esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
###### Abstract  
Detecting thermal bridges in building envelopes should be a priority to improve the thermal performance of buildings. Recently, thermographic surveys are being used to detect thermal bridges. However, conventional methods of detecting thermal bridges from thermal images rely on the subjective judgment of audits. Research has been conducted to automatically detect thermal bridges from thermal images to improve problems caused by such subjective judgment, but most of these studies are still in the early stage. Therefore, this study proposes a linear thermal bridge detection method based on image processing and machine learning. The proposed method includes thermal anomaly area clustering, feature extraction, and an artificial-neural-network-based thermal bridge detection. The proposed method was validated by detecting the thermal bridges in actual buildings. As a result, the average precision, recall, and F-score were 89.29%, 87.29, and 87.63%, respectively.  
thermal bridge; infrared thermal image; image processing; neural network; building energy
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 2 The Proposed HSI Classification Method at Superpixel Level
### Superpixel-To-Superpixel Similarity-Based Label Assignment  
In the proposed semi-supervised classification method, a pixel-level random labeling strategy is adopted. Based on the assumption of the homogeneity of superpixel, the superpixel \(S_{p}\) will be assigned to a label if at least one of the pixels in \(S_{p}\) is marked. Since clustering is not perfect, it could happen that a superpixel contains two or more pixels that are labeled with different classes. In this case, the final class label of this superpixel will be determined by the majority voting rule. Suppose that there are \(h\) marked superpixels. All such superpixels form a labeled set \(\text{S}^{\text{L}}=\left\{S_{1}^{\text{L}},S_{2}^{\text{L}},\cdots,\ S_{h}^{ \text{L}}\right\}\). The remaining unlabeled superpixels make up a collection to be classified \(\text{S}^{\text{U}}=\left\{S_{h+1}^{\text{U}},S_{h+2}^{\text{U}},\cdots,\ S_{M}^{ \text{U}}\right\}\).  
For each unlabeled superpixel \(S_{i}^{\text{L}}\in\text{S}^{\text{U}}\), we calculate its similarity to every labeled superpixel \(S_{p}^{L}\) in \(\text{S}^{\text{L}}\) by Equation (12). Then, the label of this superpixel \(S_{i}^{\text{U}}\) will be assigned according to:  
\[\text{\emph{Class}}\left(S_{i}^{\text{U}}\right)=arg_{p=1,2,\cdots,h}\text{min }\left\{s\left(S_{i}^{\text{U}},S_{p}^{\text{L}}\right)\right\},\ \ i=h+1,h+2,\cdots,M. \tag{13}\]
## 3 Application Protocol for Wireless Intelligent Sensor Networks
### WISMAP Advantages  
The advantages associated with the use of the approach presented in this paper are closely related with the utility contributed by:  
1. The collaborative FRBS embedded into the sensor nodes, which can be summarized in: * An improvement in the reliability, responsiveness and accuracy of the sensor node behaviors. * A decrease in the amount of data to be transmitted to neighbor nodes. * To give support to the minimization of sensor failures and communication errors.
2. The wireless intelligent sensors management application protocol presented, which can be summarized in: * A service interface, independent of the platform, which can be used with different sensor technologies. * To give support to a complete o incremental update of KBs. * Versatility in the way in which the sensor nodes notify their measurements, results or inferences. * To give support to minimize communication and processing in order to optimize battery usage. * To give support to new types of probes, actuators or sensors due to the open data format and hierarchical labeling system used in the protocol.
3. The multi-agent architecture presented, which can be summarized in: * To give support to future applications. This architecture provides a new framework for sensors, which embeds sensor and application management, FRBSs, communication and storing. * Completely open architecture based on free development tools. * Task and alarm scheduling without any re-coding of installed applications.
