esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications
## 2 Literature Review
### Integration of Deep-Learning Techniques with Collaborative Robots  
[PERSON] et al. (2024) presents a work that developed a vision system integrated into a 6-degree-of-freedom KINOVA robotic arm (Producer: Kinova Robotics, City: Boisbriand, QC, Canada) for real-time grasping tasks. Using computer vision, the system combines object detection with pose estimation, allowing the robot to identify and manipulate objects efficiently. It is designed for applications where precision and efficiency are critical using an architecture based on advanced algorithms to perform tasks such as \"pick and place\" [13].  
The system employs the RealSense D435i RGB-D camera (Producer: Intel Corporation, City: Santa Clara, CA, USA) for image capture, and the YOLOv3 and GG-CNN networks for detection and grasping, represented in Figure 3. Images are processed in real time, with support for segmentation algorithms for object recognition. Furthermore, the project uses a limited dataset (100 NEU-COCO images) for training, with controlled lighting to minimize errors. The integration between the robot and vision is performed synchronously, allowing efficient operation under controlled test conditions [13].  
Based on our research, this work presents the following limitations [13]:  
* The robot used (KINOVA) is more common in laboratory applications and is not optimized for large-scale industrial environments, limiting its applicability as a co  
Figure 3: Grasping system developed by [PERSON] et al. (adapted from [13]).  
* Although the system performs well in controlled scenarios, the combination of YOLOv3 and GG-CNN may not deliver the performance required for real-time industrial applications, particularly in production lines demanding greater speed.
* The system was trained with a dataset of just 100 images (NEU-COCO), which restricts its generalization capacity and adaptability to more diverse objects and conditions.
* Data synchronization and robot interaction are not designed to handle multiple dynamic factors such as lighting variations, object movement, and changes in workspace layout.
* The work does not cover advanced strategies for dealing with lighting variations, such as shadows or diffuse lights, which can compromise accuracy in real-world scenarios.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
###### Abstract  
As 6G development progresses, joint sensing and communication (JSC) is emerging as a transformative technology, promising enhanced spectrum and energy efficiency alongside innovative services. This paper delves into underexplored facets of JSC, particularly its role in vehicular technology and transportation systems. It discusses data fusion techniques that enable cooperative sensing in networked environments and underscores the critical role of resource management in balancing sensing and communication. It suggests modeling extended targets, such as vehicles, within a computationally feasible framework. Moreover, it proposes a novel integration of AI-based target recognition, allowing target-specific tracking parameters and target-based sensing resource allocation. Importantly, a case study is presented to underscore the real-world applicability of these concepts in vehicular scenarios, demonstrating how networked devices can achieve high sensing and communication performance.  
6G; cooperative sensing; joint sensing and communication; mobile radio networks; resource allocation; positioning and tracking +
Footnote †: journal: Sensors  
0000-0001-5000-0001-5000][PERSON]  
122  
[PERSON]  
1 Department of Electrical, Electronic, and Information Engineering \"[PERSON]\" (DEI), University of Bologna, 40126 Bologna, Italy; [EMAIL_ADDRESS] (E.F.); [EMAIL_ADDRESS] (L.P.);  
[EMAIL_ADDRESS] (E.P)  
W. Lab, CNIT, 40133 Bologna, Italy  
Munich Research Center, Huawei Technologies Duesseldorf GmbH, 80992 Munich, Germany; [EMAIL_ADDRESS]
## 5 Conclusions  
This scoping review identified 17 in vitro studies evaluating the flexural properties of thermoplastic materials used in clear aligner therapy. Although three-point bending was consistently used across studies, significant variability in testing conditions, such as temperature, span length, deflection, and specimen preparation, limits a direct comparison of the outcomes. PET-G-based materials were the most frequently studied, and many exhibited reductions in mechanical performance following thermoforming, hydration, or mechanical loading. In contrast, TPU-based materials demonstrated more stable or improved behavior under similar conditions. Additionally, aligner thickness, thermal exposure, and loading protocols were shown to influence force delivery and material behavior, underscoring the importance of standardizing testing protocols to reflect clinical use.  
