esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Transition on Sifnos: An Approach to Economic and Social Transition and Development
## 1 Introduction
### Prior Literature Regarding Energy Transition in Insular Systems
Both of these subjects can be approached by following the three main pillars of research:  
* The adequate and appropriate awareness of the local communities regarding the energy transition process, its necessity and objectives, the risks undertaken and thepotentially required projects and, finally, the perspectives and the anticipated benefits for the local communities. All of these can be achieved through appropriate capacity building campaigns [37; 38]. * The minimization or even the elimination of any potential impacts from the implementation of the required energy transition projects on the natural environment and human activities. These can be achieved with the selection of optimum technologies, their appropriate sizing [39; 40; 41] and the selection of the installation sites following a predefined spatial planning, which will take into account, apart from the availability of RES potential, all existing restrictions and sensibilities of environmental, cultural, historical and economic interest. * The third pillar constitutes an essential prerequisite for the achievement of the aforementioned targets. This is the active involvement of the local community in the design, the implementation and the operation and management of the energy transition projects. The most effective way for citizens and local stakeholders to be involved in these is through energy cooperatives or communities [10; 42]. From the literature search presented here, the absence of articles which examine the energy transition holistically as a pillar for social and economic development in insular, isolated communities is revealed. This work, by presenting the overall effort is Sifnos, aims to introduce in the scientific literature an integrated pilot case for how rational and effective energy transition can be exploited in order to achieve the sustainable and fair development of entire insular communities, without inequalities or discrimination. This is achieved by introducing for the first time an integrated cluster of projects which operate in a combined mode, with a hybrid power plant being the basic component. New operation algorithms have been also introduced.
## 3 Measurement Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Process** & **Frequency** & **Resolution** & **RMS Phase** & **DC Power** & **Core Area** \\  & **(GHz)** & **(bits)** & **Error (deg)** & **(mW)** & **(mm2)** \\ \hline
[13] & \(\begin{array}{c}65\,\mathrm{nm}\\ \text{CMOS}\end{array}\) & 25.4–34 & 4 & 0.5–3 & 40 & 0.19 \\ \hline
[14] & \(\begin{array}{c}65\,\mathrm{nm}\\ \text{CMOS}\end{array}\) & 20.8–25 & 7 & 1.4 \({}^{\text{c}}\) & 6.6 & 0.134 \\ \hline
[15] & \(\begin{array}{c}65\,\mathrm{nm}\\ \text{CMOS}\end{array}\) & 27.5–32 & 6 & 0.87–2.43 & 1115.8 \({}^{\text{c}}\) & 21.2 \({}^{\text{c}}\) \\ \hline
[23] & \(\begin{array}{c}65\,\mathrm{nm}\\ \text{CMOS}\end{array}\) & 15–38 & 6 & 2–3.5 & 19.2 & 0.16 \\ \hline
[24] & \(\begin{array}{c}65\,\mathrm{nm}\\ \text{CMOS}\end{array}\) & 25–30 & 6 & \textless{}3.55 & 38 & 0.13 \\ \hline
[25] & \(\begin{array}{c}65\,\mathrm{nm}\\ \text{CMOS}\end{array}\) & 30–32.5 & 4 & 2.2–3.5 & 18 & 0.32 \\ \hline
[26] & \(\begin{array}{c}65\,\mathrm{nm}\\ \text{CMOS}\end{array}\) & 22–30 & 6 & 0.68–3 & 32 & 0.15 \\ \hline
[22] & \(\begin{array}{c}0.25\,\mathrm{\SIUnitSymbolMicro m}\\ \text{SiGe BiCMOS}\end{array}\) & 5–13 & 6 & \textless{}7.9 & 90 & 0.71 \({}^{\text{c}}\) \\ \hline
[27] & \(\begin{array}{c}0.18\,\mathrm{\SIUnitSymbolMicro m}\\ \text{SiGe BiCMOS}\end{array}\) & 15–35 & 4 & 4.2–13 & 25.2 \({}^{\text{d}}\) & 0.19 \\ \hline
[28] & \(\begin{array}{c}0.13\,\mathrm{\SIUnitSymbolMicro m}\\ \text{SiGe BiCMOS}\end{array}\) & 26–28 & 8 & \textless{}0.65 & 23 & 0.45 \({}^{\text{c}}\) \\ \hline This work & \(\begin{array}{c}0.18\,\mathrm{\SIUnitSymbolMicro m}\\ \text{SiGe BiCMOS}\end{array}\) & 20–30 & 6 & \textless{}2 & 19.5 & 0.165 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance summary and comparison with the state of the art. Figure 18: (a) Phase accuracy and (b) gain fluctuation at three temperatures.
