{u}\) are listed in Table 6.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 3 Methodology
### Central Line Detection for Laser Line  
To obtain the central line of the laser line, the proposed algorithm removes outlying points [25] based on traditional line fitting method as shown in Figure 13, the relevant algorithms are as follows:  
* Depending on the pixel radius, a subset of data is selected from the feature data set: the pixel radius is used to determine the effective pixel, which refers to the maximum distance between the effective pixel and the straight line. At the beginning of line fitting, the algorithm randomly makes a straight line at two points, and selects pixels in the radius range of the line to form a data subset;
* Based on the data subset, a straight line is fitted and its Mean Square Distance (_MSD_) is calculated. The smaller the _MSD_ value is, the closer the pixel is to the fitted line with higher quality;
* A subset of the last fitted data is removed and the line is re-fitted based on the remaining data, and _MSD_ is calculated at the same time;
* Repeat the above procedure until all data in all feature data sets are fitted and then select the line with smallest _MSD_ as the candidate line;
* Optimize the candidate line and continuously remove the farthest outlying point, and re-fit the line based on the remaining point, and calculate the Line Fit Score (_LFS_): \[LFS=(1-\frac{MSD}{PR^{2}})\times 1000\] (6) where _PR_ is pixel radius. When the _LFS_ reaches the required minimum score, the current fitting line is returned.  
Therefore, the equations of the central lines of X and Y axis line of the cross laser can be obtained separately. The coordinate of the intersection point of the two center lines is the coordinate of the center point of the cross laser-pattern. Besides, with the slopes of the two center lines, the angles of X and Y axis laser lines of cross laser-pattern in camera image coordinate can be obtained, namely the attitude information of the cross laser can be determined.
## 1 Introduction
### Study Scope and Method  
This study presents a method of securing space for arranging various equipment for plant construction by using a laser scanner to collect point cloud data of the site environment and the installation objects. In this study, the focus was on searching free space and finding coordinates for arranging new equipment installation, and not on considering relocation of existing equipment. As such, the point cloud data on the equipment and the site environment were separately acquired and studied.  
In this process, the 3D grid method was used by referring to the space data utilization methods of other studies using 3D shape information. Two types of 3D shape information for space model and facilities model are outlined through the 3D grid method, and the overlap between the two data was checked by matching them.  
Two algorithms for matching the 3D shape information were created: one for the simplification of the target installation spaces and facilities using the 3D grid and the other for verifying the overlap of the two types of simplifying the shape information. Then the integrated matching algorithm was generated and is presented.
## 5 Results
### Experimental Setup  
All experiments were carried out in a water tank available at the Institute of Mechanics and Ocean Engineering, Hamburg University of Technology. The experimental setup and the performed experiments are shown in Figure 14. In Figure 14, the dashed lines depict the receiver unit trajectories and the stars the static positions. The conductivity of the water was measured and amounts to \(0.031\,\frac{\mathrm{S}}{\mathrm{m}}\). Four EM beacons, as introduced in Section 3.2, are deployed in the experimental tank as shown in Figure 14.  
The four beacons transmit EM signals at 433.90 MHz, 434.05 MHz, 434.20 MHz and 434.35 MHz. The receiver unit is mounted to a horizontal movable gantry unit with a workspace of 3 m by 1.6 m. This allows moving the receiver unit along pre-programmed trajectories and taking signal strength measurements with arbitrary high geometric resolution.  
During static measurements, the localization system is able to reach sampling frequencies of 20 Hz. For dynamic measurements, the sampling frequency is approximately 4.5 Hz. This is mainly due to the long response time of the gantry position encoder. It is worth mentioning that the sampling frequency is independent of the number of emitting beacons, since the algorithm executes an FFT for the relevant part of the power density spectrum, which includes all transceiving frequencies of the beacons.
