esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 2 Mathematical Models of the System Components
### Battery Storage System  
Among various kinds of battery storage systems such as lithium-ion battery or nickel-zinc battery, we chose lead-acid batteries for their low cost (300-600 USD per kWh). Lead-acid batteries have a good cycle efficiency of up to 90% and a low self-discharge rate of less than 0.3% [37]. They are designed to withstand more deep discharge cycles, which make them suitable for an HRES.  
One of the most important parameters of the battery system is the \(SOC\), which expresses the level of charge relative to its capacity. The excess power is used to charge the battery, while a power deficiency towards the load demand discharges the battery. The battery \(SOC\) can be defined as follows [38]:  
\[SOC_{t+1}=SOC_{t}\pm\frac{P_{Bat}\eta_{Bat}}{P_{n,Bat}}\times 100 \tag{3}\]  
where \(SOC_{t+1}\) and \(SOC_{t}\) contain the battery \(SOC\) at the next time step and the current step, respectively. \(P_{Bat}\) stands for the battery power charging or discharging (kWh), while \(P_{n,Bat}\) denotes the battery rated capacity, and \(\eta_{Bat}\) denotes the round-trip efficiency.  
When the battery is turned on during the operation, the charging and discharging rates of the battery are defined based on the amount of power required at the current time step, always satisfying:  
\[P_{Bat,discharge}\leq P_{Bat}\leq P_{Bat,\ charge} \tag{4}\]  
where \(P_{Bat,discharge}\) with negative sign indicates the discharge rate of battery, and \(P_{Bat,\ charge}\) with positive sign shows the charge rate of battery.  
At any time-step, the value of \(SOC\) must satisfy:  
\[SOC_{mint}\leq SOC\leq SOC_{max} \tag{5}\]
## 3 Methodology
### Data Preprocessing
#### 3.2.1 Data Cleaning  
Data cleaning involves the selection of relevant tables and columns from the Moodle database and then extracting valuable records while filtering out irrelevant ones. Initially, we focused on the primary tables that record students' interactions within the platform, selecting four key tables (e.g., logstore_standard_log). We extracted columns related to student interactions and grades from these tables. For example, in the logstore_standard_log table, we excluded the objecttable column, as it did not contribute to our analysis. This step ensured that only essential data necessary for predictive modeling was retained, optimizing the data's volume and relevance.
## 3 Results and Discussion
### Spatial Distribution of Defects Ti\({}_{6}\) in Nanoparticles
Points in Figure 5 show sampling experimental results, data on  
Figure 4: Dependence of the defect concentration \(C_{d}\) on the specific surface area \(S\) of TiO\({}_{2}\) nanoparticles. Points are experimental data for the powders synthesized by laser evaporation of ceramic targets. Line is calculated by Expression (2) at \(N=12\) and \(\Delta\rho\) y = 7.56 \(\times\) 10\({}^{-8}\) g/cm\({}^{2}\). \(\left(C_{O}/C_{O}^{0}\right)\) are also given. It is easy to show that the values of \(\propto\) and \(y\) obey the expression \(y=2\infty\). Bends on the dependences \(\propto\left(C_{O}/C_{O}^{0}\right)\) correspond to the formation on the nanoparticle surface of a solid monolayer in which oxygen atoms are absent. The horizontal portions of the dependences \(\propto\left(C_{O}/C_{O}^{0}\right)\) correspond to the formation in nanoparticles of one more monolayer deficient in oxygen. It follows from Figure 5 that the fraction \(\propto\) that is taken by regions consisting of 6 titanium atoms can be an easily controlled parameter. In particular, this refers to nanoparticles of TiO\({}_{2}\) produced by the SG method. In the as-synthesized state, they are virtually stoichiometric (\(\propto\approx 0\)), and by vacuum annealings and alloying with cobalt, this value can be increased to 1. It is shown that under certain conditions, in the TiO\({}_{2}\) nanoparticles there form two-dimensional defects consisting of 6 titanium atoms and localized on the nanoparticle surface, which results in a significant deviation of the chemical composition from the stoichiometry. Since, as noted in Section 1, in the TiO\({}_{2}\) nanoparticles there are present defects of other types, of interest is to compare the data on the concentration of surface two-dimensional defects with those obtained in previous studies. Unfortunately, in view of the absence of such information in works on oxide nanoparticles, we have to restrict ourselves to rough estimates. In a general case, defects of almost all types cause changes in the chemical composition of nanoparticles and their presence in the samples gives rise to a deviation from the linear dependence in Figure 3. Since in the work no deviations beyond the statistical experimental errors were registered, one can conclude that the concentration of other defects was far smaller than that of defects consisting of 6 titanium atoms.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 2 Related Work
### Accountability in Robotics  
As the complexity of robots increases along with the breadth of use, further requirements regarding the accountability and traceability of robots emerge. As an example for such requirements, we want to hint at the HLEG guidelines for ethical AI of the European Commission [28] that can to a large part also be applied to modern robotics. There, properties like accountability and human oversight are demanded. Also in robotics, those are important factors.  
