esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Case Study  
A multi-period version of the PG&E 33-bus radial distribution network [34] has been used to test the model proposed. The sizing and location of the DERs have been separately obtained using the algorithm presented in our previous work [13], considering a connected-grid system because an autonomous network, i.e., the energy, is provided 100% by DERs; it is not a proper case of study to observe the capability of the ADN to form sub-systems when the grid fails. Therefore, using [13], the location and sizing have been set as shown in Figure 2.  
In order to test the mathematical optimization model, four different 72-hour scenarios corresponding to each season of the year are taken into account to study the different topologies formed by the ADN when the system is disconnected from the utility grid and the weather conditions are not capable of supplying 100% of the power consumption. The weather resources are taken from historical data (a year and a time-step of 1[h]) of the Lugo, Galicia region in the north of Spain 2018; Ref. [35] for the wind speed, and [36] for the temperature and irradiance. The power generation data have been obtained using the modeling presented in [37] for the wind turbines, and the formulation used in [38] for PV panels. Thus, the energy demand and the power generation for the periods of study are presented in Figure 3. Every scenario of 72 h has been chosen from the 8760 h available in the data set, with the aim to represent periods of 72 h with a high difference between both the power consumption and generation. Moreover, every curve has been normalized to respect the order of magnitude and comment on the power capability available. For example, the fourth period/scenario possesses a high-level wind resource, which means that the wind turbine will be able to produce energy at its maximum capacity. On the other hand, there are many consecutive hours that show a very low power capability for PV panels and wind turbines, which will force load curtailment and the partitioning of the ADN into sub-systems. The DSR parameter is set at 30% for every load at every time, i.e., every bus at every hour has the chance to reduce its load by 30%.  
The mathematical model has been programmed in Python using Cplex as the main solver, and it has been run on an Intel Core i7-10510U CPU @ 1.80 GHz 2.30 GHz with 16 GB memory and operative system Windows, 64 bit.  
Figure 2: Capacity and location of DERs in the PG&E 33-bus radial distribution network.  
It should be noted that the goal of the case study is; (1) to size the DSR effect on the DN topology scheduling, and (2) to show the _type_ of data provided by the model and how this information can be useful in terms of operational planning for DSO. It is important to mention this factor because the results depend exclusively on the DERs capacity, electricity demand, season, geographic location, weather conditions, and the DSR considered.
###### Abstract  
Rare-earth permanent magnets, such as Nd\({}_{2}\)Fe\({}_{14}\)B, have been widely used in electric vehicle and wind turbine motors due to their high anisotropy field (\(H_{\mathrm{a}}\)), saturation magnetization (\(M_{\mathrm{s}}\)) and coercivity (\(H_{\mathrm{c}}\)). Cerium (Ce) has gained attention as a potential alternative to neodymium (Nd) due to its high abundance and low cost. The relatively poor intrinsic magnetic properties of Ce magnets, however, remain a significant challenge for their industrial applications. In this study, the synthesis of Ce-based \(RE_{2}\)Fe\({}_{14}\)B (2-14-1) phases was achieved by a modified reduction-diffusion (R-D) process using \(RE\)FeO\({}_{3}\) (\(RE\) = Ce, Nd) as a precursor. The precursor was prepared by a solid-state reaction with CeO\({}_{2}\), Nd\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\) and Fe powders, which is a much more suitable process for mass production and cost-effectiveness. Optimal composition and heat treatment conditions enabled the formation of single-phase Ce-based 2-14-1 particles. The as-synthesized single-phase Ce\({}_{2}\)Fe\({}_{14}\)B particles exhibited an \(M_{\mathrm{s}}\) value of \(\sim\)120 emu/g and an intrinsic coercivity (\(H_{\mathrm{c}}\)) value of \(\sim\)85 Oe, which can be attributed to the large particle size as observed by FE-SEM.  
