esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Final Combination with the Local Window Information  
The automatic parameter calculation strategy can establish a suitable compensation model using Equation (14) for each shadow region from the regional level. However, even in a single shadow  
Figure 3: Feature points acquisition and relative regions schematic diagram for automatic compensation. (**a**) Compensation related areas. (**b**) Similar feature point pairs.  
region, the shaded extent differs from the different locations in the region; especially, some objects along the shadow region boundary that could get some scattered radiometric sunlight appear lighter than the objects in the center. Therefore, the statistics information of the local window centered of the pixel is used to establish a local window compensation model using Equation (15). In order to balance the interior difference in a single shadow region, the final compensation model defined in Equation (16) was developed by combining the compensation models from the regional level and local window level.  
\[g_{R}^{c}(i,j)=\alpha\Bigg{(}\overline{m}_{\text{NSD}}+\frac{g(i,j)-\overline{m }_{\text{SD}}^{R}}{\beta\cdot r_{1}^{R}}-r_{0}^{R}r_{1}^{R}\Bigg{)} \tag{14}\]  
\[g_{W}^{c}(i,j)=\alpha\Bigg{(}\overline{m}_{\text{NSD}}+\frac{g(i,j)-\overline{ m}_{\text{SD}}^{W}}{\beta\cdot r_{1}^{W}}-r_{0}^{W}r_{1}^{W}\Bigg{)} \tag{15}\]  
\[g^{c}(i,j)=u\cdot g_{R}^{c}(i,j)+(1-u)\cdot g_{W}^{c}(i,j) \tag{16}\]  
where \(\alpha\) and \(\beta\) represent the compensation intensity coefficient and the stretching coefficient, respectively, which are calculated automatically for each shadow region. \(\overline{m}_{\text{SD}}^{R}\), \(r_{0}^{R}\), \(r_{1}^{R}\) and \(\overline{m}_{\text{SD}}^{W}\), \(r_{0}^{W}\), \(r_{1}^{W}\) are the mean, the additive coefficient, and the multiplicative coefficient acquired by the statistic value of a single shadow region and local window region, respectively. \(g_{R}^{c}(i,j)\) and \(g_{W}^{c}(i,j)\) are the compensated \(I\) value for the shadow regional level and the local window level around pixel \((i,j)\), respectively. The local window region is a \(N\times N\) (\(N\in[5,20]\)) matrix centered on the shadow pixel\((i,j)\), as shown in Figure 2(a). \(u\) (\(u\in(0,1)\)) is the weight.  
Finally, after enhancing component \(I\) of the shadow pixels and transforming HSI to RGB, the final converted \(R\), \(G\), and \(B\) are added by the difference between the shadow and non-shadow regions \(\Delta R=R_{\text{NSD}}-R_{\text{SD}}\), \(\Delta G=G_{\text{NSD}}-G_{\text{SD}}\) and \(\Delta B=B_{\text{NSD}}-B_{\text{SD}}\), respectively, for color correction that can prevent a color deviation from the original color.
# MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes
## 4 Experimental Results and Analysis
### Mapping Accuracy Test  
In order to analyze the mapping accuracy quantitatively, we selected two substations with large-scale and sparse characteristics which are shown in Figures 6 and 8, also the standard ICP [20] and the LOAM [12] algorithm was selected to compare with the MIM_SLAM algorithm. In the experiments, the robot is controlled running at a speed of 0.6 m/s on a flat road along a straight line with a length of 10 m. The accuracy of mapping is defined as the robot's localization error \(\varepsilon\)[28], expressed as follows:  
\[\varepsilon=\frac{1}{N}\sum\limits_{i=0}^{N-1}\|e_{i}\| \tag{16}\] \[e_{i}=(x_{0}\oplus T_{i})-(x_{0}^{*}\oplus T_{i}^{*})\]  
where \(x_{0}\) and \(x_{0}^{*}\) denote the initial pose. \(\oplus\) is the standard motion composition operator. \(T_{i}\) denotes the transformation matrix of the estimated pose \(x_{i}\) relative to the initial pose \(x_{0}\). \(T_{i}^{*}\) denotes the transformation matrix of the true pose \(x_{i}^{*}\) relative to the initial pose \(x_{0}^{*}\). \(\|\cdot\|\) is the 2-Norm used in this paper. For the robot to navigate in three-dimensional environments, \(e_{i}\) is represented as \((x_{e},y_{e},z_{e},\varphi_{e},\psi_{e},\theta_{e})\). Then \(\varepsilon\) can be divided into two parts: translation error and rotation error.  