Based on the available evidence, the following conclusions can be drawn:  
* Thermoforming typically reduces the thickness and mechanical properties of clear aligner materials, particularly PET-G.
* Water or saliva exposure, especially under loading, can accelerate force decay; TPU may offer better moisture resistance.
* Increases in temperature or thermal cycling may reduce force output and modulus in several materials.
* Constant loading leads to stress relaxation and force decay, most notably during the initial hours of wear.
* Testing at 37 \({}^{\circ}\)C, using span lengths of ~8 mm and deflection values within clinically relevant ranges, may better simulate intraoral conditions and improve translational relevance.
* To improve clinical relevance and facilitate future comparisons, a minimum experimental reference protocol is recommended: testing at 37 \({}^{\circ}\)C, using a span length of ~8 mm, and applying deflection values within clinically relevant ranges (e.g., 0.2-0.5 mm).  
Conceptualization, A.V. and G.N.; methodology, G.N. and A.M.; formal analysis, G.N. and A.M.; investigation, G.N. and A.M.; resources, A.V. and C.L.; writing--original draft preparation, G.N. and A.M.; writing--review and editing, A.M., A.V. and C.L.; visualization,G.N. and A.M.; supervision, A.V. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 2 Proposed Approach
### Uniqueness-Based Saliency Map
#### 2.3.1 Uniqueness Calculation  
The uniqueness and compactness cues are complementary to each other in some respects [9], and many uniqueness-based models have been presented [7; 13; 14; 15]. Most of them construct the saliency map by comparing each pixel (or region) to the neighboring pixels (or regions) or the entire map, which can be referred to the local contrast-based and global contrast-based methods, respectively. However, as discussed in the Section 1, both of them have the limitation. Similar with [36], we sum up the drawback as the unreasonable contrastive reference regions. The saliency comes from the obvious difference with the background. Therefore, we calculate the uniqueness via restricting the reference region to the background seeds set, which has been referred as \(\mathbf{BG}\) in Section 2.2.1. In addition, to further reduce the effect of the background, we use \(S_{com}\) as the weight to integrate the uniqueness. The uniqueness is finally calculated with:  
\[ConSal(i)=\sum_{j\in BG}S_{com}\cdot\big{|}\big{|}l_{i}-l_{j}\big{|}\big{|} \cdot sp_{ij} \tag{18}\]  
where \(sp_{ij}\) is the weight to control the contribution of the color in CIELAB color space, in this paper, we set it as:  
\[sp_{ij}=\exp(-\frac{(p_{i}-p_{j})^{2}}{\sigma^{2}}) \tag{19}\]  
and as proposed in [7], \(\sigma^{2}=0.4\). Our proposed approach can well overcome the defect of the global contrast-based models, which can be seen in Figure 7b,c. The whole salient regions can be precisely highlighted rather than the whole image, and the background can be highly compressed.  
Figure 7: Visual comparisons of global contrast-based method and our proposed uniqueness-based method. (**a**) Original images. (**b**) Saliency maps based on global contrast-based cue. (**c**) Our proposed uniqueness-based saliency maps. (**d**) Global-based saliency maps with \(S_{com}\) as weight and after diffusion on 2-layer sparse graph with nonlocal connections. (**e**) Our proposed uniqueness-based saliency map after diffusion on 2-layer sparse graph with nonlocal connections. (**f**) \(S_{com}\) saliency map. (**g**) Ground truth.
## 2 Related Work
### Action Segmentation  
Before deep neural solutions, the most prevalent were statistical models [8; 9; 10] based on conditional independent assumption between segments, which ignore long-range dependencies and have been generally outperformed by the state of the art (see Table 1 for more quantitative performance comparison). MSB-RNN [11] uses a two-stream network and bi-directional LSTMs to learn representations and capture dependencies between video chunks, respectively. ED-TCN [12] uses temporal convolutions and pooling layers within an encoder-decoder architecture to learn long-range dependencies between frames. TDRN (temporal deformable residual network) [13] proposes two parallel temporal streams, facilitating temporal segmentation at local, fine-scale, and multiple long-range scales, respectively, for improving the accuracy of frame classification. TCED [14] introduces a learnable bilinear pooling in the intermediate layers of a temporal convolutional encoder-decoder net, in order to capture more complex local statistics than conventional pooling. ASRF [15] proposes to alleviate over-segmentation errors by detecting and refining action boundaries with a dedicated boundary regression module and a wider temporal receptive field.  
