esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Architecture
### Deep Learning for LED Detection  
In OCC system, RoI algorithms are well-established. Object and feature-based detection approaches are used in most RoI methods with real-time processing for object detection. As previously noted, with the rolling-shutter effect, LEDs are shown in images  
Figure 2: Cyclic-prefix in RS-OFDM symbols.  
represented by the intensity of LED strips corresponding to the OFDM waveform. Each image includes many strips, which make problems for RoI detection, particularly concerning the mobility effect. Deep learning neural networks are well-established methods for computer vision applications, e.g., object detection, image classification, localization, and image reconstruction. Convolution neural networks have emerged as promising candidates for deep learning-based computer vision applications. The CNN-based YOLO algorithm is a state-of-the-art, real-time object detection system. This paper proposes the customization and training of YOLO models for LED detection and tracking, considering the rolling-shutter and mobility effects.  
To verify the performance of our approach, the experiment dataset was collected using real scenes. We recorded daytime and nighttime video footage with the mobility effect and generated 2500 blurry and clear images at different exposure times. Using these images as the dataset, we labeled images and trained the YOLOv5 model, which was modified with 5/7 convolution layers. Only one detection class was included. The corresponding number of filters in the final number of convolution layers was 38. The YOLOv5 model's average loss was about 0.09 after 10,000 training epochs with a low processing time of 0.0985 s for each epoch (NVIDIA GeForce GTX 1050).
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 1 Introduction
This platform addresses the limitations in existing monitoring systems, including single-source data, poor monitoring quality at night, and fragmented heterogeneous images [19; 20; 21]. The platform includes a 3 DRS sub-platform, the integration of GNSS and video monitoring data, and a nighttime supplemental lighting system. The platform enables the accurate identification of deformation areas, including surface cracks, depressions, and elevation changes. By fusing GNSS monitoring data, intelligent vision observation, and IoT data, this platform facilitates the multi-dimensional analysis of surface deformation causes.
## 4 Results of Numerical Simulations and Conclusions
### The Case of Having Three Scatterers
(**a**) SVD with limited-view data; (**b**) imaging result with limited view data; (**c**) SVD with true data; (**d**) imaging result with true data; (**e**) SVD with learning data; (**f**) imaging result with learning data. For the cases of two and three scatterers, it is possible to develop a learning model that constructs the full view data from the limited view data with a small mean square error and similar data distribution compared to the ground truth data. In addition, the distributions of singular values calculated from the data output from the developed model or the results of imaging with the subspace migration algorithm were similar to the ground truth data, so the locations of the scatterers could be found. 2. Even if the location of the scatterers could not be found when imaging with subspace migration algorithm using the limited view data, the location of the scatterers could be found as a result of imaging using the full view data output from the developed model (see Figures 10 and 14). 3. As shown in Figure 15, the error distribution of ground truth data and full view data output from the developed model shows that the error between group 1 and group 4 is relatively small. Since the full view data output from this learning model is from the limited view data of group 1, it can be seen that the correlation between the data of group 1 and the data of group 4 is high. 4. Figures 5 and 7 show that the data distribution of the MSR matrix has some symmetry, and the full view data output from the artificial neural network maintains its symmetry. 5. Attempts to solve the inverse scattering problem using artificial intelligence mainly used the image for training. However, this study shows that it can be efficient to use data collected from transmission and receiving antennas directly for training artificial intelligence.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 4 EGR Protocol
### Cluster Formation
The normal nodes are more resource constrained; thus, sink nodes can play this role better
* Information collection through the sink node is more power efficient than collecting and spreading this information to each node in the network. * Our cluster election process using EG requires nodes in the clusters to be divided based on their remaining energy. This is performed using the sink node. * Finally, using a centralized scheme, it is possible to relieve the processing load from the sensors and network lifetime. Figure 5 shows the cluster formation procedure step by step for better understanding. To form a cluster, first, the sink creates an empty cluster by associating an un-clustered sensor node. The node will be the first member in the cluster. Subsequently, the sink finds the CMs by computing the overlapped area and comparing it with the threshold. For example, if the triangle created by the FoV of a sensor node has an area of 92 units, the threshold value \(T\) can be set to the half of the area, which is 46 units. Any two sensor nodes having overlapped FoV more than that value would fall into the category of being in the same cluster. Figure 6 shows the overlapped FoVs of some sensor nodes. Nodes \(A\) and \(B\) will be in the same cluster because of their overlapped FoV crossing the threshold \(T\). This is also true for nodes G and H. Not only nodes C and D but also nodes E and F will not be in the same cluster for their overlapped FoV being smaller than threshold \(T\). Figure 5: Cluster formation process in EGR. Figure 6: Overlapped FoVs of sensor nodes in a network area.