\[\varepsilon=\frac{1}{N}\sum\limits_{i=0}^{N-1}\|trans(e_{i})\|+\frac{1}{N}\sum \limits_{i=0}^{N-1}\|rot(e_{i})\| \tag{17}\]  
Since it is difficult to obtain the true pose of the robot, 20 points are selected from the robot's trajectory. The first point is taken as the initial pose. For the next 19 points, the difference value of the localization results relative to the initial pose is calculated. The difference value of the true pose can be measured by the tape. In Table 1, the localization error is tested with the standard ICP, the LOAM and the MIM_SLAM algorithms under substation A and B. It can be seen that the MIM_SLAM algorithm is more precise. If there is a closed-loop area on the robot's path, the MIM_SLAM algorithm will be better than the LOAM. The processing time per frame using different approaches is calculated and shown in Table 2. MIM_SLAM has a similar time consuming compared to the LOAM and meets the real-time requirements. In Section 4.4, this will be further analyzed in the benchmark dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Standard ICP** & **LOAM** & **MIM\_SLAM** \\ \hline  & & Trans. Error (unit: m) & \\ A & 0.213 \(\pm\) 0.148 & 0.064 \(\pm\) 0.057 & 0.052 \(\pm\) 0.043 \\ B & 0.282 \(\pm\) 0.177 & 0.075 \(\pm\) 0.066 & 0.066 \(\pm\) 0.062 \\ \hline  & & Rot. Error (unit: deg) & \\ A & 2.5 \(\pm\) 1.9 & 1.8 \(\pm\) 1.2 & 1.6 \(\pm\
## 3 Results and Discussion
### Oak Venerers  
In the case of oak wood, the roughness parameters of three veneers from the oak veneers package that corresponds to the evolution of the whole knot, were chosen to be measured (Figure 6). According to the results, all the three surface roughness parameters (Ra, Rz, Ry) were found to be, from the statistical analysis point of view, significantly lower in the case of the area around the knot, indicating a smoother area around the knot, compared to the typical structure of wood surface veneers. Generally, oak wood is a ring-porous hardwood species, whose structure, as expected, results in higher roughness levels. The lower roughness of the wood areas around the dead knot that had been felled, could be attributed to their higher density, lower porosity and the presence of tensile wood that was also visually detected. As it is widely known, tensile wood is formed on hardwoods on the upper side of logs and branches in places that are under tension. Tensile wood is characterized by lighter color and fibers that have thick walls and very small cavities. The cell walls in tensile wood areas are characterized by the presence of a gelatinous layer, which consists of concentrated microfiber substrates arranged almost parallel to the fiber axis, and can be deposited on the layer S\({}_{3}\) or even on layer S\({}_{2}\), causing the cell walls to be of higher thickness and the surface of tensile wood to become glossy [24]. Therefore, the tensile wood areas were easily recognized on the sliced veneers of this study.  
According to the statistical analysis of the results, in all cases examined, the Levene tests revealed that the null hypothesis that the error variance of the dependent variable (Ra) is equal across the groups, was accepted (6 th requirement of a successful ANOVA), recording a significance level \(>\) 0.05 (0.12-0.278). Investigating the roughness in different depths and areas around the knot (radius of 30 mm from the knot), going from veneer V1 to veneer V10, it is apparent (Table 1), that the roughness level records a slight decrease, marginally not statistically significant. The tests of Between-Subjects effects revealed that the factor of \"veneers\", referring to this progress of the different cutting depths from V1 to V10, affected the Ra variance by 16.4%. In addition, 83.4% of Ra variance is attributed to the factor of \"Structure\", referring to the typical and non-typical structure of wood (the latter around the knot), while the interaction between the factors \"Veneers\" and \"Structure\" affects the Ra variance by 21.4%.  
