esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study on Real-Time Testing of an Isolated Microgrid  
### Test Bench for the Experimental Implementation  
This study employed MATLAB algorithms to perform real-time tests on an isolated MG at the University of Cuenca's Micro-Grid Laboratory in Ecuador [34]. The MG setup includes 15 kWp polycrystalline PV panels and a 44 kWh lithium-ion BESS. Figure 5 illustrates the configuration, which comprises solar panels (\(P_{PV}\)), a thermal diesel generator (GD) with delivered power (\(P_{CD}\)), a three-phase programmable load, and an electric vehicle charging station (EVCS) featuring a BYD electric vehicle (EV). A constant 20 kW load was applied to the programmable load to maintain system power balance, ensuring laboratory conditions where power fluctuations originate solely from intermittent PV injection. This method effectively manages the possibility of reverse power flow to the diesel generator when there is excess renewable energy, thus maintaining the safe and consistent operation of the test bench.  
Figure 4: Graphical representation depicting the inputs and output generated by the newly developed FLC.  
The laboratory includes a BYD T3 van featuring a 300 kW power output and a 100 kW electric motor, specifically the BYD-1814 TZ-XS-B35 KW model. The LiNIMnCo battery boasts a 50.3 kWh capacity and functions at a combined voltage of 438 VDC. It is structured with 12 modules housing 120 cells and includes a CCS2-type connector. For V2G emulation in a controlled experimental environment, a lithium-ion battery bank consisting of 11 cells with a voltage output of 642 VDC and a capacity of 44 kWh (Samsung model ELPT392-0002) was utilized. This setup is linked to a 50 kW power converter engineered to simulate a bidirectional charger, enabling the BESS to inject energy back into the grid. The setup supports G2V and V2G operation modes, facilitating efficient energy management and peak demand control. This precise control and testing environment provides a robust platform for validating V2G strategies. It allows emulation of various operational scenarios and accurate power flow management to evaluate system performance comprehensively. This evaluation is essential for demonstrating the practical feasibility of V2G concepts in isolated MG setups, showcasing benefits such as enhanced grid stability, increased renewable energy integration, and efficient ESS utilization.
## 4 NR Navigation System
### Interference-Free Correlation: ZPC  
First, we introduce the orthogonality condition of OFDM subcarriers: Subcarriers with different frequencies are integrated within a useful symbol period \(T_{u}\), i.e.,:  
\[\int_{0}^{T_{u}}c_{n,k}c_{m,l}^{*}e^{i2\pi k\Delta/t}e^{-\beta\pi l\Delta ft}dt =\begin{cases}0,&k\
eq l\\ T_{u}c_{n,k}c_{m,k}^{*},&k=l\end{cases} \tag{22}\]  
However, NR base stations append cyclic prefixes after OFDM modulation to prevent ISI. The CP extends the symbol duration to \(T_{\text{sym}}\). There exists interference when directly correlating OFDM signals containing CP, as depicted in Figure 4a; in other words, cross-correlation between different subcarriers becomes non-zero:  
\[\int_{0}^{T_{\text{sym}}}c_{n,k}c_{m,l}^{*}e^{i2\pi k\Delta ft}e^{-\beta\pi l \Delta ft}dt\
eq 0,\quad k\
eq l \tag{23}\]  
Using \(T_{\text{sym}}\) as the integration duration breaks subcarriers' orthogonality and induces mutual interference, degrading NR DLL tracking accuracy.  
This paper proposes an interference-free correlation: zero-padding correlation (ZPC). As depicted in Figure 4b, the ZPC process involves two aspects: (1) For every OFDM symbol, confining the integration duration to \(T_{u}\) preserves subcarrier orthogonality, ensuring that integration between different subcarriers equals zero; and (2) zero-padding the cyclic prefix (CP) intervals effectively eliminates intersymbol interference (ISI). The proposed correlation method preserves subcarrier orthogonality while eliminating the influence of CP and communication data.
# On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model
## 4 The Proposed MAPE-K Architecture  
Figure 4 provides an overview of the implemented MAPE-K architecture. The system orbits around the DSR, a reflection model [25] where all the descriptive information needed to carry out the correct execution of the missions is stored. Part of this information is perceptive information coming from the sensors and preprocessed by different agents. They provide the Monitor step of our proposal. Sometimes this information is raw data (e.g., battery level or the lower distance to obstacles), sometimes it is symbolic data, usually obtained from binary observations or from the simple evaluation of geometric data (e.g., a roll container has been detected and is close to the robot). In all cases, this information is descriptive.  
