esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective
## 3 Mathematical Formulation
### Battery Energy Storage Systems  
In this section, we define a series of equations describing the real power output of the battery energy storage system at time \(t\), adapted from [19]. Equation (3) expresses the real power output \(P_{ESS}(t)\) of the battery storage system at time \(t\), including the discharging \(P_{batt\_dis}(t)\) and charging \(P_{batt\_ch}(t)\) quantities.  
\[P_{ESS}(t)=P_{batt\_dis}(t)+P_{batt\_ch}(t) \tag{3}\]  
To limit the charging and discharging power of the battery storage system within its maximum limits, Equations (4) and (5) are introduced. \(B_{batt\_dis}(t)\) and \(B_{batt\_ch}(t)\), respectively, indicate whether the battery storage system is in a discharging or charging state at time \(t\); \(P_{batt\_dis\_max}\) and \(P_{batt\_ch\_max}\), respectively, represent the maximum discharging and charging power of the battery storage system.  
\[0\leq P_{batt\_dis}(t)\leq P_{batt\_dis\_max}B_{batt\_dis}(t) \tag{4}\]  
\[-P_{batt\_ch\_max}B_{batt\_ch}(t)\leq P_{batt\_ch}(t)\leq 0 \tag{5}\]  
To ensure that the battery storage system performs only one of the charging or discharging actions at each time point, constraint (6) is added.  
\[B_{batt\_dis}(t)+B_{batt\_ch}(t)\leq 1 \tag{6}\]  
The calculation of the SOC (State of Charge) of the battery storage system, the final SOC value, and its range are expressed by Equations (7) and (8). Equation (7) calculates the battery storage system's charge level at time \(t\), \(SOC(t)\). Here, \(\delta_{t}\) represents the time interval; \(\eta_{Ind}\) and \(\eta_{Inc}\) represent the discharging and charging efficiency of the battery storage system; the storage capacity is denoted by \(P_{batt\_capacity}\) in MWh. Equation (8) defines the minimum \(SOC_{min}\) and maximum \(SOC_{max}\) values of the battery storage system.  
These equations collectively define the operational mode of the battery storage system in a time sequence, including charging/discharging behaviors, efficiency, and capacity limits, thus integrating the storage system effectively into the overall power system operation.  
\[SOC(t)=SOC(t-1)-\delta_{t}\left(\frac{P_{batt\_dis}(t)}{\eta_{Int}\times P_{batt\_ capacity}}+\frac{\eta_{Inc}\times P_{batt\_ch}(t)}{P_{batt\_capacity}}\right) \tag{7}\]  
\[SOC_{min}\leq SOC(t)\leq SOC_{max} \tag{8}\]
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 3 The Proposed Method Description
### Sea Clutter Reconstruction Suppression  
In this section, the orthogonal matching pursuit algorithm (OMP) is used [22] to reconstruct the bistatic first-order sea clutter, which is then cancelled and suppressed after reconstruction. The flowchart of bistatic sea clutter suppression method based on compressed sensing optimization is shown in Figure 3.  
In conjunction with the optimization algorithm presented in the previous two sections, the implementation steps of the sea clutter suppression method based on compression sensing optimization proposed in this paper are as follows:  
Step 1. Initialization: the received echo \(x\), initial sparse base matrix \(\psi_{0}\), measurement matrix \(\phi\), regularization parameter \(\alpha\) and \(\beta\);  
Figure 3: The flowchart of bistatic sea clutter suppression method based on compressed sensing optimization.  
Step 2. Sparse base optimization: use the sparse base optimization algorithm based on transfer learning to obtain the optimized adaptive sparse base matrix \(\psi\);  
Step 3. Sensing matrix optimization: use the sensing matrix optimization algorithm based on eigenvalue decomposition and gradient descent to obtain the optimized sensing matrix \(\Gamma\);  
Step 4. Sea clutter reconstruction: Substitute the adaptive sparse base matrix \(\psi\) obtained in step 2 and sensing matrix \(\Gamma\) obtained in step 3 into OMP algorithm to reconstruct sea clutter;  
Step 5. Cancellation and suppression: cancel the original signal and the reconstructed sea clutter signal to achieve the purpose of sea clutter suppression.