Figure 6: Surface roughness parameters Ra, Rz and Ry (\(\upmu\)m) of oak wood veneers (V1, V5 and V10) on typical wood structure areas (Typ) and areas around dead knot (Knot).
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 4 Aim and Contribution
### Contributions  
In this paper, we investigate the issue of same and different domain TL using DFU image classification as a case study by implementing several experiments. We have utilized the same domain TL with the DFU classification task. Then, we implemented the same procedure with TL from the nature image dataset. The contributions of the paper are multi-folds:  
* A new dataset has been collected which containing 1200 images of feet that have been manually labeled by a DFU expert as normal and abnormal.
* A hybrid deep learning model has been designed that combines traditional and parallel convolutional layers along with residual connections.
* Several training scenarios have been performed with the proposed hybrid model.
* Two pre-trained deep learning models (VGG19, ResNet50) have been trained with target datasets.
* It has been empirically proven that TL from the same domain of the target dataset can significantly improve performance.
* The performance of DFU classification has been improved by attaining F1-score value of 97.6%.
## 4 Experimental Results and Analyses
### Experiment on Image-Query-Based Video Searching
#### 4.3.2 CNN2h Dataset  
The CNN2h dataset [79] is composed of 2 h of CNN video. The dataset provides 139 query photos with a ground-truth query. The photos with geometric and photometric distortions are taken with mobile phones and tablets from displays showing the video. For pair-matching experiments, 2951 true and 21,412 false ground-truth matching pairs of query images and database frames are also provided. In our experiments, we only consider image-to-video matching and ignore the pair-matching experiments. Sample query image and video frames are shown in Figure 13.  
The test videos are down-sampled at 10 fps and have 72,000 frames in total. To search for the image in the video, we first use the proposed SBD to segment the videos into shots, and then we use the feature matching method to match the query photo features and the shot feature dataset. The retrieved numbers of video frames for each query image are shown in Figure 14.  
The retrieval accuracy depends on the accuracy of the shot boundaries. In order to reduce the miss rate, in the experiment, the skip sliding should be set to a lower value. We take five frames for a sling; after three-stage shot boundary detection, we obtain a total of 2864 shots and then use the shot features and query features for feature matching. About 2-3 frames will be missed due to gradual transitions.  
Figure 14: The number of retrieved video frames for each query image.  
Figure 12: The Positive Predicted Value (PPV) of ReTRiEVED using the SIFT descriptor under different UBCMATCH thresholds.  
Figure 13: Examples of the query images and ground-truth database frames.
## 3 Related Work
### Paddle OCR System and Post-Processing Information  
PP-OCR is widely used in various applications, including document analysis, and passport card recognition [28; 29]. In the context of industrial visual inspection, PP-OCR offers a robust solution for extracting textual information from product stickers.  
The work of [PERSON] et al. [30] demonstrated the effectiveness of PP-OCR in an industrial environment for Printed Character Recognition. Their solution detects stickers from panoramic scans of industrial plants, comparing the performance of different OCR systems, such as Tesseract OCR [31] and PP-OCR, with the latter outperforming the former.  
OCR systems can recognize text with high accuracy, but there is still room for improvement, particularly with post-OCR techniques. Post-processing methods can be classified into manual and semi-automatic approaches [32]. Manual post-processing involves reviewing and correcting OCR output, while semi-automatic post-processing combines human inspection and automated resources to enhance OCR accuracy [33]. Semi-automatic techniques can be further divided into isolated word and context-dependent methods.  
Isolated word post-processing analyzes the word to which OCR has been applied, while context-dependent methods consider all words in a document and analyze surrounding words to correct errors [34]. In the context of post-OCR processing, the transformer-based architecture BERT has been employed in several works, such as Kalo and Sipos [35], which introduces a technique for correcting Tesseract OCR output.  