## 2 Materials and Methods
### Characterization of the Sol-Gel Silica Materials and of Thin Films
#### 2.3.2 Fourier-Transform Infrared Spectroscopy (FTIR)  
The sol-gel silica materials (obtained as powders) were characterized using a Jasco FT-IR 6300 instrument (JASCO Int. Co., Ltd., Tokyo, Japan). The attenuated total reflectance (ATR) accessory of a diamond crystal was used. Data were collected in the wavenumber interval of 400-4000 cm\({}^{-1}\) (at room temperature, 30 scans, resolution of 4 cm\({}^{-1}\)).
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 6 Ablation Study
### Ablation Results
#### 6.2.3 Triplet Loss Application  
Implementation Details: Variant D incorporates a triplet margin loss compared to the previous Variant C. This loss function encourages the model to distinguish between the anchor, positive, and negative patches in terms of style representation, thereby enhancing the model's ability to disentangle style and content features effectively. The triplet loss focuses on improving local style differentiation while maintaining content integrity.  
Results: With the inclusion of triplet margin loss, there was a significant improvement in both style and content accuracy. Acc (S) increased from 42.43% to 53.72%, while Acc (C) improved substantially from 78.18% to 96.91%. The perceptual similarity to target fonts, as measured by LPIPS, improved to 0.182, and the structural similarity, as measured by SSIM, rose to 0.578. The FID also improved, decreasing from 117 to 98. These results indicate that triplet loss effectively enhances both style disentanglement and overall fidelity without compromising content accuracy.
## 3 Results and Discussion
### Spectral Data for 2,4-Dtu and the Metal Complexes  
The spectral data for 2,4-ditiouracil and metal complexes are given in Table 6. Due to the bidentate coordination of 2-Tu with Cu through its sulfur and oxygen atoms, the stretching band of the C=S bond was shifted in the Raman and ATR spectra of the Cu complex, as compared to our previous (1217 cm\({}^{-1}\)) [31].  
In summary, as can be seen from the Raman and ATR spectra of 2,4-Dtu, there is a band at 3080 cm\({}^{-1}\) indicating v(C-H). Also, there is a shoulder at 3096 cm\({}^{-1}\) in the ATR spectrum and a second band in the Raman spectrum at 3097 cm\({}^{-1}\). In the case of the Au complex, Raman and ATR bands for v(C-H) were found at 3066 cm\({}^{-1}\) and 3064 cm\({}^{-1}\), while the Raman and ATR bands for the Cu complex were at 3018 cm\({}^{-1}\) and 3078 cm\({}^{-1}\). The Raman and ATR bands at 1547 cm\({}^{-1}\) and 1565 cm\({}^{-1}\) were for v(C=C) stretching in 2,4-Dtu. Similarly, the v(C=C) bands at 1544 cm\({}^{-1}\) and 1539 cm\({}^{-1}\), as well as at 1546 cm\({}^{-1}\), were found for the Au and Cu complexes, respectively. Indications for a C=S bond in 2,4-Dtu and Au complex were the Raman and ATR bands in the range 1252-1254 cm\({}^{-1}\).  