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 4 Mobile Robots Categories
### Land-Based
#### 4.2.2 Walking (Legged) Mobile Robot  
Robotics developers have been inspired by animals, varied terrestrial mobility skills, particularly facilitated through limb structures, for decades. For more dynamic and effective legged machines, designers have taken inspiration from nature's flexible designs. These newly developed robots are very different from the production robots used in factories, ushering in a new era of adaptable mobile robots for numerous social purposes. Engineers have been pursuing the design of legged mobility machines for over a century. Starting as far back as the mid-1800s, initial efforts were focused on cleverly designed linkages to mechanically create predetermined leg movements [39].  
Figure 6: Typical four-wheeled mobile robot [35].  
In both natural environments and modern scenarios, there are situations that are either inaccessible or too hazardous for humans, including planetary surfaces, construction sites, mines, disaster relief operations, and anti-terrorism actions. To achieve advancements in the field of science, technology, and society, it is imperative to conduct in-depth research in these contexts to find practical solutions. A common challenge across these situations is the presence of uneven and irregular terrains, which limits the effectiveness of wheeled and tracked robots. Legged robots (see Figure 7), with their terrain adaptation capabilities, are better suited for addressing these challenges.  
Legged robots offer several advantages. First, they exhibit superior adaptability on rugged terrains because their movement involves a series of discrete footprints, unlike the continuous rut left by wheeled or tracked robots. Second, legged vehicles possess enhanced mobility and flexibility due to the numerous degrees of freedom afforded by their multiple legs. These characteristics make legged robots a promising choice for a wide range of applications in challenging environments [40].  
Different types of legged robots are available at present, ranging from one-legged to more-than-six-legged ones [29]. Among all these legged mobile robots, the quadrupedal robot is a legged robot that is superior to wheeled and tracked robots due to its potential to explore all terrain, like that of humans and animals [42].
## 4 Conclusions  
We reported here the concept and proof-of-principle tests of the in situ Fourier transform infrared (FTIR) spectroscopy and dynamic light scattering (DLS) analysis using the high intensity and extended Q-range SANS diffractometer KWS-2. Simultaneous approaches using light absorption and scattering with standard or real time SANS are now possible with the KWS-2, which enable in-beam control of the sample quality or provide complementary structural and composition information to neutrons. The performance of each setup was demonstrated using systems representative of those typically investigated on this beamline and benchmarked to studies performed offline.  
These developments have been greatly assisted by the short exposure times provided by the high neutron flux of the FRM II reactor, the increased maneuverability allowed at the sample position of the instrument, and the flexibility of the instrument control software, NICOS, which is developed in-house at Garching.  
Conceptualization, L.B., T.E.S., A.R.; methodology, F.K., T.E.S., A.R.; software, G.B.; validation, L.B., F.K., T.E.S., A.R.; investigation, L.B., T.E.S., A.R.; data curation, L.B., T.E.S., A.R.; writing--original draft preparation, L.B., F.K., T.E.S., A.R.; writing--review and editing, L.B., T.E.S., A.R. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
Figure 12: SANS cross sections of the 15 mg/mL apomyoglobin molten globule solution. The blue line marks the simulation for the monomer scattering intensity based on the polymer with excluded volume model (\(\mathrm{R_{g}}=2.5\) nm, \(\
u=0.46\)). The green line is the simulated scattering curve for a larger structure. The red line is obtained by adding the green and blue lines and describes the data well.
## 4 On-Board Unit Implementation
### Software Architecture
The connectivity between the OBU and outside is established through an MQTT broker, onto which the MQTT client subscribes at the inception. Over the MQTT client, the OBU is able to subscribe to several topics that represent geographical regions, as was described in Section 4.2. Thus, the OBU is being notified by the MQTT broker upon receiving fresh events from other connected entities, such as backend services, vehicles, etc.