Our proposed framework employs an isolated word approach for post-processing OCR, as the target label fields are rule-based and have no interdependence. This innovation contributes to post-processing OCR techniques.
# Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton
[29] characterized the wrist proprioception by considering each of the three degrees of freedom (DoFs), showing changes in proprioceptive acuity across different directions. Similarly, other researchers [30; 31; 32; 33] started investigating how proprioception varies when single or multiple joints are involved in a motion task. However, in the latter cases, their evaluation was only conducted by analyzing the final arm position, by means of end-effector devices. Recent advancements in exoskeleton's design provide the possibility of implementing proprioceptive paradigms involving a full-human range of motion, complementing with many DoFs to precisely determine the joint position [34; 35; 36; 37]. Exoskeletons exhibit numerous advantages compared to the end-effector robots, in particular for upper limb proprioceptive assessment: they offer the possibility of implementing three-dimensional tasks, following the arm's natural workspace, and enabling for independent or simultaneous movement of the shoulder, elbow, and wrist joints. Based on the references above, it is a consolidated opinion that proprioception is fed back considering both direction of motion and final arm configuration, and given the lack of studies that evaluate proprioceptive acuity along every single arm joint in a 3D-workspace, we decided to develop a spatial task for the assessment of proprioception, using a six DoFs bimanual exoskeleton, ALEx-RS [38; 39]. Our protocol enables quantifying single and multi-joint position sense, involving an active matching movement of each upper limb. The purpose of this study was to evaluate the \"sensorimotor\" contribution in single- or multi-joint arm movements (shoulder abduction/adduction, shoulder flexion/extension, and elbow flexion/extension) in healthy subjects, using an ipsilateral joint position matching (JPM) test. We aim at understanding (i) how proprioceptive acuity changes along the arm moving from proximal body joints to the distal ones; (ii) how the human nervous system decodes the simultaneous activation of more than one body joint (multi-joint).
# Kinematic Determination of the Aerial Phase in Ski Jumping
## 3 Results and Discussion
### Hill Size and Performance Level  
After defining \(\tau\) and filter settings, the algorithm was tested on data from other hills and ski jumpers. The jumps on HS106 and HS140 were both performed by male ski jumpers normally competing in World Cup and Continental Cup competitions and the jumps on HS117 and HS77 were performed by male and female junior athletes. Testing the algorithm on the data from hill size (HS140/HS106 and HS117/HS77) and different performance levels (HS106/HS117) aimed to assess whether the algorithm was robust in conditions and athletes other than in HS106. The average data from HS106 are displayed in Figures 3 and 7 and the data from HS77, HS117, and HS140 are shown in Figure 10.  
Figure 8: Standard deviation of defined (**a**) start point (SD\({}_{1}\)), (**b**) end point (SD\({}_{2}\)) and \(LD\)-ratio at (**c**) start (SD\({}_{3}\)) and (**d**) end point (SD\({}_{4}\)) for filter F5 (\(\blacksquare\)), F6 (\(\bullet\)), F7 (\(\blacktriangle\)), and F8 (\(\blacklozenge\)) defined by using the steady glide algorithm with \(\tau\) varying from 0.005 to 0.015 s\({}^{-1}\).  
Figure 9: Sensitivity analysis of the cost function for different \(\tau\) for filter setting F6 (\(\bullet\)) and F7 (\(\blacktriangle\)).  
As was done for HS106, the data from HS77, HS117, and HS140 were analyzed with threshold values ranging from 0.005 s\({}^{-1}\) to 0.015 s\({}^{-1}\). The average relative standard deviation values for the different hills are shown in Figure 11 along with the average across all four hills.  
The cost functions of all data sets display the same trend with \(\tau_{opt}\) = 0.01 s\({}^{-1}\) leading to a minimal normalized SD. Hence, the properties here used to define the steady glide phase, with a maximum allowed change in the rate-of-change in \(LD\)-ratio (\(\tau\)), can be used regardless of the performance level of the athletes or the hill size.