The structures of all the compounds were verified by spectral data (see Supplementary Materials, Figures S1-S21).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**2,4-Dtu**} & \multicolumn{2}{c}{**Au Complex**} & \multicolumn{2}{c}{**Cu Complex**} \\ \multicolumn{1}{c}{**Raman, cm\({}^{-1}\)**} & \multicolumn{1}{c}{**ATR, cm\({}^{-1}\)**} & \multicolumn{1}{c}{**Raman, cm\({}^{-1}\)**} & \multicolumn{1}{c}{**ATR, cm\({}^{-1}\)**} & \multicolumn{1}{c}{**[PERSON], cm\({}^{-1}\)**} & \multicolumn{1}{c}{**ATR, cm\({}^{-1}\)**} \\ \hline
3097(v(C-H)) & 3168 (v(N(H))) & 3066 (v(C-H)) & 3100 & 3101 & 3386 (v(O-H)) \\
3080 (v(C-H)) & 3096 ((v(C-H)) & 2902 & 3064 (v(C-H)) & 3018 (v(C-H)) 2911 & 3078 (v(C-H)) \\
3058 & 3080 (v(C-H)) & 1604 & 1603 & 1546 (v(C=C)) 1480 & 2996 \\
1605 & 2994 & 1544 (v(C=C)) & 1539 (v(C=C)) & 1412 & 2908 \\
1547 (v(C=C)) & 2923 & 1411 & 1520 & 1384 & 2362 \\
1491 & 2894 & 1363 & 1486 & 1305 & 1654 \\
1425 & 2720 & 1289 & 1445 & 1283 & 1600 \\
1359 & 1921 & 1253 (v(C=S)) & 1407 & 1193 & 1546 (v(C=C)) \\
1357 & 1695 & 1219 & 1363 & 1148 & 1481 \\
1254 (v(C=S)) & 1673 & 1189 & 1326 & 1096 & 1433 \\
1230 & 1610 & 1147 & 1285 & 1014 & 1380 \\
1189 & 1565 (v(C=C)) & 1120 & 1252 (v(C=S)) & 978 & 1366 \\
1118 & 1486 & 1099 & 1226 & 820 & 1307 \\
1076 & 1411 & 978 & 1189 & 804 & 1282 \\
984 & 1368 & 967 & 1122 & 681 & 1185 \\
965 & 1358 & 816 & 1077 & 544 & 1159 \\
858 & 1319 & 715 & 1013 & 449 & 1148 \\
683 & 1252 (v(C=S)) & 683 & 978 & 420 & 1095 \\
611 & 1230 & 611 & 966 & 388 & 1012 \\
461 & 1211 & 542 & 949 & 269 & 950 \\
444 & 1123 & 461 & 932 & 246 & 817 \\
399 & 1099 & 444 & 857 & 205 & 754 \\
387 & 1076 & 399 & 803 & & 707 \\
229 & 984 & 387 & 789 & & 679 \\
965 & 269 & 761 & & & 613 \\
858 & 246 & 735 & & & \\  & 820 & 230 & 706 & & \\  & 792 & & 680 &
## 3 DC-Side Capacitance Analysis
### Effect of Unbalanced Current on Voltage Fluctuations in the DC-Side Capacitor  
For the three-phase, four-wire inverter, when considering the effect of the unbalanced current \(\dot{I}_{\rm O}\) on the DC-side capacitor voltage, the DC-side voltage \(U_{\rm dc}\) can be considered to be inactive, and the equivalent circuit of the system is shown in Figure 11.  
Figure 11: Equivalent circuit diagram when charging and discharging a capacitor with an unbalanced current.  
Let the three-phase unbalanced currents be represented by \(\dot{I}_{\rm a}\), \(\dot{I}_{\rm b}\) and \(\dot{I}_{\rm c}\), then the midline currents as follows:  
\[\dot{I}_{\rm o}=\dot{I}_{\rm a}+\dot{I}_{\rm b}+\dot{I}_{\rm c} \tag{14}\]  
Neglecting the higher harmonics and DC components of the system and considering only the fundamental components, the capacitor voltage amplitude is as follows:  
\[U_{\rm cm}=\frac{\sqrt{2}I_{\rm o}}{2\omega C} \tag{15}\]  
In Equation (15), \(I_{\rm o}\) is the amplitude of the unbalanced current.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
###### Abstract  
In this paper, we propose an efficient COordinate Rotation DIgital Computer (CORDIC) iteration circuit design for Light Detection and Ranging (LiDAR) sensors. A novel CORDIC architecture that achieves the goal of pre-selecting angles and reduces the number of iterations is presented for LiDAR sensors. The value of the trigonometric functions can be found in seven rotations regardless of the number of input N digits. The number of iterations are reduced by more than half. The experimental results show the similarity value to be all 1 and prove that the LiDAR decoded packet results are exactly the same as the ground truth. The total chip area is 1.93 mm \(\times\) 1.93 mm and the core area is 1.32 mm \(\times\) 1.32 mm, separately. The number of logic gates is 129,688. The designed chip only takes 0.012 ms and 0.912 ms to decode a packet and a 3D frame of LiDAR sensors, respectively. The throughput of the chip is \(8.2105\times 10^{8}\) bits/sec. The average power consumption is 237.34 mW at a maximum operating frequency of 100 MHz. This design can not only reduce the number of iterations and the computing time but also reduce the chip area. This paper provides an efficient CORDIC iteration design and solution for LiDAR sensors to reconstruct the point-cloud map for autonomous vehicles.  