## 3 Materials and Methods
### Methods
#### 3.1.2 Control Points  
To adjust the position of the images, control points were determined on the different surfaces and vertices of the elements to be studied using the GNSS receiver Leica's FLX100. The receiver was capable of receiving GPS, GLONASS, Galileo and Beidou signals and was connected to the National Reference Geodetic Network of GNSS Permanent Stations (ERGNSS) for on-the-spot topographic correction. It had a real-time planimetric accuracy of 2 cm and altimetric accuracy of 3 cm.  
Figure 4: Different flight plans to obtain photographs from drone images. In each image, you can see the situation of the images taken by the drone and the control points taken on the ground. (**A**) Cameras for North City-Wall Bastion with two orbit flights. (**B**) Orbital flight plans and cameras for Marble dish tsunami. (**C**) Grid flight plan and cameras for _Decumanus maximus_.  
For the treatment of the coordinates, we used the software TcpGPS for Aplitop, which allows the calculation of coordinates according to the ETRS89-UTM Reference System, the official system in Spain.  
The RMSE of each model was calculated from the positioning of the images and the control points, obtaining errors mostly lower than 1 m (Table 1) (Figure 4).
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 4 Experiments
### Experiment Results of Proposed Methods  
#### 4.4.1 Effects of Attention Mechanisms  
We added several advanced attention modules to the original YOLOX model for comparative experiments. SE [26] is a classic channel attention mechanism, CBAM [27] combines channel attention and spatial attention, NAM [49] is a lightweight mixed attention module which redesigns the implementation of channel attention and spatial attention in CBAM, ULSAM [50] is a lightweight subspace attention module, and ECA [37] is an improved version of SE. We added these attention mechanisms to the back of the backbone network so that the features processed by the attention mechanisms could be sent to PAFPN. We trained the model on the processed DOTA-v1.5 dataset and obtained the results shown in Table 1.  
It can be seen from Table 1 that the effect of ECA is the best among these attention mechanisms, the number of parameters added is the least, and the accuracy of the model can be improved without increasing the number of parameters. Thus, we finally chose the ECA attention mechanism to add to the YOLOX model.  
The results of our testing of the ECA module using three datasets are displayed in Table 2. After we used the ECA module, the detection accuracy of the model when used on these three datasets was improved and the speed of image detection was also slightly accelerated.
###### Abstract  
The apparent advantages of Multilevel Inverter (MLI) topologies in handling medium and high power with less loss in switching and lower harmonic distortion in an output voltage waveform makes it better than the conventional inverter. However, the MLI topologies utilize a large number of DC power supplies and power semiconductor devices. They also have a higher value of total standing voltage (TSV). Moreover, capacitor voltage balancing problems, self-voltage boosting inability, and complex control techniques require a relook and improvement in their structure. More recently, Switched-Capacitor Multilevel Inverter (SCMLI) topologies have been proposed to overcome the shortcomings of MLIs. In this paper, a generalized structure for a single-phase switched capacitor multilevel inverter (SCMLI) with self-voltage boosting and self-voltage balancing capability is proposed. A detailed analysis of a general structure of SCMLI is presented. The comparative analysis of the structures is carried out with recently reported topologies to demonstrate superiority. An optimized low-frequency modulation controls the output voltage waveform. The simulation and experimental results are included in the paper for single-unit symmetric (9-level voltage) and asymmetric (17-level voltage) configurations.  