One problem in this context arises from the fact that the annotation to mark action boundaries for training is very time- and cost-intensive, leading to recent efforts trying to train classifiers without exact start and end frames of the related action classes (which is our case); the goal of this work is to infer frame boundaries given only an ordered list of the occurring actions.
## 5 Statistical Analysis of Residual Displacement Ratios
### Effect of Aftershock Intensity  
Past earthquake events have shown that the aftershock ground motion with various intensities occurs following the mainshock. It is thus significant to investigate the effect of aftershock ground motion intensity on residual displacement ratios.  
Figure 12 shows the \(C_{\rm r,seq}\) of systems when subjected to seismic sequences with different intensities of aftershock ground motions (for brevity, only five cases, \(\kappa=1/3,1/2\), \(2/3\), \(1.0\), and \(4/3\), are presented). It is seen that the effect of aftershock intensity depends on the post-yield stiffness ratio. For zero post-yield stiffness ratio, the \(C_{\rm r,seq}\) increases with the increase in \(\kappa\), indicating that the sequences with stronger aftershock ground motions generally produce larger residual displacement ratios. However, for a higher post-yield stiffness ratio (e.g., \(r\geq 0.03\)), the \(C_{\rm r,seq}\) are relatively close to each other for different levels of \(\kappa\). This observation can be explained by the fact that the yield limit of the structure with high post-yield stiffness is far away from the zero-acceleration (or force) axis, thus resulting in an oscillation being restricted within the loading-unloading branch.
# Temporal Subtraction Technique for Thoracic MDCT Based on Residual VoxelMorph
## 3 Research Method
### Temporal Subtraction Technique with Deep Neural Network  
Figure 1 shows a flowchart of the proposed temporal subtraction image generation method. First, lung field regions were extracted for each of the current and previous images by the processing shown in Figure 2, and clipping was performed according to the lung field regions. This is expected to allow a more efficient registration of structures in the lung field, such as blood vessels, compared to applying deep neural network registration in the current and previous CT images of the entire lung field. Next, owing to changes in the field of view (FOV), the size of the pixels in the image was adjusted by upscaling or downscaling the previous lung field clip images so that the pixel size was the same as the pixel size of the current image. Furthermore, as shown in Figure 3, the current and previous lung field clip images were divided into patches, and each patch was aligned by a deep neural network to generate a deformed image of the previous patch, which was then replaced with the original position of the split to generate a deformed image of the lung field clip region. The patch size was 64 \(\times\) 64 \(\times\) 64 pixels for experimental purposes. This addresses the problem of device-memory limitations in learning deep neural networks with GPUs. Next, a subtraction image was generated by subtracting the signal values of the deformed image from the signal values of the current CT image in the lung field clip region. Finally, the subtraction image is scaled up or down such that the image is identical to the FOV of the original image of the current CT, thereby generating a temporal subtraction image.  
Figure 1: Overview of proposed CT temporal subtraction method.