Keywords:autonomous car; CORDIC; LiDAR; point cloud +
Footnote †: _Keywords_: autonomous car; CORDIC; LiDAR; point cloud
## 3 Materials and Methods
### Feature Vector Construction
Hour: * Daily Cycle: Shown in the ozone concentration pattern, there are usually lower levels in the early morning because the sun has not risen or is low in the sky, reducing the intensity of UV radiation needed for ozone formation; ozone peaks during the afternoon when sunlight is most intense and temperatures are highest; in the evening, the rate of ozone production decreases, but the loss processes are slower. UV radiation needed for ozone formation changes throughout the day, affecting the intensity of UV radiation reaching the surface. * Emissions Patterns: Human activities, such as traffic and industrial operations, have specific hourly patterns. * Mixing Layer Depth: The depth of the atmospheric mixing layer changes throughout the day, affecting the dispersion of pollutants. 4. Latitude: * Sunlight Intensity: Near the equator (low latitudes), the sun's rays are more direct, leading to more intense UV radiation, which can enhance photochemical reactions and thus ozone formation. * Distribution of Emission Sources: Industrialized regions and urban centers, significant sources of ozone precursors like \(NO_{x}\) and VOCs, may be concentrated at specific latitudes. * Stratospheric Intrusions: At high latitudes, stratospheric intrusions can introduce ozone-rich air from the stratosphere to the troposphere. 5. Longitude: * Time of Day: Due to the Earth's rotation, the position of the sun changes with longitude, affecting the daily cycle of photochemical reactions. * Distribution of Emission Sources: Significant emission sources might be concentrated at specific longitudes. * Meteorological Patterns: Weather systems vary longitudinally, especially in regions influenced by oceanic or continental effects. 6. Altitude: * Decreased Pressure: As altitude increases, atmospheric pressure decreases, potentially affecting ozone formation. * Temperature Profile: The temperature can either increase or decrease with altitude, affecting ozone concentrations. * Vertical Distribution of \(O_{3}\): Ozone concentrations generally increase with altitude in the troposphere and are high in the stratosphere. * Transport of Ozone Precursors: Elevated regions might be exposed to ozone precursors transported from lower altitudes. With the goal of predicting ozone concentrations in the study area with a 24-h lead time, considering the existing relationships between these variables and the influence they have on said concentrations, the feature vector was formed, as shown in Table 2.
# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 2 Relevant PV Problems
### Estimation of Parameters of Model Circuits  
The simulation of PV systems is important to optimize the production of the real systems. It is know that any PV can be modeled and represented by an equivalent electric circuit, whose parameters control the predicted or estimates operation of the PV cell or module. The single-diode circuit presents five unknown parameters [18; 19] (\(I_{ph}\), \(I_{sd}\), \(R_{l}\), \(R_{sh}\) and \(n\)), and the output current is evaluated as follows:  
\[I=I_{ph}-I_{sd}\times\left[exp\left(\frac{q\times(V_{L}+R_{s}\times I_{L})}{n \times k\times T}\right)-1\right]-\frac{V_{L}+R_{S}\times I_{L}}{R_{sh}}\]  
where \(I_{L}\), \(I_{ph}\), \(I_{d}\) and \(I_{sh}\) are the solar cell output current, total current, diode current and shunt current, respectively. \(R_{s}\) represents the series, and \(R_{sh}\) denotes the shunt resistances. In addition, \(V_{L}\) means the cell output voltage; \(n\) is the ideal factor of diode. \(k\) represents the Boltzmann constant, which is set as \(1.3806503\times 10^{23}\) J/K; \(q\) is set as \(1.60217646\times 10^{19}\) C, which is the electron charge, and \(T\) means the cell absolute temperature.  