engry storage; total harmonic distortion; switched-capacitor multilevel inverter (SCMLI); total standing voltage +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions
### Judgment of Hazardous Solid Waste Properties of Petrochemical Excess Sludge
#### 3.1.2 Analysis of Flammability, Corrosivity and Reactivity of Petrochemical Excess Sludge  
The petrochemical excess sludge samples were tested for corrosiveness, flammability, and reactivity according to the requirements of Chinese National standards GB 5085.1-2007 [42], GB 5085.4-2007 [43], and GB 5085.5-2007 [44], respectively. The results of the sample test showed that the samples were not reactive, flammable, and corrosive.
# PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems
## 6 Results  
This section is divided into two subsections. Our first goal was to verify the method for estimating the \(W_{klm}\) coefficients. We, therefore, simulated an artificial imaging system and tested the convergence of the introduced algorithm to find proper \(W_{klm}\) parameters. The second subsection of results involves modeling real imaging systems with the results of different orders of the Zernike polynomials. The input conditions, such as SNR > 20 dB (peak to noise) of all considered PSFs, the number of calibration images and FWHM, mentioned in Section 5 have to be taken into account before acquiring the calibration images and applying the algorithm of the PSF estimate.
## 2 Background Theories
### Augmented Dickey-Fuller (ADF) Test  
An ADF test is a fundamentally statistical significance test for determining whether time series is stationary or non-stationary. The ADF test is suitable for testing the stationarity of a time series because it belongs to a category of tests called the Unit Root Test. It exists in a time series of \(\rho\) values calculated by (13) where \(Y_{t}\) denotes the value of the time series at time \(t\), \(X_{t}^{\prime}\) denotes exogenous variables \(\epsilon_{t}\) denotes a white noise, and \(\rho\) and \(\delta\) denotes estimated parameters.  
\[Y_{t}=\rho Y_{t-1}+X_{t}^{\prime}\delta+\epsilon_{t} \tag{13}\]  
If \(|\rho|\geq 1\), \(Y\) is a non-stationary series while \(|\rho|<1\), \(Y\) is a stationary series, as a result, the stationarity hypothesis can be determined by determining if the total value of \(\rho\) is strictly smaller than \(1\).  
The ADF test expands the Dickey-Fuller test (DF) equation to include high order regressive process in the model. The DF test is a unit root test that tests the null hypothesis. The standard DF test is carried out by subtracting from both sides of the Unit Root Test from (14) where \(\alpha\) denotes a constant equal \(\rho\), \(\beta\) denotes a coefficient.  
\[\Delta Y_{t}=\alpha Y_{t-1}+X_{t}^{\prime}\delta+\epsilon_{t}1 \tag{14}\]  
The null and alternative hypotheses are evaluated using the conventional \(t\)-ratio for \(\alpha\). The ADF equation, which is a DF equation but includes a high-order regressive process in the model, can be calculated as (15),  
\[\Delta Y_{t}=\alpha Y_{t-1}+X_{t}^{\prime}\delta+\beta_{1}\Delta Y_{t-1}+ \beta_{2}\Delta Y_{t-2}+\ldots+\beta_{p}\Delta Y_{t-p}+v_{t} \tag{15}\]  
The \(t\)-ratio is then used to test the same null hypothesis as the DF test. Assuming the null hypothesis involves the presence of unit root, that is \(\alpha=\rho-1\), the \(\rho\)-value derived from the equation (13) should be greater than the significance level and the statistical test value be greater than the critical value in order to reject the null hypothesis. As a result, the series is inferred to be non-stationary [46; 47].
