esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Management Strategy for an Autonomous DC Microgrid
## 3 Power Management Strategy  
In the microgrid system, both PV power \(p_{PV}\) and load power \(p_{L}\) are randomly variable, but PV power can be limited, and load power can be shed if needed. Conversely, electrochemical battery bank power \(p_{B}\), DG power \(p_{DG}\), and SC power \(p_{SC}\) are the controllable elements within their own physical limits. The control law of power balancing must thus be completely respected at any time according to the following equation:  
\[p_{PV}(t)+p_{B}(t)+p_{SC}(t)+p_{DG}(t)=p_{L}(t)+C\cdot v_{DC}(t)\frac{dv_{DC}(t )}{dt} \tag{9}\]  
where \(C\) is the DC bus capacitor and \(v_{DC}\) is the DC bus voltage.  
To achieve real-time control, an algorithm should be designed to arrange the power system elements in a specified sequence. This algorithm should assign power references to each source and give power-shedding references for the PV source and the load. The algorithm must also comply with the power balance equation, the predefined priority order of the components to operate in the DC microgrid, and the limitations imposed. The PV source has the first priority to supply the load. Accordingly, the electrochemical battery bank is essential to ensure stable operation of the whole system due to the intermittence of PV generation. Otherwise, when \(SOC_{B}\) decreases to its lower limit or the necessary power is greater than \(p_{B\_MAX}\), the DG operates to supply the load and also to recharge both storage units: the electrochemical one and the electrostatic one. Nevertheless, the DG needs some time to start up and cannot immediately offer the power needed. Therefore, the SC is used to compensate the power deficiency during DG start-up, i.e., the SC and the DG are unified as a backup source to supply the DC bus. The SC must always have enough energy to supply the whole DC microgrid during the start-up phase of the DG. Resultantly, the priority of the SC is higher than that of battery bank in the recharging sequence, so excess power is preferentially injected into the SC. To describe the overall power management strategy, Figure 3 represents the power system control algorithm flowchart and its nine possible power flow cases.
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 3 Results  
We next compare the performance of traditional pilot placement methods with ML-based approaches, including k-means clustering, linear regression, and random forest regression. Additionally, we explore the combination of SVD and machine learning for further performance improvements.  
Simulation parameters are provided in Table 1. Throughout the simulations, we compare the performance of each method, analyzing their advantages and disadvantages.
## 2 Background and Proposed Method  
### Problem Description  
The speaker verification task is characterized by the use of a labeled dataset, which is partitioned into training and test subsets. The subsets are created by matching speech segments with their corresponding labels. Following the training phase, the speeches from the test dataset are inputted into the model to obtain prediction vectors. The similarity between these vectors is assessed using an appropriate metric to ascertain the likelihood of their origin from the same speaker. Most traditional speaker recognition systems employ MFCCs as their primary features. As illustrated in Figure 1, however, the presence of strong noise and reverberation significantly interferes with the speech signal, posing challenges to the effectiveness of MFCC-based recognition. The complexity of noise sources makes their elimination challenging, and the differentiation of reverberation from the clean signal is difficult [48]. Consequently, bypassing the spectrogram and employing an alternative initial feature extractor may be considered as a potential solution. This paper proposes employing a small amount of labeled data in conjunction with a large amount of unlabeled data, which is obviously more applicable. This results in a model with greater generalization and migration capabilities.
## 4 Case Study  
The research methodology is applied to a three-story residential building located in Italy, used as a case study to structure cost and energy data within an IFC-based framework for retrofit decision-making. The study evaluates different insulation retrofitting scenarios, focusing on structured cost assessment rather than optimizing energy consumption. The goal is to provide a transparent decision-support system that allows stakeholders to compare retrofit options based on total cost considerations.  
To facilitate this evaluation, the BIM model of the building is enriched beyond its conventional geometric representation by embedding cost and linking the energy analysis report within a standardized IFC schema. This approach ensures that the model serves as a searchable and structured information framework rather than just a static digital representation. Cost and energy consumption data are systematically linked to building components, enabling comparative analysis of retrofit scenarios. Twelve parametric insulation retrofit scenarios were developed by applying a consistent insulation thickness but varying the thermal conductivity values (\(\lambda\) ranging from 0.026 to 0.084 W/mK). It should be noted that these form a sample set and can be replaced or extended with other retrofitting scenarios in future applications. These variations represent commercially available insulation materials and support a comparative evaluation of their cost and energy performance characteristics.  