## 3 Vertical to Horizontal _PSA_ Ratios  
The ratios of vertical to horizontal _PSA_ estimates for various distances and site characteristics are depicted in Figures 5 and 6. These ratios were computed by dividing the vertical _PSA_ amplitudes obtained from Equation (1) and the coefficients from Table 2 by the horizontal PSA amplitudes obtained by Equation (1) and the coefficients from Table 1 in [18]. The plots also include the corresponding vertical to horizontal _PSA_ estimates as suggested by Eurocode 8 [1] for Type 1 and Type 2 spectra. As can be observed, the probability of empirical estimates, source-to-site distance, vibration periods, as well as both local soil and deep geology conditions, influence the vertical to horizontal _PSA_ ratios. The vertical to horizontal spectral ratios predicted by Type 1 or Type 2 Eurocode 8 spectra differ dramatically as well.  
For the 0.05 s vibration period, the vertical to horizontal _PSA_ ratios for deep soil sites atop deep geological sediments are in the range of 1.13-0.85 for the distances between 0 and 150 km. These ratios are 2.2-1.7 times higher than the Eurocode 8 ratio of 0.51, which is obtained for Type 2 spectra for Ground Type C and the same vibration period. For Type 1 Eurocode 8 spectra (used for magnitudes of most contributing earthquakes larger than 5.5), the ratios for T = 0.05 s are 1.5 to 2.0 times higher. The empirical ratios for the 0.30 s vibration period are in the range 0.44-0.50, which is 2.0-2.3 times higher than the corresponding ratios suggested by Eurocode 8 [1] for Type 2 spectra, and almost equal to the ratios for Eurocode 8 Type 1 spectra. For the 0.50 s vibration period, this study shows 0.30 to 0.48 ratios of vertical to horizontal _PSA_ values, which is 1.4 to 2.2 times higher than the corresponding ratios for Eurocode 8 Type 2 spectra but 1.1 to 1.7 times lower for the Type 1 spectra.  
Figure 4: Relationship between regional vertical pseudo-acceleration spectra at deep soil sites (\(S_{L}\) = 2) and the empirical predictions defined by Equation (1) and coefficients from Table 3.
# Brake Light Detection Algorithm for Predictive Braking
## 3 Methods  
### Dataset  
A self-gathered dataset was used to test and validate the brake light detection algorithm. Detailed specification of the videos used for constructing the dataset can be seen in Table 1. The equipment used to gather the dataset is introduced in detail in the next section. The dataset was recorded during multiple days at different times, thus including variations in weather conditions such as rain, clear sky, cloudy sky, and indirect sunlight. The recording took place in real traffic conditions in Helsinki metropolitan area during February and March 2020, and the real traffic conditions made it possible to collect data from different distances, mostly over 50 m. Longer distances were emphasized due to the intended application of predictive braking. The dataset includes image material of vehicle rears with brake lights on or off. The recorded dataset captured during this study will be publicly available after the publication. Therefore, this dataset can be used for algorithm training and testing operations in the future.  
The developed algorithm was trained and tested with images from the recorded dataset. The images were manually labeled and separated with a classical split of 80/20% into a train set and test set as can be seen in Table 2. The vehicles in the images have three brake lights in a triangular formation (two on the sides and one in the top middle). The images in which the brake lights were active were labeled as \"on\". The aim of the study was to train a random forest algorithm to identify a triangular formation of filtered colorspaceinformation in order to exclude scattered light, other light sources, and disturbances. Later, it will be shown that training the random forest algorithm with the dataset leads to a mask of weights with a triangular formation, emphasizing any light in those areas and attenuating light sources outside those areas.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 3 Core Technologies in Vision-Based Collaborative Robotics
### Scene Understanding and Environmental Modeling  
Beyond object and human recognition, holistic scene understanding is essential for cobots operating in complex environments [6; 24]. Scene understanding involves semantic segmentation, spatial reasoning, and affordance detection [50].  
By constructing rich semantic maps of their surroundings, cobots can identify interaction zones, dynamically allocate resources, and anticipate environmental changes. Techniques, such as DenseFusion for object pose estimation, and deep learning-enhanced SLAM algorithms, have propelled significant advancements in this domain [51].