## 4 Cooperative Decision-Making in a Multi-Vehicle Cooperative Task
### Problem Formulation
It reduces the vehicle to a circle with the center of the vehicle as the center and the diagonal of the vehicle as the diameter. When two vehicles are tangent to each other, a collision is considered to have occurred. The collision reward \(r_{c}\) is defined to penalize the occurrence of a collision and reward the successful pass, expressed as follows. \[r_{c}=\left\{ \begin{array}{l}-1,\text{if a collision happens},\\ 1,\text{if all CAVs pass successful},\\ 0,\text{otherwise}\end{array} \right.\] (2). For safety, the vehicle is penalized when the minimum time headway \(t_{h}\) with other vehicles is less than desired time headway \(t_{d}\). In this paper, \(t_{d}\) was set to 2 s. Our study scenario was a mixed traffic scenario where the HDVs and CAVs coexisted. The uncertain behavior of the HDVs prevented us from directly calculating the time to  
Figure 2: Schematic diagram of the decision control for multiple intelligent vehicles. collision (TTC). Therefore, within a prediction horizon \(T_{h}\), all the vehicles' trajectories are predicted. The trajectories of the CAVs are predicted via the execution of the current action, while the HDVs are estimated from the IDM model. At each step of the prediction, the collision of vehicle \(i\) is detected according to the body circle model. If a collision occurs at step \(t\), the time headway \(t_{h}\) is determined according to \(t_{h}=t/f\), where \(f\) represents the control frequency of the vehicle. When no collisions are detected, \(r_{h}\) is set to 1. Thus, the headway reward \(r_{h}\) is defined as follows. \[r_{h}=\left\{ \begin{array}{ll}\ln\frac{t_{h}}{t_{d}},\text{if collisions are detected},\\ 1,\text{ otherwise}\end{array} \right..\] (3) (3) (3) (3) (3) To pass though the intersection safely and effectively, the speed should be considered as appropriate and is punished too low. The speed reward is defined as follows. \[r_{s}=\min\biggl{(}\frac{v_{t}-v_{min}}{v_{max}-v_{min}},1\biggr{)},\] (4) where \(v_{t},v_{\min},v_{\max}\) are the current speed and the minimum and maximum speeds to be rewarded. (4) For the vehicle to make decisions under the rules demonstrated in Section 3.2 the rule reward \(r_{r}\) is set to 1 if the rule is obeyed, otherwise \(r_{r}=-1\). Based on these definitions above, the total reward for the \(v_{i}\) at time step \(t\) is defined as follows. \[r_{t}^{i}=\omega_{c}r_{c}+\omega_{h}r_{h}+\omega_{s}r_{s}+\omega_{r}r_{r},\] (5) where \(\omega_{c},\omega_{s},\omega_{h}\), and \(\omega_{r}\) are all weighting factors that account for each part of the reward.
# Experimental and Numerical Investigation of the Mesoscale Size Effect in Notched Woven Composites
## 2 Materials and Methods
### Data-Driven Calibration of Material Properties
#### 2.3.1 Optimization of the Elastic Properties  
The elastic material properties of the tows were determined by performing a tensile test on an unnotched specimen. Figure 4 shows a schema of the optimization algorithm used to determine the properties of the elastic material model.  
Figure 4: Flowchart of the optimization algorithm.  