## 4 The WADGPS Master Station Processes  
The WADGPS master station (MS) receives and processes the measurements from all WADGPS reference stations (RSs). The data collected from each RS is calibrated and used to generate the differential corrections to ionosphere and satellite errors. There are two main correction generation modules: one is for the ionosphere and the other is for satellite errors. WADGPS provides the user with the differential corrections and two system accuracy metrics, namely, the user differential range error (UDRE) and the grid ionospheric vertical error (GIVE) [8]. This section begins with the introduction of GPS observations, and then presents the dual-frequency carrier smoothing process in Section 4.2. The estimation of ionospheric delay and the calculation of GIVE will be detailed in Section 4.3 and the estimation of satellite errors and the calculation of UDRE will be described in Section 4.4.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 4 Results
### Kalman Filter Algorithm Implementation
#### 4.2.2 Correction  
In the correction stage of the algorithm (measurement update), the following three values are first calculated:  
\[\left\{\begin{array}{l}\Phi_{k}=H_{k}P_{ikk-1}H_{k}^{T}+R_{k}\\ K_{k}=P_{ikk-1}H_{k}^{T}\Phi_{k}^{-1}\\ \overline{z}_{k}=z_{k}-H_{k}\hat{x}_{ikk-1}\end{array}\right. \tag{6}\]  
In Equation (6), \(H_{k}\) is the observation transfer matrix, which maps the real space \(x_{k}\) of the dynamic system into the observation space. \(R_{k}\) is the covariance matrix of the observed noise, \(\Phi_{k}\) is the covariance matrix of the observed margin, and \(K_{k}\) is Kalman gain. \(z_{k}\) is the observation at time \(k\), and \(\overline{z}_{k}\) represents the observation margin, which is the difference between the actual observation and the observation obtained by the a priori estimation.  
The three values obtained by the above calculation are used to update the filter variables \(\hat{x}_{ikk-1}\) and \(P_{ikk-1}\) to obtain the optimal estimate \(\hat{x}_{ikk}\) and the covariance matrix \(P_{ikk}\) of the estimation error of the system state at time \(k\).  
\[\left\{\begin{array}{l}\hat{x}_{ikk}=\hat{x}_{ikk-1}+K_{k}\overline{z}_{k}\\ P_{ikk}=(1-K_{k}H_{k})P_{ikk-1}\end{array}\right. \tag{7}\]
## 4 Training Efficiency Evaluation and Statistical Analysis
### Data Analysis  
The \(t\)-test analysis is a method that tests statistical significance by comparing the mean scores between two groups [29]. In this study, a \(t\)-test analysis was conducted to verify the statistical significance of the training efficiency when using the AR content. Figure 17 shows the results of the normal distribution test for the evaluation score of each group using Minitab 20 [30]. The Anderson-Darling (AD) values of the two groups were 0.337 and 0.356, respectively. The probability values (\(p\)-value) of the two groups were 0.48 and 0.43, respectively; both values are higher than 0.05. Therefore, the evaluation scores of the two groups were normally distributed.  
Figure 16: The graph of the results for training efficiency evaluation: (**a**) box plot of evaluation scores; (**b**) graph of scores for each content feature.  
In this study, the results were analyzed using Student's \(t\)-test [31] of an independent two-sample \(t\)-test, which was used to compare the mean scores between the two groups. The null hypothesis was \"there is no difference between the means of the two groups\", and the alternative hypothesis was \"the means of the two groups differed\". Table 6 shows the results of the \(t\)-test analyzed using Minitab 20. The standard deviation (StDev) values of the two groups (A and B) were 14.03 and 14.95, respectively; these results are almost the same. The standard error mean (SE mean) values for the two groups (A, B) were 2.6 and 2.7; these results are also almost the same. The test-statistic (T-value) was 4.36, and the degree of freedom (DF) was 58. The \(p\)-value was less than 0.0001, which is less than the significance level (0.05), and the alternative hypothesis of \"the means of the two groups differed\" was validated. Therefore, the mean score increase in group B was statistically significant. This indicates that training efficiency will be improved if AR content is used for railway vehicle maintenance training.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 3 Energy-Aware Reed-Solomon Scheme
### Architecture of EA-RS in a Sensor Node  
Figure 4 depicts the sensor node architecture with the application of the proposed EA-RS method. The micro-controller unit (MCU) modes of sensor nodes are largely classified into sleep and active states. When an MCU is in sleep mode, the sensor node operation is limited in order to minimize the energy consumption. When the MCU is in active mode, practical tasks requiring sensor nodes are performed.  
Required tasks are performed through various modules, such as sensing the various data required by the application through the sensing module and transceiving the data through the radio frequency (RF) module. The EA-RS module additionally operates when an MCU is in an active mode. This first invokes the parity-determination submodule in order to calculate the predicted residual energy and determine the parity length for the next period. This submodule is performed based on the historic information on energy collection and the predicted amount of energy consumption in the next period, as well as the parity lengths of neighboring nodes. Subsequently, the EA-RS module invokes the encoding submodule in order to create an RS code with the parity length determined at the parity-determination submodule, and then, this RS code is moved to the RF module in order to be transferred.  