Ce\({}_{2}\)Fe\({}_{14}\)B; reduction-diffusion process; solid-state reaction; 2-14-1 phase; rare-earth magnet R.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 3 Modeling Framework
### Diagonal Strict Concavity
#### 3.3.2 Utility Maximization Interpretation  
The well-known approach for an MPTCP-based dynamic system is to associate a utility function \(U_{i}(\lambda_{i})\) with each user \(i\) and interpret the sources and network to be executing distributed algorithms to maximize the aggregate utility of the users. The primary idea is to connect the equilibrium points of the dynamical systems with the solution of some optimization problem. Therefore, the nonlinear dynamical system can be viewed as the one that solves the utility maximization problem in a distributed manner. For example, \((\lambda_{i}^{*},p_{i}^{*})\) is an equilibrium of the dynamical system of MPTCP users if and only if \(\lambda_{i}^{*}\) is optimal for  
\[\max\sum_{n}^{N}\sum_{i\,\in\,\mathcal{K}}U_{i}(\lambda_{i})\text{ s.t. }y_{i}\leq B_{i} \tag{16}\]  
where \(p_{i}^{*}\) is the Lagrange multiplier or pseudo-price associated with the link at the optimal \(\lambda_{i}^{*}\). Here, \(y_{i}\leq B_{i}\) means that the aggregate traffic (\(y_{i}\)) at each link is less than the capacity \(B_{i}\) of the link.  
In fact, all of the existing TCP algorithms proposed in the literature have concave utility functions implying a unique stable equilibrium [23]. Furthermore, we conclude that, for our MPTCP, given \(p_{i}\), we have a unique \(\lambda_{i}\) given by (see Equation (6))  
\[\lambda=U_{i}^{\prime-1}(p_{i}). \tag{17}\]
# Analysis of the Water Circulation Structure in the Paldang Reservoir, South Korea
## 2 Materials and Methods  
### Study Area  
The Han River, South Korea, is located in the central part of the Korean Peninsula within the area range of 36\({}^{\circ}\)30\({}^{\prime}\)-38\({}^{\circ}\)55\({}^{\prime}\) N and 126\({}^{\circ}\)24\({}^{\prime}\)-129\({}^{\circ}\)02\({}^{\prime}\) E. The river basin has an area of 34,674.0 km\({}^{2}\)and it accounts for approximately 35% of the nation's total land area of 99,313.5 km\({}^{2}\). The river length is 459.3 km, and the mean basin width is 75.5 km [11]. The Han River ranks as the largest river in South Korea. The Paldang Reservoir in the Han River system plays a vital role as a primary water supply source, and this river-type lake is characterized by a short mean annual residence time of 11.5 days (2014), which means that rainfall and water pollutants flowing into the basin can readily affect the water quality [11]. Furthermore, the influent water in the upper reaches of the Paldang Reservoir, including at upstream dams and weirs, undergoes three-dimensional mixing based on the topographic features of the lake. The Paldang Reservoir is a water body where the hydraulic and water quality characteristics vary spatially and temporally depending on the variations in inflow rates and the physicochemical properties of influent water. In this study, field surveys and analyses were conducted on the Paldang Reservoir where the South Han River and the North Han River meet at Dumulmeori and are joined by another river, the KyeonganCheon, before reaching the PDD (Figure 1).  
Figure 1: Location of monitoring sites in the Paldang Reservoir.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
###### Abstract  
To address the problem of unclear images affected by occlusion from fog, we propose an improved Retinex image enhancement algorithm based on the Gaussian pyramid transformation. Our algorithm features bilateral filtering as a replacement for the Gaussian function used in the original Retinex algorithm. Operation of the technique is as follows. To begin, we deduced the mathematical model for an improved bilateral filtering function based on the spatial domain kernel function and the pixel difference parameter. The input RGB image was subsequently converted into the Hue Saturation Intensity (HSI) color space, where the reflection component of the intensity channel was extracted to obtain an image whose edges were retained and are not affected by changes in brightness. Following reconversion to the RGB color space, color images of this reflection component were obtained at different resolutions using Gaussian pyramid down-sampling. Each of these images was then processed using the improved Retinex algorithm to improve the contrast of the final image, which was reconstructed using the Laplace algorithm. Results from experiments show that the proposed algorithm can enhance image contrast effectively, and the color of the processed image is in line with what would be perceived by a human observer.  
bilateral filtering; Gaussian down-sampling; luminance; enhancement; Retinex +
Footnote †: journal:
# Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
## 5 Control System Verification
### Verification Simulation  
After the integration of the whole control system into the FVM simulation setup as FMU, we conducted several simulations to verify the RL agent together with the control system. Additionally, the SH/GH benchmark controller was simulated as a reference. To verify the RL agent, several time domain signals were evaluated and checked for plausibility. Since we were mainly interested in the performance of the RL agent on the real vehicle, the verification simulations conducted on the FVM delivered only qualitative results and prepared for the real-world tests.  