## 1 Introduction
When EMD is used, a signal can be decomposed into a set of intrinsic mode functions (IMFs) for calculating the instantaneous frequencies through the HHT. In particular, the advantage of EMD over other signal decomposition methods (e.g., wavelet) is that EMD requires no signal base for signal decomposition; therefore, a signal can be adaptively decomposed into \(n\) empirical modes. The gain effect exists in each IMF component produced from the EMD of the ultrasound signals, and thus it may be eliminated using various IMFs for signal normalization. Note that the above assumption has been preliminarily validated by experiments on newly-formed and aged thrombin [13]. In the current stage, three fundamental issues need to be further clarified: (i) the proposed idea needs to be demonstrated theoretically; (ii) different system gains result in different signal-to-noise ratios (SNRs). The effects of SNR on the proposed method should be explored; and (iii) considering that the factors \(G\)\(h(t)\), and \(z(t)\) are essentially functions of ultrasound frequency, the frequency effect on the proposed method also needs to be investigated. To address the feasibility of using EMD as an adaptive strategy forestablishing a gain-independent index for describing tissue echogenicity, in the subsequent sections we review the principles of EMD and present an ultrasound signal model to theoretically demonstrate that the gain effect can be eliminated by signal normalization using the IMFs. We then define an echogenicity index based on using the IMFs to validate its performance in quantifying the image intensity of ultrasound B-scans under various system gains (i.e., signal-to-noise ratios [SNRs]) and explore its dependency of ultrasound frequency. Finally, we discuss the potential impacts of using the proposed method in future applications.
## 3 Proposed Scheme
### Proposed CAOR Method  
In light of the previous subsections, to improve the BER performance and alleviate the significant complexity, we joined the AOR iterative algorithm and the recursive characteristics of the Chebyshev polynomials to construct a two-stage receiver, as depicted in Figure 1, in which the second block has more apparently depicted the Chebyshev recursive procedure. It can speed up convergence with fewer iterations and produce better estimation results. Additionally, we call this proposed scheme Chebyshev-accelerated over-relaxation (CAOR) detection.  
The first stage of the receiver is an initial phase, which consists of an accelerated over-relaxation (AOR)-based estimator and is intended to yield a rough initial estimate of the relaxation factor \(\omega\), the acceleration parameter \(\gamma\), the spectral radius of \(G_{\gamma,\omega}\), and transmitted symbols. The second stage is a refined phase, the Chebyshev acceleration method is used for detection, and a more precise signal is produced through efficient iterative estimation. The procedure of the CAOR detection is shown in Algorithm 1.  
```
0: Receiver signal input: \(W_{MMSE}=H^{H}H+\sigma^{2}I_{N_{T}}\triangleq A\), and \(A=D+L+L^{H}\)
0: \(\mathbf{y}^{MF}=H^{H}\mathbf{y}\triangleq\mathbf{b}\) The first stage: (Initialize phase) \(G_{\gamma,\omega}=\left(D+\gamma L\right)^{-1}\!\!\left[(1-\omega)D-(\omega- \gamma)L-\omega L^{H}\right]\) \(\mathbf{d}=\omega(D+\gamma L)^{-1}\mathbf{b}\) \(\mathbf{set}\rho=\rho(G_{0,1})\), \(\rho(G_{\gamma,\omega})\) is the spectral radius of \(G_{\gamma,\omega}\) \(\mathbf{set}\mu_{0}=1\), \(\mu_{1}=\rho\), \(\tilde{\mathbf{x}}^{(0)}=\mathbf{x}^{(0)}=\mathbf{1}\) and \(k=1\) Compute \(\omega=\frac{1}{\sqrt{1-\mu_{1}^{2}}}\), \(\gamma=\frac{2}{1+\sqrt{1-\mu_{1}^{2}}}\) Compute \(\mathbf{x}^{(1)}=G_{\gamma,\omega}\mathbf{x}^{(0)}+\mathbf{d}\triangleq\tilde{ \mathbf{x}}^{(1)}\) The second stage: (Refined phase) While not converge do \(k=k+1\) \(\mu_{k}=\left(\frac{2}{\rho}\cdot\frac{1}{\mu_{k-1}}-\frac{1}{\mu_{k-2}} \right)^{-1}\) \(\tilde{\mathbf{x}}^{(k)}=\frac{2\mu_{k}}{\mu_{(k-1)}}\), \(\tilde{\mathbf{c}}_{\rho\omega}\hat{\mathbf{x}}^{(k-1)}-\frac{\mu_{k}}{\mu_{(k -2)}}\tilde{\mathbf{x}}^{(k-2)}+\frac{2\mu_{k}}{\mu_{(k-1)}\rho}\mathbf{d}\)  end for set\(\tilde{\mathbf{x}}=\tilde{\mathbf{x}}^{(k)}\)
```  
**Algorithm 1** Chebyshev-Accelerated Over-Relaxation
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 3 Methodology
### Decoupling of Dynamics
#### 3.1.4 Design of the Trajectory Tracking Controller  
Based on the inverse system, the control framework is built in Figure 4. The target motion trajectory transformed into the vehicle dynamic state references--that is, the longitudinal acceleration, the lateral acceleration, and the yaw acceleration. By following the target vehicle dynamic state, the inverse system exports the vehicle inputs and drives the vehicle tracking desired trajectory.  