## 1 Introduction  
Left ventricular assist device (LVAD) implantation is increasingly used as a treatment option for patients with advanced heart failure (HF). Modern advancements in LVAD technology enable survival rates comparable to those achieved through heart transplantation. These devices are used not only as a bridge to transplant for patients awaiting heart transplantation but also as a destination therapy for individuals with poorer prognoses who are not eligible for transplantation [1; 2; 3; 4].  
Cardiac rehabilitation is recognized as a beneficial and safe treatment in the post-implantation phase, improving physical capacity, Quality of Life (QoL), and prognosis in this group [3; 5; 6; 7; 8]. Despite the promising improvements in physical health and QoL achieved by enhancing hemodynamics and reducing symptoms like dyspnea and fatigue, LVAD patients often experience anxiety and mood disorders after implantation [9; 10; 11]. This highlights the need for long-term care, preferably provided at home. The implementation of home-based telerehabilitation (HTR) and telecare offers new opportunities in this context [12; 13]. Cardiac teherehabilitation programs represent a promising approach to enhancing long-term functional capacity and improving adjustment and QoL for LVAD patients. These programs achieve this by delivering tailored, multidisciplinary care, increasing levels of physical activity, and providing extended support through telemonitoring during the home-based telerehabilitation phase [12; 13; 14].  
However, little is known about remotely supervised exercise training in HF patients with LVADs. To date, no studies have been published that evaluate home-based telerehabilitation programs for patients with HF and implanted LVADs. Therefore, the aim of our study was to assess the feasibility and safety of HTR and telecare in HF patients with implanted LVADs and to evaluate patients' acceptance of and adherence to HTR.
###### Abstract  
Many biomedical applications require concentration measurements of biological compounds, which may be achieved using targeted fluorescent probes. It has been shown that the ratio between amyloid-Beta and tau protein in the cerebrospinal fluid (CSF) is a good indicator of incipient Alzheimer's disease (AD). We have previously proposed a method that can accurately estimate the concentration ratio of these two proteins without the need to collect CSF samples: Fluorescent probes are injected to the blood and bind to the CSF biomarkers. A miniature needle with an optical fiber excites the fluorescent probes and collects the fluorescence emission. The concentration ratio between the proteins is estimated, and used for diagnosis of incipient AD. We present here the results of the method's concentration ratio estimation during trials performed on mice. Miniature tubes containing two fluorescent probes in several concentration ratios were implanted in two locations in the mice: subcutaneously, and deeper in the abdomen. The fluorescent probes were excited and the fluorescence intensity was measured. The concentration ratios were estimated from the fluorescence intensities with an average error of less than 20%. This study is a step towards the demonstration of the method in in vivo studies in mice with markers in the bloodstream/CSF.  
Keywords:fluorescence; disease markers; Alzheimer's early diagnostics; CSF biomarkers +
Footnote †: journal: Applied Sciences
## 3 Cost and Benefit Models
### Quality Costs  
In Section 2, the PAR model was defined. It assumes that each preventive maintenance activity reduces the age of the equipment, depending on an effectiveness parameter \(\varepsilon\). Changing the age of the equipment affects the time distribution of the system swaps to an out-of-control state. As a consequence, the expected number of non-conforming items is also affected.  
In this section, we develop a quality cost model that considers the effects of PM and upgrading activities on component age based on the PAR model. The model is defined under the following assumptions:  
* (1) the equipment only produces non-conforming items with constant rate, \(\alpha\), while the process is out-of-control,
* (2) the time of the system swaps out-of-control follows a Weibull distribution which depends on the age of the equipment,
* (3) the preventive maintenance and the process inspection are performed simultaneously,
* (4) inspections are error free,
* (5) the process returns to the in-control state when the preventive maintenance activity is performed.  
