esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the context of power-line scenarios characterized by complex backgrounds and diverse scales and shapes of targets, and addressing issues such as large model parameter sizes, insufficient feature extraction, and the susceptibility to missing small targets in engineering-vehicle detection tasks, a lightweight detection algorithm termed CER-YOLOv5s is firstly proposed. The C3 module was restructured by embedding a lightweight Ghost bottleneck structure and convolutional attention module, enhancing the model's ability to extract key features while reducing computational costs. Secondly, an E-BiFPN feature pyramid network is proposed, utilizing channel attention mechanisms to effectively suppress background noise and enhance the model's focus on important regions. Bidirectional connections were introduced to optimize the feature fusion paths, improving the efficiency of multi-scale feature fusion. At the same time, in the feature fusion part, an ERM (enhanced receptive module) was added to expand the receptive field of shallow feature maps through multiple convolution repetitions, enhancing the global information perception capability in relation to small targets. Lastly, a Soft-DIoU-NMS suppression algorithm is proposed to improve the candidate box selection mechanism, addressing the issue of suboptimal detection of occluded targets. The experimental results indicated that compared with the baseline YOLOv5s algorithm, the improved algorithm reduced parameters and computations by 27.8% and 31.9%, respectively. The mean average precision (mAP) increased by 2.9%, reaching 98.3%. This improvement surpasses recent mainstream algorithms and suggests stronger robustness across various scenarios. The algorithm meets the lightweight requirements for embedded devices in power-line scenarios.  
power line; YOLOv5s; lightweight network; bidirectional feature pyramid; attention mechanism; receptive field
# Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model
## 4 Simulation and Results
### Data Pretreatment  
Considering the data integrity, the two time periods of 5:00-6:00 and 23:00-24:00 are excluded when selecting the data set. Only 17 time periods between 6:00-23:00 are selected as the data set, and the passenger flow in the non-operation time period of 0:00-5:00 is zero. There are 29 sections of data in each time period. Finally, about 27,000 normal passenger flow data and 20 available emergency passenger flow data are obtained.  
For data with particularly large scales such as people flow, the maximum-minimum normalization method is used to normalize the data.  
\[x_{nor}=\frac{x-x_{min}}{x_{max}-x_{min}}, \tag{5}\]  
\(x_{min}\) is the minimum value of passenger flow, which is 0 in this paper, \(x_{max}\) is the maximum value of passenger flow.
# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 2 Related Work
### Deep Learning and Spatiotemporal Modeling  
The emergence of deep learning significantly advanced the field. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), particularly LSTM-based models,were adopted to learn hierarchical and temporal representations of facial dynamics [14; 15; 16]. These methods improved recognition accuracy by capturing both spatial detail and the temporal progression of expressions. More recent explorations have incorporated 3D CNNs, transformers, and generative adversarial networks (GANs) to enhance the extraction of micro-expression features [17; 18]. However, many of these models are computationally intensive and often optimized for controlled environments.  
[PERSON] et al. [19] provide an extensive review of the application of graph convolutional networks (GCNs) in sentiment analysis, which proves pertinent due to the natural graph-structured representation of facial landmarks and their dynamic relations. [PERSON] et al. [20] offer a detailed survey on facial expression recognition datasets and methods, highlighting key challenges such as spontaneous expression recognition, class imbalance, and the difficulty of annotating low-intensity emotions. These findings emphasize the growing consensus in the literature that traditional single-model approaches often fall short when confronted with subtle or ambiguous expressions. In this context, the Transformer-Augmented Network with Online Label Correction proposed by [PERSON] et al. [21] illustrates a compelling approach by combining spatial and temporal attention with dynamic label correction. Their work underlines the importance of both architectural adaptiveness and label reliability, especially when working with inherently uncertain or low-intensity facial expressions.