Figure 3: One group of vehicle stochastic inputs.
## 1 Introduction
### Outline of the Article and Research Contributions  
This study is organized as follows. This section covers recent work related to multi-objective RL optimization and developments in pinion manufacturing. Section 2 formalizes the problem of multi-objective optimization in mathematical terms. Section 3 describes the Simufact simulation used to generate synthetic data for pinion manufacturing, along with the corresponding machine learning-based surrogate model. Section 4 presents the theoretical background and methodology for training the RL agent. Validation on two use cases, pinion manufacturing and open-source data, is presented in Section 5. Section 6 discusses considerations for integrating the proposed approach into production environments. Finally, Section 7 summarizes the main findings of the study.  
The key contributions of this research are as follows:  
* Algorithmic Development: A multi-objective RL framework specifically adapted for manufacturing control tasks, developed by extending the Multi-Objective Maximum a Posteriori Optimization (MO-MPO) algorithm.
* Industrial Validation: Validation of the proposed framework across two distinct industrial use cases, utilizing both real-world and synthetically generated datasets.
* Empirical Evaluation: Demonstration of effectiveness through improved Process Capability Index (Cp) values and Pareto front approximations, verified using a gradient-based half-space analysis technique.
* Production-Ready Implementation: Deployment of a production-ready system via containerized deployment with REST API integration, enabling seamless integration into computer-integrated manufacturing workflows.
## 2 Materials and Methods
### Eddy Covariance Data  
We used five selected flux towers to interpret the satellite-observed dynamic of vegetation under extreme drought. The original level 3 (AU-How, AU-Dry, AU-Stp) and level 6 (AU-ASM, AU-TTE) flux data provided by the OzFlux network (Available online: [[http://www.ozflux.org.au/](http://www.ozflux.org.au/)]([http://www.ozflux.org.au/](http://www.ozflux.org.au/)) (accessed on 15 July 2020)) were used to pre-process, including quality control assessment, removal of outliers, and gap-filling [(51)]. The R package, REddyProc, was implemented for Level 3 data to estimate daily mean GPP with hourly eddy covariance and meteorological data [(52)]. This tool used the gap-filling and flux partitioning algorithms to partition Level 3 data into GPP and field ecosystem respiration [(53)], conducted in open-source R scientific computation environment (Version 3.5.1). The estimated daily GPP were aggregated into monthly and 16-day GPP to match with satellite-based observations.
# Video Super-Resolution with Regional Focus for Recurrent Network
###### Abstract
In this recursive network, the regional focus block is designed to change the coupling relationship between the former and the latter layers in the cascading residual structure, and to give different degrees of attention to the regions with different reconstruction difficulties in the video frame. Meanwhile, we designed a feature aggregation structure that can send different features from shallow layer to deep layer extracted from each regional focus block in parallel to the last layer of the network for feature fusion, so as to increase the feature richness involved in video frame reconstruction. The innovation of this paper mainly includes the following two points:  
1. The regional focus block proposed in this paper changes the coupling relationship between adjacent layers in the cascading residual structure, and pays different degrees of attention to different texture regions in the video frame. The feature information extracted from the network of different layers can participate in the reconstruction of the video frame, and will not lead the optimization direction of the network because the video frame contains more simple areas. 2. The feature aggregation structure in the network sends the feature information extracted from different layers of networks from the shallow layer to the deep layer into the last layer of the network in a parallel manner, and carries out the feature fusion with the feature information extracted from the series structure in the network, which increases the feature information involved in the reconstruction of video frames and improves the quality of the reconstruction results. The above structure can effectively improve the reconstruction quality of the video frame while guaranteeing the network computing speed. As shown in Figure 1, it is a comparison of the results of our method and other different methods. From the figure, we can see that our method has achieved better results in both contour reconstruction and the detailed recovery of images. Finally, we carry out experiments in different test datasets, and compare them with the current mainstream video super-resolution work, the experimental results of which show that the reconstruction results obtained by our method are better than those obtained using other methods.