It is necessary to determine the fraction of time in which the process is in Under Control state (UC state):  
\[\kappa_{m}(w)=\int_{w_{m-1}^{+}}^{w_{m}^{*}}w_{m}\cdot f(w_{m})\cdot dw_{m}, \tag{32}\]  
where \(f(w_{m})\) is the density function, obtained using the conditional hazard function and calculated as:  
\[f(w_{m})=\lambda\cdot\gamma\cdot(\lambda w_{m})^{\gamma-1}\cdot\exp\left(- \left(\lambda\cdot w_{m}\right)^{\gamma}\right). \tag{33}\]  
After substituting (33) in expression (32), we obtain:  
\[\begin{array}{rcl}\kappa_{m}(w)&=&\int_{w_{m-1}^{+}}^{w_{m}^{*}}w_{m}\cdot f (w_{m})\cdot dw_{m}=\\ &=&\int_{w_{m}(1-\epsilon)\sum_{k=1}^{m-1}\epsilon^{k-1}}^{\epsilon^{k-1}}w_{ m}\left(\lambda\cdot\gamma\cdot(\lambda w_{m})^{\gamma-1}\cdot\exp\left(- \left(\lambda\cdot w_{m}\right)^{\gamma}\right)\right)dw_{m}.\end{array} \tag{34}\]  
Once the time the process is in-control between two maintenance activities is calculated, \((M_{m}-\kappa_{m})\), it is possible to obtain quality costs:  
\[C_{q}=n_{c}\cdot C_{\alpha}\cdot\sum_{m=1}^{x}V_{m}^{*}\cdot(M_{m}-\kappa_{m} )\cdot A_{s}(\mathbf{x})\cdot\alpha, \tag{35}\]  
where \(C_{\alpha}\) is the cost of the non-conforming unit.
## 3 Results and Discussion
### Analysis Inputs
#### 3.3.2 Biomass:
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Plant** & **Plant Cross** & **Plant CHF** & **Live Steam** & **Live Steam** & **Adiabatic** & **FGC** & **Effective** \\  & **Gross** & **Electric** & **Efficiency** & **Pressure** & **Flow Rate** & **Frame** & **FGC** & **Radiation** \\  & **Power** & **Efficiency** & **Efficiency** & **Pressure** & **Temperature** & **Temperature** & **Temperature** \\ \hline
**Fuel** & & & & & & & & \\ \hline Solid-fuel-weight percent of moisture & 1.04 & 1.08 & 1.26 & 1.00 & 1.14 & 1.41 & 1.40 \\ Solid-fuel-weight percent of ash & & 1.93 & & & & 1.41 & 4.77 & 1.75 \\ Solid-fuel-weight percent of C & 22.65 & 22.71 & 24.33 & 23.62 & 23.17 & 31.76 & 24.03 & 31.44 \\ Solid-fuel-weight percent of H & 38.44 & 38.29 & 37.09 & 36.80 & _37.09_ & 45.98 & 32.85 & 44.94 \\ Solid-fuel-specified LHV @ 2 SC & 24.20 & 24.10 & 27.91 & 19.38 & 19.02 & 21.08 & 19.10 & 21.29 \\ \hline
**Fumace** & & & & & & & & \\ \hline Evaporator scan outlet temperature & & & & & & & & \\ \hline Flue gas O\({}_{2}\) content & 1.21 & 1.25 & 1.43 & 1.15 & 1.14 & 1.16 & 1.13 \\ Waterwall radial fluid flow correction factor & & & & & & & & 4.23 \\ Carbon-to-so conversion rate & & & & & & & & \\ Asplistivity exponent correction factor & & & & & & & 1.38 \\ Alph particle mean diameter & & & & & & & 2.02 \\ Non-uniform radiant flux factor & & & & & & & 4.32 \\ Radiant color: minor heat loss & 2.50 & 2.35 & 1.50 & & & & & \\ Gas/air side convective heat transfer & & & & & & & \\ \hline coefficient adjustment factor\(-\)SHI & & & & & & & & \\ Gas/air side convective heat transfer & & & & & & & \\ coefficient adjustment factor\(-\)SHZ & & & & & & & \\ Gas/air side convective heat transfer & & & & & & & \\ coefficient adjustment factor\(-\)SHZ & & & & & & & \\ \hline
**Combustion air** & & & & & & & & \\ \hline Rotary air heater: cleanliness factor & 3.19 & 3.16 & 2.16 & 1.15 & & & & \\ \hline
**Water/steam** & & & & & & & & \\ \hline High-pressure preheater 3: condensing zone & & & & & & & \\ heat transfer coefficient correction factor & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sobol’ indices in % of the impactful input parameters per output parameter for biomass.