Figure 11a shows the rendering of the simulated vehicle and, in Figure 11b, the road height profile for an ISO 8608 [19] type B road is displayed. The feedback of the controller and the vehicle response of the FVM for the excitation with the selected type B road are depicted in Figure 12. The depicted signals are the dynamic wheel load, the body acceleration, and the damper currents calculated by the RL agent or the SH/GH, respectively. The depicted signals are all exemplary for the front left side of the vehicle. We selected the ISO 8608 type B road as excitation for the depicted verification simulation, as ISO 8608 road types A and B are the most common road types according to [45]. Since road type A is very smooth, we chose road type B as representative but still wavy enough to excite the controller. In the simulation, the vehicle was initialized with a velocity of 95 km/h, which is below the recommended maximum velocity for this road type, as analyzed in [45].  
Figure 12 reveals that the dynamic wheel load as well as the chassis acceleration were similar for both controller types. Additionally, it can be seen that the commanded signal calculated by the RL policy depicted in blue on the bottom subplot was reasonably smooth. This is an important check, as ANN-based policies obtained from RL sometimes tend to be very noisy. In contrast to the SH/GH controller, the RL policy made fewer interventions with high currents and did not induce big current jumps. The SH/GH controller relied on the direction of the damper velocity with respect to both the wheel velocity and chassis velocity. Therefore, it was prone to command current jumps in case one of the velocities changed direction.  
Additional simulations verified the integrity of the whole control system together with the embedded RL agent. The application of the RL agent, which was trained on QVMs, in the FVM did not show concerning behavior. Therefore, we concluded that the control system was sufficient to be tested on the vehicle in real-world tests.  
Figure 11: **(a)** Rendering of the AFM’s full-vehicle model simulation setup (adapted from [20]) and (b) part of the ISO 8608 type B road height profile used as excitation for verification.
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 4 Experimental Results  
To evaluate the effectiveness of our method, we perform several experiments on a set of grayscale images. Eight images of size 512 \(\times\) 512, namely, Lena, Jet, Baboon, Tiffany, Sailboat, Splash, Peppers, and House are used as test images for the experiments, as shown in Figure 3. These test images can be obtained from the USC-SIPI image database [33]. Comparisons of image quality and detectability between prior works [25; 26; 27] and the proposed method are also shown in this section. In the experiments, the peak signal-to-noise ratio (PSNR) is employed to measure the marked image quality:  
\[\text{PSNR}=10\times\text{log}_{10}\frac{255^{2}}{\text{MSE}} \tag{6}\]  
where MSE is the mean square error of the marked image and the original image. Equation (6) shows that the smaller the MSE is, the larger is the PSNR. The structural similarity (SSIM) metric [34] is also employed to measure the similarity between the marked and original images. The SSIM is calculated by:  
\[\text{SSIM}(x,y)=\frac{(2\mu_{x}\,\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x} ^{2}+\mu_{y}^{2}+C_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{7}\]  
where \(x\) and \(y\) represent the original and marked images. \(\mu_{x}\), \(\mu_{y}\) and \(\sigma_{x}\), \(\sigma_{y}\) are the mean value and standard deviation of \(x\), \(y\), respectively. \(\sigma_{xy}\) is the covariance of \(x\) and \(y\), and \(C_{1}\) and \(C_{2}\) are constants. The value of SSIM is within the range [0, 1], and the larger the SSIM is, the higher the visual quality of the marked image is.
# Safety Design of Rotary Drilling Rig Mast Based on Multi-Condition Analysis
## 2 Classification of Working Conditions and Structural Mechanics Model
### Load-Bearing Capacity Analysis
#### 2.3.2 Stiffness Analysis  
In engineering, the slenderness ratio is commonly used to check stiffness, as shown in Equation (8):  
\[\lambda=l_{0}/r\leq[\lambda] \tag{8}\]  
In the equation, \(\lambda\) is the slenderness ratio of the member. \(l_{0}\) represents the effective length, \(l_{0}=\mu_{1}l\). \(r\) denotes the minimum radius of gyration of the gross cross-section, \(r=\sqrt{I/A}\). \([\lambda]\) is the allowable slenderness ratio.