The case study utilizes the IfcBuildingSystem to organize the building envelope, ensuring that insulation interventions are clearly classified and their cost implications are systematically integrated. Initially, cost data (i.e., material, installation costs) are defined using IfcCostItem entities, associated with IfcBuildingSystem to organize functional components, and grouped into separate IfcCostSchedules corresponding to each retrofit  
Figure 2: IFC-based workflow for structuring cost and energy data in retrofit analysis.  
scenario. This structured flow improves interoperability and accessibility for cost estimation evaluations. By structuring data within an IFC-based model throughout a semi-automated process, this approach reduces data fragmentation and enhances the clarity of cost-energy relationships, demonstrating the advantages of an integrated BIM framework for retrofit planning and decision-making. Due to current limitations in embedding dynamic energy data within standard IFC models, operational energy consumption is simulated externally in EnergyPlus, and corresponding energy costs are subsequently derived and added to the model.  
Figure 3 presents the BIM representation of the case study building (left) and the corresponding insulation layer applied for the retrofit analysis (right).
## 3 System Architecture  
The fundamental idea behind the OCC system is to utilize the intensity of optical signals for data transmission and reception. Accordingly, enhancing the system's overall communication performance significantly depends on an efficient modulation scheme. The most common modulation scheme is OOK, which is amplitude-shift keying modulation in its most basic form. It transmits data using two signal states, ON and OFF, which represent bits \"1\" and \"0\", respectively. This paper presents the details of the proposed LED array scheme that uses the OCC technique to be designed for IoT systems. The spatial frame format defined in our scheme makes it easy to use deep-learning algorithms at the receiver side for data decoding and detection based on the location of each LED in the LED array. The architecture of the OOK-MIMO transceiver is illustrated in Figure 1, with the operational details of the blocks expounded in Section 3.  
Figure 1: Reference architecture of 2D–MIMO technique based on a deep-learning neural network.
## 4 Biaxial Failure Prediction for the PRC with Interfacial Debonding
### Effect of Interfacial Debonding  
For investigating the interfacial debonding, perfect interfacial bonding is also used to analyze the effect of the biaxial failure strength for comparison. In Figure 5, the biaxial failure envelopes of the spherical PRC are plotted. For validating the proposed method, the failure strength of experimental data acquired by [PERSON] [36] in uniaxial tensile loading is also shown in the figure. It can be seen that the theoretical results show good agreement with experimental data only when the interfacial debonding is considered. The relative error is less than 8%. For the PRC with perfect interfacial bonding, the failure envelope is symmetrical about the origin of coordinates (0,0). In the case of compression-compression loading, the interfacial bonding influences on failure strength can be ignored, and the final failure for the PRC is decided by the compressive failure of the reinforced particles. However, the PRC with interfacial debonding show lower failure strength in region I. The interfacial debonding influences on the biaxial failure envelope show different variations in tensile-tensile loading. The failure strength is determined by the interfacial cohesive strength and constituent materials' properties. Meanwhile, the maximum difference of failure strength between perfect interface and interfacial debonding is along the line E'-E.  
Figure 4: Stress-strain responses of composites with different strain rates.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Multi-Access Protocols
#### 3.2.4 Frequency Division Multiple Access  
FDMA splits the frequency band into several channels that can be used by end-devices to separate their transmissions. In [62], the authors presented a FDMA-based approach for collision avoidance. This approach allows the end-devices clusters to transmit in parallel. Note that each cluster on uses its own frequency. However, within each cluster, the end-devices transmit in sequence. All end-devices are synchronized according to the GW clock. The end-devices are in sleep mode until they receive the next synchronization (SYNCH) message and update their clock. In addition, they optimize the mechanism in order to allow several end-devices of the same cluster to transmit in parallel. Simulations showed that this approach outperforms conventional LoRaWAN in terms of PDR.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 4 Waveform Design
### Linear Frequency-Modulated Pulse  
Linear frequency modulation (LFM), also known as chirp, is probably the most popular waveform used for pulse compression. It consists of sweeping the carrier wave by the  
Figure 5: Rectangular pulse (**left**) and zero cut of AF (**right**).  
Figure 6: Part of the Nested Barker Code (**left**) and zero cut of AF (**right**).  
frequency band \(B\) during the pulse duration \(T\). The complex envelope of a linear chirp is given by [43]:  
\[u(t)=\frac{1}{\sqrt{(}T)}rect(\frac{t}{T})e^{j\pi kt^{2}} \tag{3}\]  
where \(k=\pm\frac{B}{T}\) for a linear chirp and the signal indicates an increasing/decreasing sweep. The time-bandwidth product of the signal \(B\times T\) is the compression factor in relation to the rectangular pulse, that is, by increasing \(B\), the range resolution improves. Since ATLAS currently only supports amplitude modulation of the carrier, it is not possible to generate traditional chirp pulses because it requires phase modulation. In order to overcome this limitation, we developed an AM Chirp.  