The Analyze step aims to monitor the DSR for recommending alternative actions to the decision making module. The scheme encodes non-functional properties (e.g., safety or punctuality) into Quality-of-Service metrics. Using context information and these metrics as inputs, a recommender quantifies what the best next action to be launched by the high-level decision maker should be. These recommendations are filtered by considering temporal causality and for avoiding erratic behaviours (e.g., cyclically categorizing a situation as safe or unsafe because the robot senses, sometimes yes and sometimes no, a person moving  
Figure 4: The proposed MAPE-K framework.  
Figure 3: Modification of the nominal course of action: the detection of people close to the robot causes the robot to abort the pickingUp task (see text).  
in front of it). Once filtered, the tentative actions are annotated in the DSR (we can see the node associated to these actions in the simplified views of the DSR in Figures 2 and 3). The deliberative module will take into consideration these recommendations, modifying the course of action when necessary. Both the process of estimating QoS metrics and obtaining recommendations are based on fuzzy logic.  
As the figure shows, several decision-making modules can coexist in our architecture. They use the information stored by the previous Monitor and Analyze steps for self-adapting the behaviour of the robot to the internal and external context. In our case, we have encoded the use cases into Behaviour Trees (BT) and these decision makers are the software modules in charge of managing their execution. These BTs can be extended with alternative branches for dealing with variability [26]. As illustrated in Figures 2 and 3, these BT Executors need to coordinate their activities, and they do that by sharing information in the DSR. Section 5 provides more details about our implementation and instantiation in the intralogistic domain.
# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 4 Experiment and Discussion
### Experiment Setting
#### 4.1.2 Baseline  
To evaluate the effectiveness of CMVAE, we compare it with both traditional clustering methods and a deep clustering approach. Since there are limited prior studies specifically focused on code strategy clustering, we adopt widely used clustering techniques as baselines to provide a comprehensive performance evaluation. We select three classical clustering methods and one deep clustering method as baselines:  
* K-means [17]: A widely used centroid-based clustering algorithm. It partitions data into clusters by minimizing the within-cluster variance.
* Spectral Clustering [18]: A graph-based clustering method. It leverages eigenvalues of the similarity matrix to perform clustering.
* MeanShift [19]: A density-based clustering algorithm. It finds clusters by shifting points towards regions of higher density.
* DEC [21]: A well-known deep clustering method. It jointly learns feature representations and cluster assignments through a self-supervised learning process.  
For a fair comparison, all baseline methods are applied to the same extracted features and pre-training phase as CMVAE. Specifically, the feature extraction process remains unchanged. The same multi-view representations obtained from AST-based structural analysis and CodeBERT embeddings are used. The only difference lies in the clustering stage, where CMVAE's deep clustering component is replaced with the selected baselines. By doing so, any differences in clustering performance arise from the clustering methods themselves rather than variations in feature extraction.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
### Contribution  
This paper is for researchers, practitioners, and readers interested in researching the utilization of deep-learning techniques in Arabic OCR and text correction, as well as those who want to rapidly learn from the recent works in this area and gain a holistic understanding of the topic.  
This paper's contributions include the following:  
* For the first time, a thorough review of 83 papers is presented, mainly from 2020 to 2023, to build a holistic review of Arabic OCR using deep learning and Arabic text correction. Moreover, this review elucidates the challenges to overcome for Arabic OCR. In addition, metrics for evaluating the accuracy of OCR systems are presented. Overall, this work presents the challenges, techniques, recent work analysis, and future directions for the use of deep learning for Arabic OCR and text correction.
* Unprecedented in scope, a critical analysis and deep discussion are presented on 25 recent works, from 2020 to 2023, that apply deep learning for Arabic OCR and Arabic text correction. The findings determine the current research frontier, trends, and limitations. This is a gap that no work filled, to the knowledge of the author, in the recent time frame defined above.
* Current challenges and future trends are extrapolated from the gaps that need to be filled in the works analyzed in this study and the possibilities of leveraging applications in other OCR systems for use in Arabic OCR, including datasets, models, and applications.