# Interference Monitoring from Low Earth Orbit: The OPS-SAT Experiment +
## 4 Single Satellite Two-Step Geolocation of the On-Ground Emitter
In this case, the measurement model can be expressed as  
\[\text{FDOA}_{i,j}^{CAF}(t_{k})=-\frac{1}{\lambda}\left(\tilde{\mathbf{r}}_{i}^{T}( t_{k})\mathbf{v}_{i}(t_{k})-\tilde{\mathbf{r}}_{j}^{T}(t_{j})\mathbf{v}_{j}(t_{j})\right)+ \delta B+\
u, \tag{2}\]  
In (3), \(\delta B\) incorporates receiver clock offset rate and other residual stationary biases. Instead, \(\
u\) represent the measurement noise error introduced by noisy signals involved in the CAF estimation. The measurement model in (2) directly defines the emitter unknown localization as the minimum of the following non-linear estimation problem:  
\[\min_{\lambda,\varphi}\sum_{k=1}^{N}\left\{\text{FDOA}_{i,j}^{CAF}(t_{k})-h(x )\right\}^{T}R_{i,j}^{FDOA}\left\{\text{FDOA}_{i,j}^{CAF}(t_{k})-h(x)\right\}, \tag{3}\]  
where \(x=\left\{\lambda_{e},\varphi_{e},\mathbf{r}_{(}t_{k}),\mathbf{r}_{(}t_{j}),\mathbf{v}_{r, j}(t_{k}),\mathbf{v}_{r,j}(t_{j}),\delta B\right\}\); \(h\) represents the final FDOA measurement model including the general model simplification according to emitter position constraints and all reference frame mapping functions; \(\mathbf{r}_{r}(t_{k}),\mathbf{r}_{(}t_{j}),\mathbf{v}_{r,i}(t_{k}),\mathbf{v}_{r,j}(t_{j})\) are the known receiver positions provided by the satellite orbit data propagation at time \(t_{k}\) and \(t_{j}\); \(R_{i,j}(t_{k})\) is a proper whitening covariance matrix taking into account FDOA\({}_{i,j}^{CAF}\) measurement noise and residual errors; and \(\delta B\) is here considered inner bias affecting the estimation problem. The problem in (4), as per standard FDOA localization approaches, can be prone to multiple minima, so a global search is generally suggested. Different optimizers can be investigated, but a local non-linear least square solver integrated with multiple start approach has been selected as working solution to find the global minima.
## 1 Introduction
In this regard, it is interesting to assess the light-reflectance properties of the WG coated tiles if they are planned to be used as coatings on tiles located in roofs, walls, or other applications in the construction sector. The characterization of solar-reflectance performance of WG coatings, especially in the near-infrared, was poorly discussed in the literature, and even less evidence on the use of waste glass as coatings on tiles. Therefore, a first proof of concept of this influence was performed by testing the relevance of variables, such as the WG-coated tile's composition, and other manufacturing features, such as the holding time or temperature of burning in the whiteness index or light spectral reflectance. To achieve this goal, laboratory measurements were used to determine the light spectral reflectance and degree of whiteness of the specimens in the wavelength range of 350-1100 nm. The overall process was divided into four main steps: characterization of raw materials, production of the specimens, firing stage, and, finally, several tests for the measurement of the degree of whiteness and the specular light reflectance were conducted. Therefore, this work aimed at the use of three different types of WG in the preparation of coating for clay tiles specimens: two types of SLS WGs with different compositions and an LS WG derived from CRT.