## 2 Background
### Related Work  
In comparison to conventional WSNs, IP-WSN provides flexibility to access and manage individual sensor nodes locally or from remote sites through the Internet. This specific issue provides great significance to different mission specific applications. Our paper focuses on the energy efficient mobility of both IP sensor nodes and sensor networks which is based on PMIPv6 and 6 LoWPAN. There are few approaches proposed in the literature regarding IP-WSNs and their mobility issues. RFC 5213 gives a complete description of PMIPv6 [11]. RFC 4944 depicts the overall aspects of 6 LoWPAN, the packet format provided by IETF to ensure IPv6 communication over low-power, wireless personal area networks. In [9] the authors represent the overall scenario for extending IP to LoWPAN such as 6 LoWPAN adaptation layer, header format encapsulation and compression and finally focus on addressing, routing, auto configuration and neighbor discovery issues. In [14], its authors propose a network mobility protocol to support 6 LoWPAN, but they do not consider the 6 LoWPAN node mobility. The respective authors only consider network mobility which is performed with the help of mobile routers as well as they also do not consider unique addresses for each sensor node. In [8] the authors describe some real life design and application spaces for 6 LoWPANs which show the importance of 6 LoWPANs in near future. In [15] the authors propose a dual addressing scheme for 6 LoWPAN-based WSNs. It combines a global unicast address to cope with association link changes and node mobility, and it links local addresses to reduce the overhead. Although our proposed scheme considers global unicast address only, it is energy efficient because of using SPMIPv6 for communication protocol.
## 3 Materials and Methods
### Software Components Overview
#### 3.2.1 Augmented Reality: Vuforia and Unity  
To show the contextual energy information, an AR tool able to detect the target images where the energy data are going to be overlapped is needed. At present, there are many libraries for the development of AR applications; some of the most popular are Wikitude, DeepAR, EasyAR, Kudan, Maxst, NyARToolkit, and ARcore. In comparison with these toolkits, Vuforia [16] allows access to a powerful set of functionalities; that is, recognition of the different types of visual objects, text and environment recognition, developing platform-independent applications, and so on. Vuforia works around the image targetconcept, which represents images that Vuforia Engine can detect and track (see Figure 3a). To carry out this process, Vuforia does not use traditional markers such as data matrix codes or QR codes, and the used image targets also do not need special black and white regions to be recognized; in contrast, Vuforia detects and tracks the features that are naturally found in the image itself (see yellow points in Figure 3b) by comparing them against a known target resource database. Once the system identifies an image target, it is tracked until it disappears from the visual field of the camera. This image is used as the base to overlap the information of interest; in our case, the energy information obtained by the IoT PQ sensor.  
Another interesting feature of Vuforia is that it can be easily integrated with Unity [17], which is one of the most popular and powerful platforms used for mobile game applications and is progressing steadily in the augmented reality area. In our case, Unity was used as the base infrastructure for the application layer of our AR system. In other words, Unity helps us to develop the augmented reality application and to design attractive and intuitive graphical interfaces for monitoring the IoT PQ sensor values through the unity object concept (see Figure 3c). These objects (3D or 2D elements) are associated with the image target and they are displayed on the screen whenever the application detects the associated marker (see Figure 3d). Thus, to unfold this PQ AR infrastructure, a combination of the Unity and Vuforia technologies was selected. In short, image target is the image associated with the  
Figure 2: The IoT PQ sensor developed.  
part of the real world on which the information is going to be overlapped, and the unity objects are the virtual elements (text, 3D objects, and so on) overlapped on the image target.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 5 General Discussion
### Sensibility to Chromosome Distortions  
In this section, the ability of color metrics to take into account chrominance artifacts is discussed. The discussion is focused on the database 4 Kdtb which is the only database providing significant chrominance artifacts. Also we only consider metrics using the \(ICICp\) color space since the best performances are observed with this color space. Figure 14 presents the Mean Opinion Score (MOS) and objective scores for the reference image \"Regatta_24s\", for the distorted images (compressed with HEVC). The objective scores are given after applying the logistic regression presented in Section 4. Results for the other reference images can be found on Appendix C.  
There is a clear difference of quality perception between the images compressed with the chroma \(Qp\) adaptation (cf. Section 3.1.2) (red Line) and the images compressed without the chroma \(Qp\) adaptation and a 8 bits quantization on the chrominance (blue line). The MOS of images compressed without the chroma \(Qp\) adaptation algorithm and a 10 bits quantization (green line) are in-between the two previous encodings.  
As expected, the color-blind metrics, i.e., HDR-VDP2 and FSIM, are not sensitive at all to the chrominance distortions. However, more surprisingly, the color extension of FSIM, namely FSIMc, is not sensitive to the generated chrominance artifacts. The metrics was tailored for images in a BT.709 gamut with a SDR range. Its non-sensibility to the chrominance might be due to the pre-defined constant used for the color comparisons [29].  