## 3 Results
### Dynamic Variation of Landing Zone  
The mean cross-sectional area and circumference dynamic variations of each plane during the whole cardiac cycle for the pre- and the post-CTs are shown in Figures 6 and 7. There was high variation in the RVOT and PPV cross-sectional area for the pre-CTs in the ten phases of the cardiac cycle, especially from 50% to 60% and 90% to 100%. For the straightened pre-CT model, the maximum and minimum mean annulus areas of the RVOT plane were 646 \(\pm\) 149 mm\({}^{2}\) (90%) and 272 \(\pm\) 122 mm\({}^{2}\) (50%), respectively, and the maximum and minimum cross-sectional area mean values of the PPV plane were 471 \(\pm\) 48 mm\({}^{2}\) (100%) and 266 \(\pm\) 101 mm\({}^{2}\) (60%), respectively. In addition, the post-CT RVOT annulus area differed greatly between 100% and 50% with areas of 1028 \(\pm\) 210 mm\({}^{2}\) and 435 \(\pm\) 144 mm\({}^{2}\), respectively. The dynamic variation trends for the circumference were similar to those for the cross-sectional area. The detailed mean differences (\(\pm\)standard deviation) of the cross-sectional area and the circumference are presented in Table S1. Figure 3: Pearson correlation scatterplot and Bland–Altman plot of minimum diameter and maximum diameter for the agreement between anatomical model and straightened model from pre-CT. LOA—limit of agreement. However, the variation in the BPS plane area in the post-CTs was small because the radial force of the implanted stent fixed the stent on the annulus between the BPS and the PA plane, allowing the PA to expand fully, which also led to an increase in the annulus ellipticity of the BPS plane (Table 2). The annulus of the RVOT plane has a more notable elliptical geometry than the other planes. The detailed ellipticity indices of the five planes during the ten cardiac phases are presented in Table S2. Figure 4: Pearson correlation scatterplot and Bland–Altman plot of cross-sectional area and circumference for the agreement between the anatomical model and straightened model from post-CT. LOA—limit of agreement. Figure 5: Pearson correlation scatterplot and Bland–Altman plot of minimum diameter and maximum diameter for the agreement between the anatomical model and straightened model from post-CT. LOA—limit of agreement. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Ellipticity Index**} \\ \hline  & **Pre-CT** & \multicolumn{3}{c}{**Post-CT**} \\ \hline RVOT & 0.55 \(\pm\) 0.08 & RVOT & 0.69 \(\pm\) 0.10 \\ BPV & 0.69 \(\pm\) 0.12 & BPS & 0.88 \(\pm\) 0.08 \\ Sinus & 0.89 \(\pm\) 0.06 & MPS & 0.88 \(\pm\) 0.09 \\ STJ & 0.90 \(\pm\) 0.06 & TPS & 0.84 \(\pm\) 0.10 \\ PA & 0.83 \(\pm\) 0.06 & PA & 0.86 \(\pm\) 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Annulus ellipticity index of five planes for pre-operative CT and post-operative CT. The ellipticity index was defined as minimum diameter/maximum diameter.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 5 Security Performance Analysis
### Statistical Attacks  
A statistical attack refers to the method by which the attacker, [PERSON], deciphers the password according to the statistical law of plaintext, ciphertext, and key. Being able to resist statistical analysis has become a basic requirement of modern cryptography. In practical applications, due to the number of encrypted OTFS symbols, the illegal eavesdropper, [PERSON], cannot launch statistical attacks through statistical changes in OTFS symbols. Therefore, the transmission mechanism we propose is sufficient to resist statistical attacks.
## 3 Results and Discussion
### Adsorption Properties
#### 3.2.4 Effect of Competitive Cations  
To study the effect of competitive cations, we conducted the experiment under an optimized pH (pH = 5) and adsorption time (i.e., 8 h), using different concentrations of common cations, as well as a combination of all four cations. Figure 8 shows that: the adsorption capacity decreased by approximately 0.5 mg/g in the presence of the competitive-cation-concentration increase.  
We observed no significant decrease in the adsorption capacity when the concentrations of competitive cations were lower than 100 mg/L. The adsorption capacity was decreased by 1.3 mg/g (20%) for the combination of all four cations with concentrations of 100 and 200 mg/L.  