The authors of [29] tried to find a modern definition of [PERSON]'s three laws called the three laws of responsible robotics. Law 1 reads \"A human may not deploy a robot without the human-robot work system meeting the highest legal and professional standards of safety and ethics.\" thus defining in some way the background to our work. Law 2 is re-defined as \"A robot must respond to humans as appropriate for their roles\" thus effectively defining some of the requirements fulfilled by this work that only authorized entities may give instructions to a robot (in form of programming in our case).  
In [30], [PERSON] describes scenarios of robot use in critical areas. He argues that accountability is a key point whenever the operator needs to rely on the robot's correct task execution.  
[PERSON] and [PERSON] have presented a formalization of accountability for cyber-physical systems [31]. Such a formalization allows for comparison of different accountability approaches and makes it possible to define the exact accountability properties for a certain system. Together with what the authors have presented in [32], this enables the applied consideration of accountability for systems that interact with the environment.  
Technical systems that support accountability by recording data are often referred to as black box systems. From the original concept developed for airplanes [33], many offsprings have been developed in different fields. For robotics, our group has presented a cryptographically secured black box system that can be a software component or a dedicated device [34]. [PERSON] et al. have presented a blockchain-based solution for ROS2 [35].  
For mobile service robots, [PERSON] et al. have presented a framework and a black box component for [36]. The group has also presented an approach on how to integrate accountability logging into ROS [37] and has demonstrated how a graphical environment can support the processing of accountability data [38].  
All the work mentioned here fuels our effort to protect robot programs in distributed environments and make the development of robot applications compliant and eventually trustworthy.
## 5 Results
### Robustness to Measurement Noise  
Finally, the developed control system is tested by applying it to the nonlinear model of the triple inverted pendulum given in Equation (3), with measurement noise in the form of random white noise and with amplitude \(\pm\)0.4 V applied to each reading. The three angles respond as shown in Figure 17; the phase-plane trajectories of the angles and their derivatives are shown in Figure 18, and the applied torques are shown in Figure 19.  
Figure 16: Perturbed by external-disturbance system torque: (**a**) first motor; (**b**) second motor.  
Figure 17: Perturbed by measurement-noise system response: (**a**) first link’s angle; (**b**) second link’s angle; (**c**) third link’s angle.  
Figure 18: Perturbed by measurement-noise system phase-plane trajectory: (**a**) (\(\theta_{1},\dot{\theta}_{1}\)); (**b**) (\(\theta_{2},\dot{\theta}_{2}\)); (**c**) (\(\theta_{3},\dot{\theta}_{3}\)).  
In this case, the hybrid H\({}_{\infty}\)/SMC system produces less overshoot in the first and third angles' responses and fewer errors in the second and third angles' responses.  
Figure 19 shows that, compared to the H\({}_{2}\) controller, the proposed hybrid H\({}_{\infty}\)/SMC controller requires much less effort at start-up, and almost the same effort is needed at steady state.  
The characteristics of the proposed hybrid H\({}_{\infty}\)/SMC system performance in all tested cases are summarized in the three charts in Figures 20-22, and will be discussed in the next section.  
Figure 19: Perturbed by measurement-noise system torque: (**a**) first motor; (**b**) second motor.  
Figure 21: Overview of the three angles’ overshoots in all tested cases.  
Figure 22: Overview of the two motors’ torques in all tested cases.  
Figure 20: Overview of the three angles’ errors in all tested cases.
# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
## 3 ETS-YOLOv7 Algorithm  
We propose ETS-YOLOv7 for traffic surveillance, building on the YOLOv7 model due to its advantages of fast detection speed, high accuracy, and ease of deployment on edge devices. In this section, we present a comprehensive description of the ETS-YOLOv7 algorithm from four perspectives: algorithm overview, multidimensional attention mechanisms, loss function enhancement, and compact layer aggregation networks.