The AM Chirp consists of designing a chirp signal at a much lower frequency and using it for modulating the carrier in amplitude. The amplitude modulation is given by:  
\[y(t)=A(t)\cos(2\pi f_{c}t) \tag{4}\]  
where \(A(t)\) is the modulating signal and \(f_{c}\) is the carrier frequency. In the case of the AM chirp, \(A(t)\) is given by:  
\[A(t)=\alpha+\beta\cos[2\pi(ct+f_{0})t],\;c=\frac{f_{1}-f_{0}}{T} \tag{5}\]  
where \(\alpha\) and \(\beta\) define the modulation parameters, \(f_{0}\) and \(f_{1}\) are the starting and final frequencies and \(T\) is the pulse duration.  
Figure 7 illustrates a simple case of AM chirp. The left side depicts a 1 kHz carrier modulated in amplitude by a chirp signal with \(\alpha=0.7\), \(\beta=0.3\), \(f_{1}=0\) and \(f_{1}=50\) Hz. After carrier demodulation, filtering, and signal reconstruction in the digital domain, the chirp waveform is successfully recovered as depicted on the right side of Figure 7.  
As said in Section 2, ATLAS has a waveform resolution of 10 ns and a receiver bandwidth of 80 MHz, which enables generation of AM chirps with high compression factors. As an example, Figure 8 shows a linear chirp with a compression factor of 1000 inside of the 3.3 ms rectangular pulse. For visualization purposes, only a small fraction of the waveform inside of the pulse is shown.  
A compression factor of 1000 was used to maintain a reasonable simulation time while showing the benefits of using this waveform for pulse compression. As can be seen on the right side of Figure 8, the range resolution now is approximately 500 m, which corresponds to an error of 0.05% at 1000 km of altitude.  
Figure 7: AM Chirp (**left**) and reconstructed chirp (**right**).  
This waveform requires a bandwidth of 300 kHz which is only a small fraction of the capabilities of the system to generate and receive it. This indicates that it is possible to synthesize waveforms with even higher compression factors.
## 2 Materials and Methods
### Research Parameters  
#### 2.6.1 Pain Sensitivity  
To evaluate pain sensitivity, a computer-controlled cuff algometer and protocols from Cortex Technology and Aalborg University (Aalborg, Denmark) were used. The cuff algometer is a reliable tool used for the assessment of cPPT, TS, and CPM [44,45] and is equipped with two 13 cm wide tourniquets placed on both legs and connected to an electronic 100 mm visual analogue scale (VAS). Participants used the VAS to rate pressure-induced pain intensity and were instructed to press a button to release the pressure. The cuff was inflated automatically (1 kPa/s rate) until a maximum pressure limit (100 kPa) was reached. cPPT was assessed by rating pain intensity continuously on the VAS from the first sensation of pain up until measuring the greatest level of pain by rising pressure that a subject was prepared to endure. cPPT was defined as the moment of transition between strong and painful pressure (i.e., first time VAS exceeds 0). The pressure value at the termination of pressure inflation was defined as the pressure tolerance threshold (PTT). TS was assessed by delivering ten stimuli, each set to the PTT, to the dominant leg. Each stimulus lasted for one second with a one-second interval between them. Participants continuously rated pain intensity using a VAS, and TS was calculated as the difference in pain intensity between the first and tenth stimuli. CPM was evaluated by applying a conditioning pressure stimulus to the non-dominant leg while measuring cPPT on the dominant leg. The conditioning stimulus was set at 70% of the pain tolerance threshold. The CPM effect was determined as the difference between the conditioned cPPT and the unconditioned cPPT.
# Lunar Satellite Constellations in Frozen Low Orbits
## 5 Candidate Constellations Performance and Stability
This implies that the optimal periodicity of frozen orbit corrections is about once per month. Using the same dynamical model parameters as those employed in the design of the frozen orbits (see Section 2), the numerical simulation of the station-keeping process for a typical frozen LLO has shown that a simple two-impulse correction maneuver works fairly well (see Figure 14), with the associated cost being on average from 10 to 30 m/s for different navigation uncertainty levels. Such an estimate correlates well with the 150 m/s annual station-keeping budget allocated for the Lunar Reconnaissance Orbiter mission [33]. The future extension of this work may include the results of Monte Carlo experiments to confirm the feasibility of long-term station keeping of frozen constellations.