The other color metrics, i.e., \(\overline{\text{AE}^{S}}\), SSIMc and CSSIM, are more sensitive to the chrominance artifacts. However, SSIMc and CSSIM have a tendency to underestimate the influence of chrominance artifacts for images compressed with a low \(Qp\) (so low distortion in the luminance channel) and a 8 bits quantization on the chrominance (cf. Figures A7, A9-A11 and A13).  
Figure 13: SROCC of (**a**) FSIM\({}_{HDR\text{-}Lab}\), (**b**) MS-SSIM\({}_{HDR\text{-}Lab}\), (**c**) SSIM\({}_{HDR\text{-}Lab}\) in function of the diffuse white luminance.
# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 6 Discussion
Despite solving most of the tasks for safe deployment in level 4 or 5 AVS, challenges remain, such as complex training cost, lack of proper surrounding vehicles' behavior analysis and unfinished case in complex scenarios. Some problems remain to be resolved for better outcomes, such as the requirement of a larger labelled dataset [57], struggle to classify in blurry visual conditions [49] and small traffic signs from a far field of view [51], background complexity [48] and detecting two traffic signs rather than one, which occurred for differentlocations of the proposed region [47]. Apart from these, one of the most complicated tasks for AVS, only vision-based path and motion planning were analyzed by reviewing approaches such as deep inverse reinforcement learning, DQN time-to-go method, MPC, Dijkstra with TEB method, DNN, discrete optimizer-based approach, artificial potential field, MPC with LSTM-RNN, advance dynamic window using, 3D-CNN, spatio-temporal LSTM and fuzzy logic, where solutions were provided by avoiding cost function and manual labelling, reducing the limitation of rule-based methods for safe navigation [164] and better path planning for intersections [165], motion planning by analyzing risks and predicting motions of surrounding vehicles [166], hazard detection-based safe navigation [168], avoiding obstacles for smooth planning in multilane scenarios [169], decreasing computational cost [170] and path planning by replicating human-like control thinking in ambiguous circumstances. Nevertheless, these approaches faced challenges such as lack of live testing, low accuracy in far predicted horizon, impaired performance in complex situations or being limited to non-rule-based approaches and constrained kinematics or even difficulty in establishing a rule base to tackle unstructured conditions. Finally, to visualize overlaying outcomes generated from the previous methods superimposed on the front head-up display or smart windshield, augmented reality-based approaches combining deep learning methods were reviewed in the last section. AR-HUD based solutions such as 3D surface reconstruction, object marking, path overlaying, reducing drivers' attention, boosting visualization in tough hazy or low-light conditions by overlapping lanes, traffic signs as well as on-road objects to reduce accidents using deep CNN, RANSAC, TTC methods and so on. However, there are still many challenges for practical execution, such as human adoption of AR-based HUD UI, limited visualization in bright daytime conditions, overlapping non-superior objects as well as visualization delay for fast moving on-road objects. In summary, the literature review established for vision-based deep learning approaches of 10 taxonomies for AVS with discussion of outcomes, challenges and limitations could be a pathway to improve and rapidly develop cost-efficient level 4 or 5 AVS without depending on expensive and complex sensor fusion.
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 1 Introduction  
The new generation of mobile communications networks, e.g., 5G and beyond, along with the abundance of technologies and the support of high transmission rates and reliability, bring a massive increase in mobile devices, sensors, and services ranging from high-throughput multimedia applications to ultra-reliable Internet of Things (IoT). Hence, mobile operators should consider exploiting novel and efficient offloading schemes to alleviate the stress on cellular networks. In this context, the employment of unmanned aerial vehicles (UAVs) as flying base stations, is expected to be a promising solution offering several degrees of freedom and flexibility in the network [1, 2].  
Traditionally, fixed cells, e.g., picocells and femtocells, are employed to offload the existing terrestrial cellular networks. This approach requires design and analysis to efficiently integrate the fixed small cells in high data traffic demand areas. However, this strategy significantly restricts the degrees of freedom of the network and cannot cope with future on-demand changes in other regions covered by the terrestrial infrastructure [3, 4].  