## 2 Materials and Methods
### Structure of the Proposed Network  
The proposed LDCT reconstruction scheme is presented in Figure 1. The LDCT reconstruction method consists of two modules: a sinogram domain network and an FBP module. The sinogram domain network learns the mapping in (3) to recover the full-view sinogram. The FBP module achieves the linear mapping in (2) with a convolutional layer and a fully-connected layer, both of which have fixed (pre-computed) weights. An image domain network can also be appended after the FBP module to form a dual-domain framework.  
The detailed structure of the sinogram domain network is depicted in Figure 2, using the residual structure to predict the missing projection views \(\mathbf{Y}_{S}\). A linear interpolation module is designed to predict the linear component of \(\mathbf{Y}_{S}\), and a full convolution module consisting of four convolutional layers is employed to predict the nonlinear residuals between \(\mathbf{Y}_{S}\) and its linear component. The first three convolutional layers of the full convolution module use a standard convolutional operation with \(stride=1\), _valid padding_ and _tanh_ activation to extract sinogram features. The last convolutional layer is a projection-view regression layer with the number of channels \(S-1\) which uses linear activation. To compensate for the feature map shrinkage due to valid padding, the sparse-view sinogram \(\mathbf{Y}_{S}\) is first expanded before being input into the convolution module. Zeroes are interpolated in the detector dimension, and in the view dimension, since the projection views are sampled uniformly around a circle, the extended views (columns) can directly copy the original views (columns) of the same angles.  
The method can flexibly handle CT reconstruction of different sparse-view levels by adjusting the parameters and view shuffling methods in the last layer. The use of a residual structure based on linear interpolation can effectively improve the efficiency of the model. The neural-network-based method implicitly learn  
Figure 1: Sinogram domain reconstruction scheme for sparse-view CT.  
Figure 2: Structure of the proposed sinogram domain network.  
sparse-view sinograms to full-view sinograms, making the reconstruction process more accurate and robust.
## 3 Joint Cooperative Spectrum-Sharing Scheme
### Analysis of the Proposed Two-Stage Stackelberg Game
#### 3.3.1 \(DT_{n}\)'s Strategy in Stage \(I\)  
Given the time allocation coefficient \(\tau_{mn}^{*}\) offered by the \(E\!U_{m}\) in Stage \(I\), \(DT_{n}\) has to calculate the optimal power allocation coefficient \(\alpha_{mn}^{*}\) for the sake of maximizing its utility. Hence, the optimal power allocation coefficient \(\alpha_{mn}^{*}\) may be expressed as:  
\[\alpha_{mn}^{*}=arg \max_{\alpha_{mn}}U_{mn}^{D2D}(\alpha_{mn},\tau_{mn}^{*}) \tag{13}\] \[s.t. R_{nm}^{C}\geq\xi R_{m}^{dir}\] (14) \[0<\tau_{mn}^{*}<1\] (15) \[0<\alpha_{mn}<1\] (16) \[\forall m\in\{1,\ldots,M\},\forall n\in\{1,\ldots,N\} \tag{17}\]The first constraint guarantees that \(E\!U_{m}\) is willing to cooperate. The \(E\!U_{m}\) has a transmit rate requirement of \(R_{req}\), which the \(DT_{n}\) should help achieve. Hence, transmission rate \(R_{mn}^{C}\) achieved by the cooperative communication has to be higher than the \(E\!U_{m}\)'s target rate, namely \(R_{mn}^{C}\geq\xi R_{m}^{dir}\). \(\xi\) is the rate requirement coefficient.  
Based on Equation (12), the second derivative of \(U_{DT}(\alpha_{mn},\tau_{mn})\) is smaller than zero, namely \(\frac{d^{2}U_{DD2}}{d\alpha_{mn}^{2}}<0\). Hence, \(U_{DD2}(\alpha_{mn},\tau_{mn})\) is a convex function of \(\alpha_{mn}\). Therefore, the optimal power allocation coefficient of \(\alpha_{mn}^{*}\) may be calculated by solving the KKT conditions [44], as shown in Equation (18).  