## 5 Implementation and Results
### Data Handling  
The next step involves data handling. The ranking requires two datasets. The LightGBM algorithm is numeral based. The data should be pre-sorted and stored in a csv (comma separated value) file. We built two datasets, one for controversial (\(N=3662\)) and another for non-controversial (\(N=2462\)) artworks, each with nine columns, namely id, label, comments, likes, saved to collection, media type, media count, has more comments, and Facebook shares. The \"label\" column depicts the impact of attributes on what is popular or not. As in our prior research on memes [84], we provide experiments that declared relevance equally. There are some previous examples. In their music popularity prediction experiments, [PERSON] and [PERSON] [124] utilized multiple popularity indicators extracted from song ranks measured using rank scores, including max rank and rank (the song's rank). They anticipated the most popular song on a 100-song chart would be 100 and the least popular would be 1. [PERSON] and [PERSON] [125] employed labels {0, 1, 2, 3, 4} for document retrieval ranking in their model, where positive numbers denoted higher relevance and negative numbers denoted lower relevance. In our most recent analysis [84], we assigned the values for this feature from 0 (absolute negative correlation) to 3 (absolute positive correlation). Each artwork was in a separate group. Using OpenRefine [103], all samples were properly allocated a value between 0 and 3 based on the number of interactions per post. We gave 3 to the most interacted-with posts. We gave 0 to the posts having the least engagement. Relevantly, the remaining medium values were assigned 1 and 2.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 3 The Static Environment Modeling and Path Planning
### The Tunable Fast Marching Square Method  
In the Figure 5, the path generated by FMM is too close to obstacles. In the real sea environment, there are usually shallow water near the islands and coastlines. This is dangerous for USVs. Hence, USVs should be as far away from obstacles as possible for safety. The literature [33] has proposed a new algorithm named the fast marching square (FMS) method to tackle this problem, which adds one step to the conventional FMM algorithm. First, the FMS method generates a security potential map \((V)\) with the FMM executing from all the points in obstacle area (see Figure 6a) by using the previous planning space. Its specific principle is multiple wave source expansion of the FMM, as shown in Figure 4c,d. The potential map \(V\) is the arrival time matrix of the wave. Then, with the potential map \(V\) as the speed matrix, the FMM is again applied to generate the arrival time matrix \(T\) from the end point in Figure 6b. The final path generated by the FMS method is shown as the magenta trajectory in Figure 6c,d with increased safety.  
Figure 4: The fast marching method (FMM) applied over a grid map. (**a**) One wave source in two dimensions; (**b**) One wave source in three dimensions; (**c**) Four wave sources in two dimensions; (**d**) Four wave sources in three dimensions.  
Figure 5: Path planning with the fast marching method (FMM). (**a**) Output of the FMM, i.e., arrival time matrix \(T\); (**b**) Path obtained after applying gradient descent on \(T\); (**c**) Generated path and arrival time matrix \(T\) in three dimensions.  
However, the path generated by FMS method is the farthest route from the obstacles, i.e., in the safest area. The farther away from the obstacles, the longer the length of the path. Hence, the tunable fast marching square (TFMS) method is proposed to tradeoff between security and path length. This method only changes the potential map \((V)\) of the FMS method. The TFMS method needs to scale the \(V\) into the range of 0 to 1 and \(V=min(V1,Saturation)\), where \(V1\) is the scaled potential map and \(Saturation\) describes security from 0 to 1, and then rescales the \(V\) into range of 0 to 1. Figure 7 shows the result of path planning with different \(Saturation\) values in the potential map of the TFMS method. The TFMS is FMS when \(Saturation\) is 1.0, and FMM when \(Saturation\) is 0.0.
## 5 Evaluation
### Experiments on the Kaggle17 Dataset  
For active phase iterations, the initial model was trained with a fixed number of labeled samples. Subsequently, the model was trained using 10 EM iterations, and 10 ground truth labels were added in each EM iteration.  
As shown in Figure 6 (left), the model was trained with 25% of initially labeled samples, and 10 ground truth labels were added per EM iteration. The ROC curve using 3D-CNN architecture for active phase iterations (active 10) is shown in Figure 6. ASEM-CAD with high classification entropy shows a high true positive rate and low false positive rate, with an AUC of 0.89, as shown in Figure 6 (left). The AUC for high classification entropy has a better AUC value in comparison with average classification entropy and semi-supervised classification. A fully supervised model has an AUC of 0.92. However, with 25% of labeled data, ASEM-CAD with high classification entropy provides good results.  