The algorithm is based on a surrogate optimization solver from MATLAB global optimization toolbox, which was chosen for its ability to converge to a global minimum [24]. The objective was to identify the material parameters by minimizing the difference between the surface strains measured with DIC and those computed in the uppermost integration points in the FE model. DIC and FEM strains were linearly interpolated over a common grid. The objective function was formulated as:  
\[C=\sum\left(\left(\frac{\varepsilon_{xx}^{FE}-\varepsilon_{xx}^{DIC}}{RMS \big{(}\varepsilon_{xx}^{DIC}\big{)}}\right)^{2}+\left(\frac{\varepsilon_{yy}^ {FE}-\varepsilon_{yy}^{DIC}}{RMS\big{(}\varepsilon_{yy}^{DIC}\big{)}}\right)^{ 2}+\left(\frac{\varepsilon_{xy}^{FE}-\varepsilon_{xy}^{DIC}}{RMS\big{(} \varepsilon_{xy}^{DIC}\big{)}}\right)^{2}\right) \tag{1}\]  
where \(\varepsilon_{xx}\), \(\varepsilon_{yy}\), and \(\varepsilon_{xy}\) are transverse, longitudinal, and plane shear strain fields, respectively; the FE and DIC superscripts indicate numerical and experimental/DIC grid-interpolated strain fields; RMS is the root mean square value of the strain field; and \(\sum\) indicates the summation over all the strain field grid points. To obtain better results, it is useful to weigh the strain field differences over their respective experimental RMS values, since strains in the loading direction were an order of magnitude greater than those in the transverse direction. During the optimization stage, the model was loaded imposing the experimental load curve. For the validation and stress concentration study, this was changed to a constant displacement rate, to better duplicate the experimental tests.
## 3 System Implementation Based on LPA Synchronization Algorithm
### Multi-h CPM Receiver Implementation
#### 3.2.3 State-Space Partitioning (SSP) Unit  
The SSP algorithm is a decision feedback scheme that could reduce the trellis state according to the partitioning maps. We partition the cumulative phase states of the original  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Indication** \\ \hline \(\mathbf{M}_{n}\) & Surviving path index vector for all 16 phase states, and \(n\) is the symbol index \\ \(\alpha_{n}\) & Global winning state index \\ \(\hat{\phi}_{s}(nT)\) & Surviving partitioned phase \\ \(\theta_{n}^{\mathbf{x}}\) & Modified partitioned phase with respect to \(\alpha_{n}\) \\ \(\phi_{t}(nT)\) & Tilted phase \\ \(\gamma_{\varphi 1},\gamma_{\varphi 2}\) & Coefficients of the LF for carrier synchronization \\ \(\gamma_{\tau 1},\gamma_{\tau 2}\) & Coefficients of the LF for for timing synchronization \\ \(\phi_{n}\) & Estimated carrier phase \\ \(\tau_{n}\) & Estimated timing phase \\ \(v_{n}\) & Symbol clock phase increment \\ \(r(\mathbf{K}t_{s})\) & Received signal sampled with \(T_{s}\) interval and \(k\) is the sampling interval index \\ \(s(kT_{s})\) & Modulated signal sampled with \(T_{s}\) interval \\ \(\mathbf{Z}_{0,n}\) & MF output vector with the elements calculated by (27) for various branch \\ \(\text{sign}(\mathbf{b})\) & Sign vector for various branch calculated by (24) and (34) \\ \(\left\{\mathbf{Z}_{0,n}^{\prime}\right\}\) & Timing error signal vector with SLPA from (33) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of symbols.  
trellis from \(p=16\) into \(p^{\prime}=4\). Thus, the branch metrics calculated by the MF banks have to be compensated by the estimated surviving phase \(\hat{\phi}_{s}(nT)\), which is obtained from the previous surviving phase \(\hat{\phi}_{t}((n-1)T)\) and the modified partitioned phase \(\theta_{n}^{*}=2\pi h_{n}\hat{x}_{n}\) with respect to the estimated \(\hat{x}_{n}\).
# Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application
###### Abstract  
This paper presents improved digital shearing speckle pattern interferometry based on Rochon Prism. The advantage of this method is that it avoids the shadow noise caused by transmission light, which improves the shearography accuracy. By reducing the angle of divergence and coordinating the measuring distance and the precision coordinate, the beam-splitting path of the interference optical path is improved. Experimental results prove that the improved method contributes to the practical and instrumental application of shearography.  
speckle; shearography; Rochon Prism; Wollaston Prism +
Footnote †: journal: Applied Sciences
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Safety Training and Education
#### 4.2.1 Interactive Learning Platforms  
LLMs can power interactive learning platforms that offer personalized training programs tailored to individual crew members' needs [123]. By assessing a trainee's current knowledge level, the model can generate customized lesson plans that focus on areas requiring improvement. These platforms can simulate realistic maritime scenarios, allowing trainees to practice responses to various situations in a risk-free environment.  