## 3 Method  
In this research, data collection involved gathering vibration, current, and voltage signals from operational DC motors using high-fidelity sensors and acquisition systems, capturing signals under diverse conditions. After that, visual inspection and Fast Fourier Transform (FFT) spectral analysis helped find patterns that could tell the difference between healthy and faulty states. Logarithmic scaling made frequency domain visualization easier. Frequency analysis pinpointed fault-associated components within specific ranges, extracting features crucial for gear fault diagnosis. Leveraging machine learning, particularly the AdaBoost Ensemble Approach with weak classifiers, classifiers were trained on these features for fault diagnosis, while model parameters were meticulously calculated to optimize predictive performance. Testing the method on different sets of data and different operating conditions proved that it accurately found and predicted gear faults in DC motors, demonstrating that it can be used in real life.
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 1 Introduction
The issue with pump scheduling is to formulate an optimal policy that will allow water utilities to decide on the best strategy to minimize pumping costs for water distribution networks while providing an acceptable level of service to end users. Pressure control deals with monitoring and controlling the pressure at a node not to be too high (to reduce leaks) and also not to be too low (to meet the required pressure-head level needed to satisfy consumer demand at such node). In addition, water distribution networks are vulnerable to various sources of attacks that may be physical,  
Figure 1: Schematics of a water distribution piping network. cyber or a combination of both [5]. While numerous cyber-attacks on water distribution systems infrastructure have been reported [14; 15; 16; 17], contamination of the water system due to chemical or biological means is also a challenge. Another major issue affecting the operation of water supply systems for sustainable development is the increase in urbanization, which raises the water demand level. The human population in urban settlements is growing significantly. For instance, by 2025, most of the global population will reside in urban regions [18]. Furthermore, UNESCO [19] reports that by 2050, the human population in urban regions will increase from 3.9 to 6.3 billion. Of course, the rate at which urbanization increases will differ between countries. In Asia and Africa, an increasing population in the urban settlements in these regions is close to 90% [20]. This means that the people and industries in these regions will use a large portion of the total available water. The increasing trend in urbanization due to technology advancement cannot be overlooked. It poses a serious threat to the available water resources. Thus, if the current trend continues, then by the year 2030, water demand will tend to be more than the supply by 40% [21]. At this crucial stage, it then becomes necessary to revolutionize the water supply system for sustainable smart city development. The paper is organized as follows. Section 2 briefly discusses the need to improve water system operation, related studies and the main contribution of this paper. In Section 3, the revolutions taking place in the industrial and water sector are discussed while Section 4 presents the digital technologies needed to revolutionize water supply system operation. Section 5 discusses the potential application of the new industrial revolution in the water sector. In Section 6, the summary of the potential application of the digital technologies in improving the water system operation, the prospect of each technology as well as the technical challenges is presented while Section 7 concludes the paper and presents future works.
## 3 Proposed LVRT Control Strategy for Grid-Connected ESSs and Analysis of PCC Voltage Variation
### LVRT Control Strategy Depending on State of Charge of Battery  
Contrary to the LVRT control strategy for the WPG and the SEG systems, in the grid-connected ESSs with a battery in the DC-source, shown in Figure 4, a state of charge (SOC) should be considered for the LVRT control strategy. The SOC is an essential indicator used to regulate the operating decisions and to avoid the over-charge or over-discharge. However, it cannot be measured directly by sensors. In general, the SOC is obtained by the battery management system or various algorithms for estimation of the SOC using the battery model [37; 38; 39].  
In case the SOC is higher or lower than the designated value under the discharging or charging conditions of the grid-connected ESS, the proposed LVRT control strategy, which is mentioned above can be applied. However, in case the SOC is lower or higher than the designated value under the discharging or charging conditions of the grid-connected ESS, the SOC should be considered in the grid-connected ESS with the LVRT control strategy. In other words, if the SOC is lower than the designated value under discharging conditions of the grid-connected ESS, the active current cannot be supplied to the three-phase grid. The other way, if the SOC is higher than designated value under charging conditions, the active current is not required. Therefore, regardless of the grid voltage drops, the grid-connected ESS controls the reactive current as rating current.