\[\begin{split}\alpha_{mn}^{*}=& arg\ \max_{\alpha_{mn}}U_{mn}^{DD2}( \alpha_{mn},\tau_{mn}^{*})\\ =&\frac{h_{bm}P_{n}-N_{0}\left[\left(1+\frac{h_{bm}P _{n}}{N_{0}}\right)^{\frac{\xi}{1-\tau_{mn}}}-1-\frac{h_{bm}P_{n}}{N_{0}} \right]}{h_{bm}P_{n}+ah_{bm}P_{n}\left[\left(1+\frac{h_{bm}P_{n}}{N_{0}} \right)^{\frac{\xi}{1-\tau_{mn}}}-1-\frac{h_{bm}P_{n}}{N_{0}}\right]}\end{split} \tag{18}\]
# Analysis of Sealing Characteristics of Lip Seal Rings for Deep-Sea Separable Pressure Vessels
## 2 Seal Calculation Model
### Seal Structure and Force Analysis  
A finite element method (FEM) has been used here to get the solutions to seal problems. The finite element method is a powerful numerical approach originally developed for solving nonlinear problems, and it continues to be the method of choice for complex nonlinear systems [16; 17]. Because of the symmetrical shape and operational characteristics of the seal, both its structure and the loads it endures are entirely symmetrical. As a result, a two-dimensional axisymmetric model is employed for simulation calculations. The ANSYS Workbench software was utilized to develop a model of the upper and lower spherical shells of the pressure vessel along with the lip seal. Considering the seal was not subjected to any pressure, the distance between the upper and lower spherical shells was 5 mm. The mesh was divided, as illustrated in Figure 5, with a refined contact boundary. A total of 13,671 nodes and 12,675 cells were generated. The contact between the seal and the two sealing surfaces was defined as frictional contact, with a friction coefficient of 0.3 [18]. The Normal Lagrange method was employed to avert any plausible interpenetration between the seal and the sealing surfaces.  
Boundary Conditions  
The finite element analysis comprises multiple load steps:  
* The lower spherical shell is completely fixed, and a preloading force of 20 kN is imposed on the upper spherical shell to simulate the downward pressure exerted on it following the application of the preload force;
* Calculate the vertical compressive force on the pressure vessel at different water depths using Equation (1) and apply it to the upper spherical shell to simulate the gradual downward pressure exerted on the shell by water pressure;
* Subject the lip seal and the inner cavity surface to a pressure load to simulate the effects of water pressure on the seal, as depicted in Figure 6.
## 2 Materials and Methods
### Velocity Profiles Characteristics  
For a rectangular cross-section microchannel of width w and height h, the velocity profile can be determined by solving the Navier-Stokes Equation (2) using the Fourier series [40]:  
\[\mathrm{v(x,z)}=\frac{48Q}{\pi^{3}\mathrm{\cdot h\cdot w}}\frac{\sum_{i=\mathrm{ odd}}^{\infty}\frac{1}{i^{3}}\left[1-\frac{\cosh(i\pi\frac{w}{\mathrm{m}})}{\cosh(i \pi\frac{w}{\mathrm{m}})}\sin(i\pi\frac{z}{\mathrm{m}})\right]}{1-\sum_{i= \mathrm{odd}}^{\infty}\frac{1}{i^{3}}\frac{1}{i^{3}}\frac{\mathrm{m}^{2}}{i^{ 3}}\frac{\mathrm{h}}{\mathrm{w}}\tanh\left(i\pi\frac{w}{\mathrm{m}}\right)} \tag{4}\]  
From Equation (4) [42, 43], the velocity profile has a different distribution along the x- and z-directions. The velocity profile is parabolic, whereas, along the larger dimension, the velocity shows a plateau in the centre and then changes near the wall [44, 45, 46].  
As shown by [PERSON] et al. [46], as the ratio between the height (h) and the width (w) of the microchannel decreases, the velocity profile tends to flatten in the centre of the microchannel, showing a plateau region [47]. Secondly, it is important to highlight the role of wall shear stress, which is a mechanical stimulus generated by the friction of the fluid against both the channel walls and the apical cell membranes. The shear stress plays a key role in the human body as it can alter the behaviour and properties of cells, such as their morphology, detachment from the substrate, alignment, and gene expression [48]. The physiological values of shear stress in the human vascular system are in the range of \(5\times 10^{-2}\) to 12 Pa. Therefore, maintaining this parameter within this range is fundamental for the physiological functions of cells. In fact, when the shear stress values are below this range, the cells can undergo apoptosis; conversely, when its values are above the range, the cells can be washed away with the fluid flow [49, 50, 51].  