Figure 6 (right) shows the ROC curve with 50% of initially labeled samples for active phase iterations. ASEM-CAD with high classification entropy achieves an AUC of 0.92. This is comparable to a fully supervised model, which uses 100% of the labels, with an AUC of 0.94. The ROC curve shows similarity in TPR and FPR between the performance of our ASEM-CAD model and that of a fully supervised learning model. Our model achieved sensitivity of 0.9 and specificity of 0.81.  
The ASEM-CAD performance increases when the number of samples used by ASEM-CAD is increased (see Figure 7). The ASEM-CAD achieved an average and maximum AUC of 0.93 and 0.94, respectively compared with 0.925 and 0.96 for the fully supervised models.  
Figure 5: The initial training and validation accuracy for ASEM-CAD with (**left**) Kaggle and (**right**) NLST datasets. The accuracy curves illustrate the ASEM-CAD performance, which is not underfit or overfit with the improvement in validation accuracy over the number of epochs.  
Figure 6: Active phase iterations for the Kaggle dataset using a 3D CNN with (**left**) 34% and (**right**) 59% of training labels. Fully supervised learning using 100% of training labels.  
With an increasing number of samples for training experiments, the ASEM-CAD performed as well as the supervised learning model (using 100% of labeled samples) but only used 59% of labeled samples.
## 5 Results and Analysis  
### Performance of the Solar PV Array and Converter  
The efficiency of the PV array, defined as the ratio of electrical output power to incident solar power, was found to average 19.2% under standard test conditions, demonstrating effective energy conversion. The response time of the MPPT algorithm, measuring the system's ability to adapt to rapid changes in irradiance, averaged 170 ms during step changes from 1000 W/m\({}^{2}\) to 500 W/m\({}^{2}\), ensuring minimal power loss during environmental fluctuations.  
Figure 11a demonstrates the successful performance of the P&O MPPT algorithm in a solar PV array utilizing a SEPIC, under fluctuating solar insolation conditions. The insolation varies with a step change from 1000 W/m\({}^{2}\) to 500 W/m\({}^{2}\), and the MPPT algorithm effectively tracks the maximum power point. As shown in Figure 11b, under maximum steady insolation, the optimal power (\(P_{pv}\)) of 420 W, as designed, is achieved, indicating satisfactory operation of the system.  
Figure 11: Performance of the solar PV array [(**a**) Dynamic performance, with solar insolation from 1000 W/m\({}^{2}\) to 500 W/m\({}^{2}\); (**b**) Steady-state performance for 1000 W/m\({}^{2}\)]. Time per division: 10 ms.  
The performance indices of the solar PV array, including currents (\(i_{PV}\)), output voltage (\(V_{PV}\)), power (\(P_{pv}\)), and motor speed (\(N\)), are shown in Figure 11a,b. The inductor current ripples are maintained within the range of 5%, below the specified design limits, ensuring smooth current and voltage at the output. An additional advantage of this converter is its non-inverting output voltage. Moreover, the motor speed remains unaffected by variations in solar insolation.  
In Figure 11a,b it is observed that stability is maintained across the varying conditions, with the MPPT algorithm showing minimal oscillations around the maximum power point due to an optimized step size in the P&O method. Experimental MPPT performance is shown in Figure 12a,b. The system exhibited strong robustness, maintaining power extraction efficiency above 95% of the theoretical maximum power point during partial shading and wide irradiance variations, facilitated by the SEPIC's capability to handle significant input voltage fluctuations.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 6 Results  
Figure 5 depicts the measured spectra of the different RGB LEDs at various temperatures. It can be observed how the spectra change as the p-n junction temperature increases, causing peak-wavelength shifts, spectral broadening, and spectral shape distortions. It must be noted that the responses are normalized by the maximum value in order to ease the visualization of the aforementioned phenomena, so changes in LED efficiency caused by temperature are not appreciated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Current source & Yokogawa GS820 \\ Spectrometer & Spectral Products SM442 \\ Integrating sphere & Gigahertz \(\phi\)32 cm \\ Thermal camera & FLIR A640 \\ CMOS sensors & Sony IMX249 and Foveon X3 \\ RGB LED under test & Kingbright L-154A4 SUREQBFZGEW \\ \hline Temperature range & 27 \({}^{\circ}\)C to 80 \({}^{\circ}\)C \\ Driving current & 10 mA to 150 mA \\ Red peak wavelength & 676 nm @ 10 mA \\ Green peak wavelength & 563 nm @ 10 mA \\ Blue peak wavelength & 460 nm @ 10 mA \\ Input SNR & 30 dB, 20 dB, 10 dB \\ Spectrometer’s integration time & 25 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the experimental setup.  