# Voting-Based Document Image Skew Detection
## 4 Tests and Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Skew Detection Method** & **Execution Time** & **Average Error** & **Average Confidence** & **Uptime** \\ \hline _Frequency Domain (FFT-based)\({}^{\star}\)_ & 1451 ms & 0.218\({}^{\circ}\) & 77\% & 95\% \\ _Projection Profiling Row Align\({}^{\star}\)_ & 1640 ms & 0.553\({}^{\circ}\) & 77\% & 94\% \\ _Spatial Domain Lines (Hough-based)\({}^{\star}\)_ & 794 ms & 0.128\({}^{\circ}\) & 82 \% & 86\% \\ PCA-based\({}^{\star\star}\) & 2224 ms & 0.396\({}^{\circ}\) & N/A & N/A \\ Radon Transform\({}^{\star\star}\) & 800 ms & 0.290\({}^{\circ}\) & N/A & N/A \\ Nearest-Neighbor Clustering\({}^{\star\star}\) & 1822 ms & 0.484\({}^{\circ}\) & N/A & N/A \\ Vertical Projections\({}^{\star\star}\) & 3343 ms & 0.272\({}^{\circ}\) & N/A & N/A \\ Morphology-based\({}^{\star\star}\) & 2020 ms & 0.402\({}^{\circ}\) & N/A & N/A \\ _Best First Voting\({}^{\star}\)_ & 3908 ms & 0.111\({}^{\circ}\) & 94\% & 99\% \\ _Weighted Voting\({}^{\star}\)_ & 3908 ms & 0.266\({}^{\circ}\) & 89\% & 99\% \\ _Unanimous Voting\({}^{\star}\)_ & 3908 ms & 0.372\({}^{\circ}\) & 75\% & 99\% \\ \hline \hline \end{tabular}  
* **Notes: a) The execution times marked with \({}^{\star}\) were obtained using the article’s demonstrator application. b) The execution times (and the subsequent desked images) marked with \({}^{\star\star}\) were obtained using the CCS’s DocWorks 6.8 commercial solution for creating digital libraries [30]. As a result, they should be used only qualitatively. Also, since the confidence measures are not defined in this case and they were also considered when measuring the uptime, in these cases no uptime can be provided for comparison.  
\end{table} 
Table 1: Test results for the proposed skew detection system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Voting Method** & **Frequency Domain (\(\tau_{1}\))** & **Projection Profiling Row Align (\(\tau_{2}\))** & **Spatial Domain Lines (\(\tau_{3}\))** \\ \hline Best First & 0.25 & 1.05 & 1.21 \\ Weighted & 0.26 & 1.02 & 1.17 \\ Unanimous & 0.28 & 0.98 & 1.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Brute-force fine-tuning exponent search results.
# On the Lagged Diffusivity Method for the Solution of Nonlinear Finite Difference Systems
## 4 Solution Procedure
### Summary of the Solution Method  
The diagram in Figure 3 summarizes the equations and the systems that we used, illustrating how the partial differential equation is first transformed in a system of ordinary differential equations and then in a series of nonlinear algebraic systems, which are afterwards progressively linearized by the iterative methods.  
Figure 2: Starting vectors and tolerances of the iterative methods.  
Finally, we can write the algorithm summarizing the entire procedure as follows.  
```
0: initial condition \(\mathbf{u}|_{0}\) at \(t=0\); a tolerance \(\overline{\epsilon}\)
1:for\(n=1,2,...\)do
2: Initialize solution vector for lagged iteration: \(\mathbf{u}^{(0)}=\mathbf{u}|_{n-1}\)
3: Initialize lagged tol.: \(\epsilon_{1}=\epsilon_{0}\|\mathbf{F}(\mathbf{u}^{(0)})\|\)
4:for\(\
u=0,1,...\)do
5: Initialize linear solver tol.: \(\hat{\epsilon}_{1}=\max(\hat{q}\|\mathbf{F}_{\
u}(\mathbf{u}^{(\
u)})\|,\hat{\eta} \epsilon_{\
u+1})\)
6:for\(k=0,1,...\)do
7:for\(j=0,1,...\)do
8: Compute \((j+1)\)-th iterate \(\mathbf{u}^{(\
u+1,k+1,j+1)}\)
9: Compute residual \(\mathbf{r}_{j+1}\) of the linear system
10:if\(\|\mathbf{r}_{j+1}\|\leq\)then break
11: j = j+1
12:endfor
13: Compute Newton residual \(F_{\
u}(\mathbf{u}^{(\
u+1,k+1)})\)
14:if\(\|\mathbf{F}_{\
u}(\mathbf{u}^{(\
u+1,k+1)})\|\leq\epsilon_{\
u+1}\)then break
15: Update linear solver tol.: \(\hat{\epsilon}_{k+1}=\hat{\eta}\|\mathbf{F}_{\
u}(\mathbf{u}^{(\
u+1,k+1)})\|\)
16: k = k+1
17:endfor
18: Update vectors and matrices: find \(\mathbf{F}_{\
u+1}(\mathbf{u}^{(\
u+1)})\)
19:\(\
u=\
u+1\)
20:\(\epsilon_{\
u+1}=0.5\epsilon_{\
u}\)
21:if\(\epsilon_{\
u+1}\leq\overline{\epsilon}\)then return
22:endfor
23:\(n=n+1\)
24:endfor
```  
**Algorithm 1** Lagged diffusivity procedure  
Here, **break** means that we exit the current loop and go back to the outer one. On the other hand, **return** means that we exit from the entire procedure and the algorithm returns the final result. If the smoothness assumptions in Section 3.2 are satisfied (and if the inner linear solver converges), then the convergence of Algorithm 1 is ensured by Theorem 2 or by analogous theorems, depending on the chosen discretization.  