# Heat Transfer Enhancement of Falling Film Evaporation on a Horizontal Tube by Thermal Spray Coating
## 2 Experimental Apparatus and Methods
### Data Reduction  
The film Reynolds number, \(Re_{f}\), is defined by  
\[Re_{f}=4\Gamma\mu_{L}^{-1}=4m(2L)^{-1}\mu_{L}^{-1} \tag{1}\]  
where \(\Gamma\) is the liquid mass flow rate on the one side per unit length of the test tube (kg\(\cdot\)m\({}^{-1}\)\(\cdot\)s\({}^{-1}\)), \(\mu_{L}\) is the viscosity of the refrigerant liquid (Pa\(\cdot\)s), \(m\) is the mass flow rate (kg\(\cdot\)s\({}^{-1}\)), and \(L\) is the heating length (m). The heat flux \(q\) (W\(\cdot\)m\({}^{-2}\)) is defined by  
\[q=QA^{-1}=Q(\pi DL)^{-1} \tag{2}\]  
where \(Q\) is the heat input (W), \(A\) is the heat transfer area (m\({}^{2}\)), and \(D\) is the outer diameter of the test tube (m). For the tube with the thermal spray coating, the heat flux was defined using the diameter at the bottom of the coating. The heat transfer coefficient \(\alpha\) (W\(\cdot\)m\({}^{-2}\)\(\cdot\)K\({}^{-1}\)) is defined by  
\[\alpha=q(T_{wall}-T_{sat})^{-1} \tag{3}\]  
where \(T_{wall}\) is the measured surface temperature of the test tube (\({}^{\circ}\)C), and \(T_{sat}\) is the saturation temperature (\({}^{\circ}\)C) calculated from the measured pressure using REFPLOP [16] version 10.0. \(T_{wall}\) is obtained by the trimmed average of the seven measured wall temperatures; the maximum and the minimum values were trimmed, and the others were averaged. Because only five measurements were used for averaging, the error bars represent the maximum and minimum values. The measurement repeatability was confirmed by introducing the trimmed average.  
The uncertainties of measurement accuracies are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measured Physical Value** & **Uncertainty** \\ \hline Temperature & \(\pm\)0.05 K \\ Pressure & \(\pm\)4 \(\times\) 10\({}^{3}\) Pa \\ Mass flow rate & \(\pm\)4\% R.D. \\ Heat input & \(\pm\)5.5 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement accuracies.  
Figure 3: Microphotographs of the thermal spray coating. (**a**) Top view. (**b**) Cross-sectional view.