Figure 8: Effects of common cations on the adsorption capacity (pH = 5, \(t\) = 8 h, \(T\) = 25 \({}^{\circ}\)C, \(C_{0}\) = 100 mg/L, \(n\) = 100 rpm).  
Figure 7: Effect of temperature on the adsorption capacity (pH = 5, \(t\) = 8 h, \(n\) = 100 rpm).
## 2 Materials and Methods
### Hardware, Software, and Methodology under Use Case
The Raspberry Pi, which runs a software based in GStreamer and ZeroTier, captures video from the Pi Camera through a CSI interface, and captures telemetry data from the PixHawk 2 Cube controller, through the GPIO interface. Additionally, a USB interface connects the Raspberry with the 4G modem. Finally, a PC acts as a GCS and receives the data through the VPN tunnel. For instance, it could play in real-time the video using GStreamer, VLC or MissionPlanner. In this practical case, the pilot is located near the ground control station, which is also acting as an EXO Data server, so there are only two VPN endpoints. As an extra interface, we enable a second gate to obtain access to the Raspberry through the ad-hoc WiFi network using the SSH protocol. This way, when it is necessary to setup the software before flight, the 4G connection does not have to be used. Finally, video frames are processed in the EXO Data server, where Faster-RCNN algorithms are located to detect and classify vehicles [21], to track its trajectories applying the IOU-tracker algorithm and the _intersection over union_ concept [22], and extracting the relative movement of the drone respect to the ground objects to determine its speed [23].
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 5 Experimental Validation
### Non-Intrusive Load Monitoring (NILM) Algorithms  
Three popular load disaggregation algorithms have been chosen to disaggregate the meter recovered data. The methods are: FHMM, Parson, and Weiss (more details about these algorithms are in [12]).  
These experiments are validated using the _F1 score_ and _accuracy_ metrics. The _F1 score_ is mainly focused on how well the system estimate the ON events. The _accuracy_ measures how many correct guesses there are in relation to the total amount of guesses. The F1 score and the accuracy are calculated based on Equations (14) and (15).  
\[F1=2\times\frac{precision\times recall}{precision+recall} \tag{14}\]  
\[accuracy=\frac{TP+TN}{TN+TP+FN+FP} \tag{15}\]  
\[where:\;precision=\frac{TP}{TP+FP},\;recall=\frac{TP}{TP+FN}\]  
_TP_ is the number of true positives, _TN_ is the number of true negatives, _FP_ is the number of false positives, and _FN_ is the number of false negatives.  
From the F1 score's equation, Figure 10 is built and shown the F1 score of detecting a TV by using [PERSON] and [PERSON]'s methods combined with gap filling algorithms (i.e., Envelop, Linear, and No gap filling). As shown in Figure 10a, Parson with Linear interpolation gap filling gives good F1 score especially when the error rate is higher than 5%. However, Figure 10b shows that [PERSON] algorithm with the gap filling algorithms do not give good results.  
Figure 9: Jitter power illustration.  
Based on the previous analysis, we have chosen _Linear_ interpolation gap filling with _Parson_ to reconstruct and disaggregate the SmartHG meter data. The results are stored in the DBCS.
## 3 Results
### Results of Gap Acceptance
#### 3.1.1 Gap Acceptance Under Full eHMI Penetration Condition  
A total of 5184 vehicle-pedestrian encounters were recorded for 12 participants, with each completing 432 trials (2 speeds \(\times\) 9 gap sizes \(\times\) 4 repetitions \(\times\) 6 eHMI information content conditions). Table 1 presents the fixed effects of the final model, showing significant effects of eHMI information content, gap size, and vehicle speed on gap acceptance.  
Larger gap sizes and higher vehicle speeds increased the likelihood of crossing. Compared to the three-level low-medium-high condition, participants were more likely to cross under single-level medium, single-level high, two-level low-medium, and two-level medium-high conditions. Significant negative interaction effects with gap size were observed for single-level medium, single-level high, and two-level medium-high conditions, indicating reduced sensitivity to gap size variations. This pattern suggests a higher like lihood of accepting smaller gaps (potentially unsafe) or rejecting larger gaps (missed opportunities), as illustrated in Figure 5.