Figure 3: Systems arising from the discretization of the initial PDE and from the used iterative methods.
### Requirements of Underwater Communication
#### 1.4.2 Communication Range and Method  
In UWSN, the sensor nodes are placed in a group and need to communicate with each other; hence, the transmission range of the nodes becomes a significant factor in deployment [14] and communication method. In underwater communication, acoustic signals are most widely used, but it also has some drawbacks such as low propagation, low data rate, high latency, high delay, and causing harm to aquatic life.  
Figure 1: Architecture of underwater wireless sensor network.
# Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System
## 2 Vehicle in the Loop
### Sync  
This synchronization module allows the movement of a real vehicle in a virtual environment. For this purpose, the Global Positioning System (GPS) and Inertial Navigation System (INS) devices are mounted onto the test vehicle. The position measured by the GPS device is usually in the form of a spherical coordinate system, which is a coordinate system of longitude and latitude. However, the conversion to the Cartesian coordinate system is useful for intuitive system development, user acceptance and improving system connectivity. The algorithm is included in the synchronization module. In this paper, WGS84 (World Geodetic System 1984) coordinates obtained from GPS are converted to Transverse Mercator (TM) using the following method.  
\[x^{ECO}=(R_{N}+h)(\phi-\phi_{0})+\Delta X \tag{4}\]  
\[y^{ECO}=(R_{E}+h)(\lambda-\lambda_{0})+\Delta Y \tag{5}\]  
\(\left\{\phi_{0},\ \lambda_{0}\right\}\) is the central origin constant for longitude and latitude, and \(\phi\), \(\lambda\), \(h\) are the longitude, latitude, and altitude of the vehicle's position as measured by GPS. \(\left\{\Delta X,\ \Delta Y\right\}\) is the origin added values, (e.g., 2,000,000 and 6,000,000 set by the National Geographic Information Service in Korea). However, in this study, it is aimed to test a vehicle which is carried out in a narrow area, so that the added values of the origin can be modified to match the test location. The radius of curvature in the north/east direction is calculated using the major axis (\(R_{major}\)) and eccentricity (e) of the earth ellipse.  
\[R_{N}=\frac{R_{major}\left(1-\mathrm{e}\right)^{2}}{\left(1-\mathrm{e}^{2} sin^{2}\phi\right)^{3/2}} \tag{6}\]  
\[R_{E}=\frac{R_{major}}{\left(1-\mathrm{e}^{2}sin^{2}\phi\right)^{1/2}} \tag{7}\]  
The relationship between eccentricity (e) and flatness (\(f\)) is as follows. The major axis (\(R_{major}\)) and minor axis (\(R_{minor}\)) of the earth are used for the calculation.  
\[\mathrm{e}=\sqrt{1-\left(1-f\right)^{2}}=\sqrt{1-\left(1-\frac{R_{major}-R_{minor }}{R_{major}}\right)^{2}} \tag{8}\]  
The yaw of the vehicle (\(\psi^{ECO}\)) can directly use the data measured by the INS device.
## 5 Implementation of the Methods and Algorithms Used
### Receptor Location  
The model used requires a description of the exact location of the receptor at the entrance. It involves an introduction of:  
* The distance from the emitter (in the axis parallel to the wind direction)--\(x\) [m];
* The distance from the emitter (in the axis perpendicular to the wind direction)--\(y\) [m];
* The height for which the concentration of the substance is determined--\(z\) [m].