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
## 2 Methodology
### Improved YOLOv12 Algorithm
#### 2.2.3 Slide Alignment Loss (SAL)
Furthermore, most importantly, to enhance the model's ability to detect hard examples, SAL introduces a weight matrix factor \(R^{SAL}\) for pixel-wise weighting:  
\[R^{\text{SAL}}=\exp(\frac{\left(x-x_{gt}\right)^{2}+\left(y-y_{gt}\right)^{2} }{\left(W_{g}^{2}+H_{g}^{2}\right)^{*}}). \tag{12}\]  
To prevent \(R^{SAL}\) from generating gradients that impede convergence, \(W_{g}\) and \(H_{g}\) are decoupled from the computation graph. Here, \(W_{g}\) and \(H_{g}\) denote the width and height of the minimum bounding box, respectively. This matrix can be customized based on pixel importance, such as object type, pixel position, or pixel difficulty. \(R^{SAL}\) is used in the weighted calculation of intersection and area to get \(\mathcal{L}_{\text{sample}}\):  
\[\mathcal{L}_{\text{sample}}=rR^{\text{SAL}}L_{\text{IoU}}. \tag{13}\]  
The expression for \(r\) is given by \(r=\frac{\beta}{\delta\omega^{\beta-\beta}}\). Here, \(\beta\) constructs a non-monotonic focusing factor that defines the degree of clustering and describes the quality of the Anchor boxes. A higher degree of clustering indicates better Anchor box quality. Finally, SAL computes the total loss as the weighted sum of the losses described above:  
\[\mathcal{L}_{\text{SAL}}=\lambda_{1}\mathcal{L}_{\text{center}}+\lambda_{2} \mathcal{L}_{\text{angle}}+\lambda_{3}\mathcal{L}_{\text{sample}}. \tag{14}\]  
where \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are hyperparameters used to balance the contributions of each loss component. For PCB inspection scenarios with small-sized targets that have significant morphological variations and inconsistent rotation angles, the SAL function enables more precise bounding box regression by comprehensively considering errors in center, size, and angle. At the same time, by utilizing an adaptive weighting mechanism, SAL can focus on difficult samples with low IoU, further enhancing overall detection performance.
## 2 Background and Challenges
### Challenges in Mining GNSS Data  
While the above discussions indicate the necessities and potentials of mining GNSS data, several challenges need to be overcome to complete this task. Before a more comprehensive study of GNSS data in Section 3.1, we briefly hint at two major issues here.  
* **Beyond dimensionality**: the involved data structure is fairly complicated, as shown in Table 1 for one satellite. One could deem it a high-dimensional vector and simply stack 32 of them (for all GPS satellites) into a matrix, yet such an input data structure is inefficient: many fields (features) can be highly redundant, yet we are expecting a compact and effective feature set for each satellite, which enables a learning module to efficiently capture location contexts under profiling.
* **Inherent misalignment**: No matter the data structure used, proper indexing is key to aligning relevant features. Whereas conventional mobile sensing has sensor IDs as a natural indexing, using satellite IDs for the same purpose can severely misalign the target feature: many IDs are rarely visible and the same location may witness very different satellite coverage at different points in time, while even the same number of visible satellites may form various patterns and, thus, result in very different localization performances.
## 4 Results and Discussions
### Permeability Characteristics  
Permeability plays an important role in controlling the high yield of CBM [9; 29; 46]. According to the injection/fall-off well testing results of coal seams, it was found that the permeabilities of coal seams in the study area are very low, ranging from 0.001 mD to 2.85 mD (Figure 8), with an average of only 0.29 mD. The permeabilities between different coal seams or even the same coal seam show a strong heterogeneity with greatly variable permeability values (Figure 8). The median permeability from the C\({}_{14}\) to C\({}_{25}\) coal seam shows a decreasing trend (Figure 8).  
The correlation between coal permeability and buried depth in the study area is, overall, low, but it was found that the coal permeability shows a decreasing trend with the increase in buried depth (Figure 9a). Previous studies have shown that present-day stress magnitude and orientation are important reasons for vertical variation in permeability [47; 48; 49]. Figure 9b illustrates that a negative relationship exists between coal permeability and minimum horizontal stress magnitude in the study area.  
Figure 8: Distribution ranges of well testing permeabilities of different coal seams of the Dacun Block.  
Figure 9: The relationship between the (**a**) burial depth, (**b**) minimum horizontal stress, and permeabilities of coal seams in the Dacun Block.