Through natural language interactions, trainees can engage with the LLM to ask questions, seek clarification, and receive immediate feedback. This interactive approach enhances engagement and retention of information, leading to more effective training outcomes. The use of LLMs in training also facilitates remote learning, which is particularly beneficial for crews dispersed across different locations [124].
## 3 Hysteresis Curve Characteristics of GRS
### Effect of D-W Cycles on the Hysteresis Curve Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(k_{\text{ave}}\)} & \multicolumn{1}{c}{\multirow{2}{*}{**0**}} & \multicolumn{1}{c}{\multirow{2}{*}{**1**}} & \multicolumn{1}{c}{\multirow{2}{*}{**2**}} & \multicolumn{1}{c}{\multirow{2}{*}{**3**}} & \multicolumn{1}{c}{\multirow{2}{*}{**4**}} & \multicolumn{1}{c}{\multirow{2}{*}{**5**}} & \multicolumn{1}{c}{\multirow{2}{*}{**6**}} & \multicolumn{1}{c}{\multirow{2}{*}{**7**}} & \multicolumn{1}{c}{\multirow{2}{*}{**\(\Delta k=k_{\text{max}}-k_{\text{min}}\)**}} \\ \cline{2-2} \cline{11-19}  & **N1** & **N3** & **N1** & **N3** & **N1** & **N3** & **N1** & **N3** & **N1** & **N3** & **N1** & **N3** & **N1** & **N3** & **N1** & **N3** & **N1** & **N3** \\ \hline C0 & 397 & 652 & 399 & 614 & 429 & 620 & 391 & 580 & 356 & 530 & 267 & 472 & 275 & 436 & 267 & 418 & 162 & 233 \\ C10 & 277 & 456 & 291 & 480 & 276 & 442 & 268 & 431 & 285 & 432 & 229 & 392 & 380 & 216 & 352 & 75 & 127 \\ C20 & 280 & 535 & 301 & 463 & 230 & 409 & 333 & 491 & 290 & 468 & 237 & 358 & 230 & 390 & 265 & 392 & 102 & 176 \\ C30 & 189 & 457 & 213 & 392 & 220 & 408 & 222 & 377 & 220 & 392 & 209 & 359 & 215 & 386 & 220 & 364 & 33 & 97 \\ \hline \hline \end{tabular} Note: \(k_{\text{ave}}\) means the average of \(k\) under different numbers of loads. N1 means \(N=1\), 2, 3,..., 9; N3 means \(N=1000\), 2000, 3000,..., 9000.  
\end{table} 
Table 4: Variation of major axis gradient \(k_{\text{ave}}\) under different D–W cycles. Figure 10: Variation of hysteresis curve parameters with D–W cycles (C30). More fine particles wrap the coarse particles, resulting in a more stable skeleton structure. As a result, the variation of parameters is stabilized.
## 3 System Model and Problem Formulation
### Energy Consumption Model  
The energy consumed during node deployment optimization mainly comes from node rotation, information transmission, and node moving. The energy consumption generated by moving is much greater than that by the first two. Therefore, we can approximate the energy consumption of the deployment optimization process by measuring the moving distance. The initial energy of both stationary and mobile sensors in the network is \(E\). The energy consumption generated by moving a unit distance is \(e_{m}\). The mobile energy consumption of nodes can be expressed as:  
\[E_{M}=e_{m}\times d(l_{i},l_{o})=e_{m}\times\sqrt{\left(x_{o}-x_{i}\right)^{2 }+\left(y_{o}-y_{i}\right)^{2}} \tag{4}\]  
where \(l_{i}\) indicates the initial location of the mobile node, and \(l_{o}\) indicates the redeployment location.