## 2 Methods and Materials
### Performance Evaluation Index [17; 18]
#### 2.3.2 SSIM  
The Structural Similarity Index Measure (SSIM) was devised to evaluate the structural similarity of images as perceived by the human eye and has recently become frequently used in the field of image reconstruction [21; 22]. It is calculated using Equation (9).  
\[(SSIM)=\frac{(2\mu_{t}\mu_{re}+c_{1})(2\sigma_{re}+c_{1})}{\left(\mu_{t}^{2}+ \mu_{re}^{2}+c_{1}\right)\left(\sigma_{t}^{2}+\sigma_{re}^{2}+c_{2}\right)} \tag{9}\]  
\(\mu_{t}\) is the mean of the pixel values of the true image, \(\mu_{re}\) is the mean of the pixel values of the reconstructed image, \(\sigma_{t}^{2}\) is the variance of the true image, \(\sigma_{re}^{2}\) is the variance of the pixel values of the reconstructed image, and \(\sigma_{t,re}\) is the covariance between the true and reconstructed images. Additionally, \(c_{1}\) and \(c_{2}\) can be set arbitrarily. In this study, we used the prescribed values \(c_{1}=(0.01\times 255)^{2}\) and \(c_{2}=(0.03\times 255)^{2}\).  
From this equation, SSIM is 1 when the reconstructed and true images are perfectly matched and approaches 0 as the structural similarity decreases [23].
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 1 Introduction
### Enhancing Efficiency with Variable-Speed Drives  
The introduction of variable-speed drives (VSDs) has revolutionised the efficiency of pumps. They allow the pump speed to be adjusted to maintain high system efficiency over a wide range of operating conditions, effectively replacing outdated throttling methods [18].  
In particular, converter-fed synchronous reluctance motors (SRMs) offer high efficiency at a smaller size compared to conventional induction motors (IMs) [19]. Optimising system efficiency through a combination of variable-speed and fixed-speed drives can lead to significant energy savings, especially in low-flow operation [20]. Studies have also focused on the stable operation of variable-speed driven pumps (VSDPs), where pressure fluctuations--although unavoidable--can be minimised through optimised hydraulic design, improving both performance and efficiency [21; 22]. Energy savings using VSDPs can range from 5% to 50% depending on the application [23].  
Studies have shown that VSDP pumps are superior to fixed-speed pumps in terms of energy savings, especially in systems such as condenser cooling water pumps in thermal power plants [24]. However, the efficiency of pumps at reduced speeds is often difficult to quantify as manufacturers do not provide comprehensive efficiency curves. Nevertheless, the use of VSDPs for flow regulation has been advocated in recent studies as a method to achieve significant energy savings [25; 26; 27].  
Electric motors account for a significant proportion of industrial energy consumption, with three-phase squirrel cage IMs dominating the market [28]. However, IM efficiency declines significantly in partial-load conditions, making VSDPs a necessary alternative. In addition, SRMs can offer higher energy efficiency in VSDP applications, especially in systems such as chillers, where they have the potential to replace IMs [29]. The effectiveness of these technological advancements is strongly influenced by energy policies, which have driven industrial energy efficiency initiatives.
# Maintaining the Sense of Agency in Semi-Autonomous Robot Conferencing
## 6. Results
### Comfort Level  
When listening to the partner speak, the auto condition was more comfortable (\(t(11)=-3.545\), \(p<0.05\), \(r=0.730\)) and less nervousness-inducing (\(t(11)=2.035\), \(p=0.06\), \(r=0.414\)) than the tele condition. On the other hand, when speaking to the partners, there was no significant difference in  
Figure 7: Results of measuring intention.  
comfort level (listening: \(t(11)=-0.456\), \(p=0.658\), \(r=0.136\); speaking: \(t(11)=-0.938\), \(p=0.368\), \(r=0.272\)) and nervousness (listening: \(t(11)=-0.491\), \(p=0.633\), \(r=0.146\); speaking: \(t(11)=0.000\), \(p=1.000\), \(r=0.000\)) between the auto- and tele-conditions. In addition, the result of the memory test showed that the subjects tended to remember the answers of the answerer in the auto condition better than in the tele condition (\(t(11)=-2.030\), \(p=0.067\), \(r=0.522\)). However, the difference between the tele- and semi-conditions was not significant (\(t(11)=0.583\), \(p=0.572\), \(r=0.173\)). These results indicated that completely replacing a teleoperation with autonomous behavior improves the comfort level while listening to the partner speak, but this effect is not attained when an operator is speaking and when partially replacing a teleoperation.