In order to illustrate the potentially harmful effects of compensating the RGB channel with obsolete CSI, the SINR at different p-n junction temperatures was obtained using the CSI corresponding to 27 \({}^{\circ}\)C and 60 \({}^{\circ}\)C. Some exemplary thermographic images are presented in Figure 6, illustrating the p-n junction temperature estimation procedure.  
The results section has been divided into three cases depending on the input SNR. High, medium, and low SNR values are associated with 30 dB, 20 dB, and 10 dB, respectively. This distinction has been carried out to analyze the input SNR's combined effect and the emitter's temperature variations on the system performance. Each subsection includes the effect of compensating the RGB OCC link using channel matrices at two different temperatures, as described above. Furthermore, this effect is evaluated on the two considered CMOS sensors, obtaining the SINR after the ZF compensation and, ultimately, the expected BER assuming an OOK transmission.
## 3 Cable Models and Methodology
### Verification and Reference  
The geometry, solver configuration, and boundary conditions used here align with the finite-element approaches discussed in [12], ensuring numerical consistency and validating that the solver accurately captures skin and proximity effects under compressed conductor arrangements. Consequently, any observed difference between 2D and 3D results primarily reflects the effect of axial extrusion rather than inconsistencies in modeling assumptions.
## 3 Experimental Results
### Performance of Our Proposed Scheme  
In this paper, we only considered the compression of the 3D models by preserving 5 valid bits of each coordinate, i.e., using 32 binary bits to represent one \(x\), \(y\), or \(z\) coordinate, and 96 binary bits for a vertex. The compression rate only changes the value, but does not change the trend of the embedding rate. We first used the test 3D mesh model \"dog\" to visually display some processing results of our proposed scheme, as shown in Figure 4. Then, we set every two vertices into one group to test the performance as listed in Table 2. For each model, half of the vertices were embeddable, resulting in the total number of embeddable bits being equal to twice the number of embeddable vertices. As the recovery information is used for usable vertices recovery, \(\left|\mathbb{R}\right|\) bits should be used for recovery information embedding. In addition to the first usable vertex, used for \(\left|\mathbb{R}\right|\) recording, the remaining bits can be totally vacated for additional data embedding as listed in the second-to-last column. The embedding rate of each model in the last column can be obtained by dividing the vacated bits by the number of vertices. Therefore, the embedding rate is directly proportional to the vacated bits and inversely proportional to the number of vertices.  
As the embedding rate was mainly affected by the number of vertices in each group of our proposed MGP technique, we further tested the embedding rates when setting different values of \(\alpha\). As shown in Figure 5, except for the model dog, the remaining models achieved the best embedding rate when setting \(\alpha=3\). The underlying reason is the reference vertices were far more than required when \(\alpha=2\). On the other hand, the reference is insufficient when \(\alpha=4\), as the prediction distance becomes larger, and more bits are needed to record the recovery information. When the number of vertices within the group is too small, it results in excessive vertices being classified as reference vertices. As the coordinates of reference vertices will be retained, this leads to significant spatial wastage. However, when the number of vertices within the group is too large, it results in a considerable distance between usable vertices and reference vertices, causing a decrease in prediction accuracy and, consequently, affecting compression efficiency. Therefore, the proposed MGP can adaptively find the optimal number of vertices in a group for different 3D models to achieve the best embedding rate.