Since small-geometric-length scales characterise microfluidic systems (water is used as fluid for the simulations), from the Navier-Stokes equation, it is possible to express the wall shear stress along a rectangular cross-section microchannel as [29, 52]:  
\[\tau=\frac{6\upmu Q}{\mathrm{wh}^{2}} \tag{5}\]  
where \(\upmu\) is the dynamic viscosity of the fluid (Pa-s), Q is the volumetric flow rate (m\({}^{3}\)/s), w is the channel width (m), and h is the channel height (m).
###### Abstract  
Background: Skin photoaging caused by ultraviolet B (UV-B) irradiation leads to the formation of wrinkles. A method to lessen wrinkles is the application of Polydioxanone (PDO) lifting threads. HanDam (Twist) is a PDO lifting thread with a unique morphological twisted shape. Objective: The aim of this study was to evaluate the wrinkle improvement ability of HanDam (Twist) on UV-B irradiation-induced skin photoaging in a hairless mouse model. Methods: Wrinkles were induced by UV-B irradiation to the backs of female hairless mice for six weeks. After induction, the wrinkles were treated with threads, and the mice were monitored for six weeks post-treatment. Results: Our results showed that treatment with HanDam (Twist) effectively ameliorated UV-B irradiation-induced wrinkle depth and significantly increased collagen density by 13% compared to HanDam (non-Twist) in the histological analysis. In measuring protein expression related to collagen production, HanDam (Twist) significantly increased transforming growth factor beta (TGF-\(\beta\)) and collagen type 1 (COL1) by 46% and 67% compared to HanDam (non-Twist). Matrix metalloproteinase-1 (MMP-1) protein expression showed similar density and no significance compared to HanDam (non-Twist). Conclusions: These findings suggest that HanDam (Twist) improves the effectiveness of lifting threads for skin care compared to that of existing products.  
skin photoaging; facelift; transforming growth factor beta (TGF-\(\beta\)); collagen type 1 (COL1); HanDam (Twist)
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 2 Related Works
### Summary  
In short, CBC counting-related research can fall into three types: automated analyzer, image processing-based methods, and machine learning-based methods. An automated analyzer, which substitutes chemical manipulations and measurement, is currently the most general method, although it requires lots of time and manpower. With the development of hardware and algorithms, computer-aided methods emerged in relevant studies. Traditional image processing-based methods used various image processing techniques to extract handcrafted features and statistical classifiers for blood cell detection and recognition and achieved outstanding scores. On the other hand, machine learning-based methods built deep structures to capture more non-intuitive but useful features to better carry out CBC counting.
## 6 Conclusions  
In this paper, RST-FLOOD has been applied to analyze the flood event that affected the Basilicata and Puglia regions in the first week of December 2013. Two different indicators, based on the difference and ratio between NIR and VIS reflectance (i.e., channel 2 and channel 1 for MODIS, I2 and I1 for VIIRS), respectively, have been used to detect flooded areas.  
When implemented on MODIS data, the RST-FLOOD indices showed a behavior similar to the one observed when studying a different test case. A similar behavior between the two ALICE indices has been recognized, with the one based on the difference being more exposed to a few false positivesdue to suspended sediments flow into the San Giuliano lake. A better sensitivity of the VIIRS-based indices was observed. This aspect, which needs more investigation, can be preliminarily justified by considering the different specific spectral and radiometric accuracies of the two sensors and the longer historical series of MODIS data.  
The flood maps provided by both RST-FLOOD indices, aggregating the MODIS and VIIRS detections, are in good geographical agreement with the one derived using Landsat 7 ETM+ data of 5 December 2013. Such an integration allowed for the discrimination of a flood area extent up to 80 km\({}^{2}\), lower than that potentially detectable by using a high spatial resolution sensor like the ETM+ (about 24% greater). Despite this, the double advantage of an integrated system in effectively supporting flood risk management is clearly demonstrated. The high temporal revisiting capability offered by optical sensors aboard weather satellites allows for the improvement of the observational capability of an operational monitoring system, while the combined use of different indices improves the accuracy in flood extent mapping.  
Conceptualization, T.L., M.F., N.P. and V.T.; Methodology, T.L., N.P. and V.T.; Validation, M.F., T.L. and V.S.; Data Curation, E.C., M.F. and V.S., Writing--Original Draft Preparation, T.L., M.F. and E.C.; Writing--Review & Editing, N.P. and V.T.  
This research received no external funding.  
The authors declare no conflict of interest.