## 2 Metasurface Design and Simulation
### Parameters Analysis  
The aim of this section is to investigate how different parameters influence the phase difference and 3 dB axial ratio of the dual-frequency wideband linear-to-circular polarization conversion. To achieve an optimal parameter, we optimized the values of the radial stub (\(r\)) and the width of the rectangular patch (\(w\)). A sequence of parametric simulations was performed to evaluate the influence of the parameters on the phase difference and 3 dB axial ratio of the converter unit cell when one parameter was changed while the other stayed the same. The calculated results of the parametric phase difference and 3 dB axial ratio of the dual-frequency wideband LTC polarization conversion are shown in Figures 5 and 6. The phase difference of a perfect linear-to-circular polarization converter can be calculated [7] from the succeeding equation:  
\[\Delta\varphi_{yx}=\varphi_{yy}-\varphi_{xy}=2k\pi\pm\pi/2 \tag{5}\]  
where \(k\) is an integer, and the negative \((-)\) and positive \((+)\) signs represent the RHCP and LHCP, respectively. According to our calculation results, changing the radius of the stub and width of the rectangular patch of a dual-wideband LTC polarization conversion does not influence the phase difference, as shown in Figure 5a,b. The phase difference is \(-90^{\circ}\)  
Figure 4: The calculated results of proposed converter under \(y\)-polarized incidence. (**a**) Linear-to-circular polarization conversion ratio (%) and (**b**) polarization extinction ratio (dB).  
and \(-450^{\circ}\) in the frequency range of 15-24.18 GHz and 25.44-38 GHz, respectively, which satisfies Equation (5), as demonstrated in Figure 5.  
The 3 dB axial ratio of a dual-frequency wideband is influenced by changing the parametric values of the radial stub and width of the rectangular patch, as shown in Figure 6a,b. The resonance peak of the 3 dB axial ratio of a dual-frequency wideband linear-to-circular polarization converter at the lower and higher frequency range was less than 3 dB after increasing the radius of the stub and width of the rectangular patch from 0.84 to 0.90 mm and 0.54 to 0.60 mm, as shown in Figure 6a,b. The parameter ranges \(r\) and \(w\) shown in Figure 6 were selected to follow the 3 dB axial ratio (0.84 mm-0.90 mm and 0.54 mm-0.60 mm). When the parameters are changed, it is interesting to see that the axial ratio exists below 3 dB. The preceding explanation explains why our proposed reflective metasurface excels in dual-wideband linear-to-circular polarization conversion.  
Figure 5: The simulated parametric result of phase difference (degree) at \(y\)-polarized incidence. (a) \(r\) (0.84 mm to 0.90 mm) and (b) \(w\) (0.54 mm to 0.60 mm).  
Figure 6: The simulated parametric results of axial ratio (dB) at \(y\)-polarized incidence. (a) \(r\) (0.84 mm to 0.90 mm) and (b) \(w\) (0.54 mm to 0.60 mm).
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 2 Related Work  
### Satellite Communication with 5G  
The satellite will be an important part of the next generation 5G communication network. Future satellite communication and terrestrial mobile communication systems will share the use of frequency resources and build a unified resource coordination platform [19; 20]. In terms of hardware devices, 5G uses the development experience and results of Phased Array Antenna and Space Division Multiplexing (SDM) technologies in the field of satellite communications and the satellite industry to take advantage of 5G standardization, scale, and chip to reduce the high-frequency phased array antenna and improve reliability and integration. In addition, in terms of network control, the satellite network can be merged with the 5G virtualized core network through software gradual upgrade, and finally integrated into the 5G network control cloud in the form of satellite communication dedicated network element software and plug-ins [21].  
The 5G context offers a promising opportunity to offer an integrated satellite/cellular service to 5G user equipment [1]. Earlier papers combined 5G and satellite communication through the Software Defined Network (SDN) technology [22; 23; 24]. [PERSON] and [PERSON] et al. applied 5G base station nodes equipped with MEC capabilities to a marine data acquisition and cartography system over Ship Ad-hoc Networks (SANET) [25]. [PERSON] et al. presented satellite network architecture based on mobile edge computing, which integrates network resources through a dynamic network virtualization technology and further designs a collaborative computing offload (CCO) model [26]. However, this work is more focused on the performance of task scheduling models, while our work specifies the design of the architecture.