Figure 4: Architecture of a node in which the EA-RS method is applied.
## 2 Transmission Line Equivalent Circuit Model of a Flexible Antenna  
The major aim of this study was to investigate the capability of antennas to flex and function properly under bending conditions. When deformation of an antenna implicates a significant change in radiation characteristics, such as return loss of antennas, which results in a shift in the resonant frequency, the signal degradation is sometimes too high to allow the signal to be received appropriately at the receiving ends. Therefore, a Transmission Line (TL) model under flex conditions was analyzed to corroborate the impact of bending on the radiation characteristics of a flexible antenna.  
In the case of flexible polymer-based antennas, the return loss is severely impacted when antennas undergo a certain level of bending. A simple TL model for a microstrip patch antenna is presented in Figure 1, where a co-planar microstrip circuit with aspect ratio \(W_{m}\)/\(h\) is presented with an open-end termination where \(W,L,Y_{c}\) and \(W_{m},L_{m},Y_{cm}\) are width, length, and characteristic admittances for the microstrip patch and TL, respectively, \(h\) is the height of the substrate, and \(Y\)s is the self-admittance or radiation admittance and represents the open-ended TL of the microstrip antenna. The general TL model for a microstrip patch antenna is shown in Figure 1. In the case of the microstrip feed line, the equivalent is represented by an open-ended admittance \(Y_{S}\).
## 5 Experimental Validation of the Designed SHM System
### Impact Amplitude and Location  
An experiment on the impact detection is described in this section. The purpose of this experiment is to verify the impact detection function of the system and verify its ability to distinguish signals from each channel. This ability is used to calculate the impact amplitude and the location of the impact.  
The steps for the experiment are as follows. (1) The experimental object is allowed to stand for a certain period of time to ensure the initial state of the system is stable. (2) The initialization switch is  
Figure 12: PZT sensors attached to the curved supporting structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Density (g/cm\({}^{3}\)) & 7.80 & d\({}_{31}\) (pm/V) & \(-\)1800 \\ \(\epsilon_{33}^{\mathrm{T}}/\epsilon_{0}\) & 1750 & d\({}_{33}\) (pm/V) & 400 \\ \(\epsilon_{11}/\epsilon_{0}\) & 1650 & d\({}_{15}\) (pm/V) & 550 \\ Curie Temperature (\({}^{\circ}\)C) & 350 & Mechanical Quality Factor Q\({}_{\mathrm{m}}\) & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the PZT sensor.  
Figure 13: The designed SHM system in experiments.  
Figure 15 demonstrates that for each impact signal, the response of the sensor that is closest to the impact signal is most pronounced. It was also found that the response data for the No. 8 sensor showed a random state, and the response to all the impact signals was not obvious. After inspection, it was found that the No. 8 sensor was not functional. The main reason for that might be the poor contact of the interface due to mechanical load during the co-curing and assembling process. The result of this inspection explains this phenomenon. It should be emphasized that as sensor No. 5 is located close to sensor No. 8 in the same half of the structure, when the impact is applied in the area near the sensor location No.8, sensor No. 5 will also receive a relatively large signal.  
The time-domain response signals (Figures 14 and 15) also reveal that the designed SHM system can not only distinguish the different impact signals by the sensor responses, but also maintain the original signals from sensors as it is.  
To quantify the robustness to measurement noise, the signal-to-noise ratio (SNR) values are calculated. The following SNR definition of the measurement signal is adopted.  
\[\mathrm{SNR_{dB}}\ =\ 20\log\frac{A_{signal}}{A_{noise}} \tag{10}\]  
where \(A_{signal}\) represents the peak amplitude of the impact signal and \(A_{noise}\) denotes the peak amplitude of the noise signal.  
Based on Equation (10), the SNR of the impact monitoring is 62 dB. The test results indicate that the system has good performance for impact detection in the amplitude evaluation and localization. The system hardware can record the impact response from the sensor, and the system software algorithm can recognize the location of the impact based on these signals.