The double-diode model presents seven unknown parameters [18; 19] (\(I_{ph}\), \(I_{sd1}\), \(I_{sd2}\), \(R_{l}\), \(R_{sh}\), \(n_{1}\) and \(n_{2}\) ), and the output current is evaluated as follows:  
\[I_{L}= I_{ph}-I_{sd1}\times\left[exp\left(\frac{q\times(V_{L}+R_{s}\times I _{L})}{n_{1}\times k\times T}\right)-1\right]\] \[-I_{sd2}\times\left[exp\left(\frac{q\times(V_{L}+R_{s}\times I_{L })}{n_{2}\times k\times T}\right)-1\right]-\frac{V_{L}+R_{S}\times I_{L}}{R_{s}h}\]  
where \(I_{sd1}\) and \(I_{sd2}\) represent the diffusion and saturation currents, while \(n_{1}\) and \(n_{2}\) represent the ideal factors of diffusion and recombination diode. The other parameters have the same meaning as the previous equation.  
This problem is presented as a optimization problem, where the output to optimize is \(I_{L}\), and the variables to be found are the unknown parameters.
## 3 Background
### Permutation Importance  
PI measures feature importance by calculating the changes in a model's error when a feature is replaced by a shuffled version of itself. The algorithm of how PI quantifies feature importance is shown in Algorithm 1.  
```
Result: Permutation feature importance Input: features, labels, Trained_Model predicted_output = Trained_Model(features));  baseline_performance = Loss(Predicted_output, labels); for\(i=0\); \(i<length(features)\); \(i++\)do  original_feature = features[i];  shuffled_feature = shuffle(features[i]);  features[i] = shuffled_feature;  predicted_output = Trained_Model(features));  error = Loss(Predicted_output, labels);  feature_importance[i] = error - baseline_performance;  features[i] = original_feature;  end for return feature_importance;
```  
**Algorithm 1**Algorithms of permutation importance.  
The \(shuffle\) function randomly changes the position of the row of the feature in every instance of the data. The magnitude of difference between \(baseline\_performance\) and \(error\) in Algorithm 1 signifies the importance of a feature. A feature has high importance if the performance of ML deviates significantly from the baseline after a shuffling; it therefore, has low importance if the performance does not change significantly. PI can be run on training or test data, but the test data are usually chosen to avoid retraining ML models to save computational overhead. If the computational cost is not an important factor to be considered, a drop-feature importance approach can be adopted to achieve greater accuracy. This is because, in PI, there is a possibility where a shuffled feature does not differ much from an unshuffled feature for some instances. For example, if a dataset has five instances and the original values for a feature is \([1,1,3,4,2]\), after shuffling, the new order of the feature is \([1,1,4,3,2]\); there is not much difference between the pre-shuffled and shuffled feature, making it difficult to determine its true importance accurately. In contrast, drop-feature importance excludes a feature, as opposed to performing its permutation, leading to more accurate quantification of importance. However, as it requires the ML model to be retrained every time a different feature is dropped, it is computationally expensive for high dimensional data. In this paper, we use PI over drop-feature importance to reduce the computational cost.  
Additionally, we choose to use PI over MDI using Gini Importance. MDI tends to disproportionately increase the importance of continuous or high-cardinality categorical variables [23], leading to bias and unreliable feature importance measurements. PI is a model-agnostic approach to feature importance, and it can be used on GBT, RF, and DNN.
# Learning Swimming Techniques by Means of Real-Time Monitoring with Embedded Devices
## 2 Materials and Methods
### Hardware Analysis
#### 2.1.2 Computing and Data Transmission  
The data obtained from the sensor is processed by an ESP32 WeMos Lolin 32 Lite microcontroller [13], which offers the following key features: it communicates via the I2C (Inter-Integrated Circuit) protocol, it is easily programmable and debugged, and it supports the addition of other components thanks to its extensive input/output pins (more than 30). Additionally, it provides the option to connect an external power supply via a built-in socket for a LiPo battery. The microcontroller supports communication through Bluetooth and Wi-Fi protocols, and its programming environment is straightforward, utilizing the Arduino IDE (Integrated Development Environment), which includes numerous software examples and libraries.  
Since the application requires wireless functionality, the use of wired connections is not ideal for the user. This necessitates an external power supply, as USB energy delivery is not feasible without a direct connection to a PC. To address this, a rechargeable 3.7 V LiPo battery [14] is integrated into the design, providing sufficient power for both the development board and the accelerometer sensor. Figure 1 presents an overview of the data flow in the system.