In addition, in case of user hot spots formed for a limited period, e.g., sports events and festivals, offloading the terrestrial network would require installing a fixed terrestrial infrastructure, e.g., picocells. Moreover, following this approach, various small base stations will probably be inactive in the long term due to the continuous mobility of the users' hot spots. In other words, if future demand increases in another region within the coverage area provided by the terrestrial infrastructure, it will require re-analysis, design, and installation of fixed cells. Hence, this solution is both cost and time ineffective for the operators. Preferably, it would be better to utilize a network entity capable of moving from regionto region on-demand and efficiently offloading the terrestrial network; therefore, various studies have been proposed in the literature, using UAVs as flying base stations, which, unlike the ground-fixed picocells, can on-demand move to the area of interest and offload the terrestrial network [5].
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 5 Result Analysis
### International Cargo  
The forecasting results and comparisons with other models for international cargo flow are presented in Table 3 and Figure 7. From the results, it can be concluded that:  
1. Consistent with the domestic cargo cases, the EMD-SARIMA model forecasts with the best accuracy, followed by the Holt-Winters model, the SARIMA model, and the naive model, especially for the local maximum and minimum values;
2. Due to greater variability of the international cargo data, the performances of the forecasting models generate larger prediction errors compared with the domestic cargo series;
3. Again, the EMD-SARIMA model forecasts with better accuracy consistently regardless of whether it is single- or multi-step forecasting.
# Spinal Cord Segmentation in Ultrasound Medical Imagery
## 4 Experimental Results
### Experiments on Dataset-A  
#### 4.2.1 Selection of the Best Performing Algorithm on Dataset-A  
We began by evaluating the state of the art segmentation algorithms on Dataset-A. This aims to measure the ability of the algorithm to segment spinal cords that were already learned in the training set. We tested five state of the art algorithms. First, we tested Fully Convolutional DenseNets [30], we took the version FC-DenseNet103 as it is being considered as the best variation of FC-DenseNets [30]. We also tested Deeplab v3 plus [33], it is currently the state of the art model tested on Pascal VOC semantic segmentation dataset [34]. We tested PSPNet [36], which is currently the state of the art on Cityscapes semantic segmentation dataset [37]. We also tested two other state of the art algorithms, which are U-Net [26] and BiseNet [38]. To train these algorithms we used Semantic Segmentation Suite [39]. It is an open-source framework that contains implementations of some semantic segmentation algorithms in Tensorflow [40].  
We made the training of the chosen algorithms on Dataset-A for 400 epochs. Figure 12 shows the measure of IoU on the test set of Dataset-A after every epoch of the training. It shows that FC-DenseNet103 and U-Net outperform clearly other algorithms. FC-DenseNet103 is slightly better than U-Net because the DenseNets block is more able to capture complex pattern provided in the data. Other algorithms are not capable to capture similarly the complexity of the data patterns inside small datasets like Dataset-A.  
Table 3 shows the metrics measured of every one of the tested algorithms. The table shows that FC-DenseNet103 outperforms all other algorithms in all the metrics.  
Figure 12: IoU after every epoch of the training.  
Figure 13: Samples from Dataset-A (patient 01 to patient 05), the ground truth and the predicted segmentation mask.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **IoU** & **Dice Score** & **Accuracy** & **Recall** & **Precision** \\ \hline FC-DenseNet103 & 0.946 & 0.989 & 0.989 & 0.989 & 0.989 \\ UNet & 0.944 & 0.988 & 0.988 & 0.988 & 0.988 \\ BiSeNet & 0.904 & 0.974 & 0.975 & 0.975 & 0.975 \\ PSPNet & 0.891 & 0.977 & 0.978 & 0.978 & 0.984 \\ Deeplab v3+ & 0.879 & 0.976 & 0.975 & 0.975 & 0.978 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metrics measured for every segmentation algorithm.  
In order to improve more the accuracy, we picked out FC-DenseNet103 and applied a list of modifications. The modifications are detailed in the next sub-section
# Research into Image Point Positioning Compensation of a High-Orbit Thermal Infrared Staring Camera
## 2 Virtual Control Points Calibration Scheme Based on Image Space Uniformity
### Calibration Principle
For the image point positioning error caused by the external orientation elements, the relative installation angle change caused by the temperature change is used to compensate the external parameters, and the external parameter compensation model is constructed to calculate the external calibration parameters. 7. After calculating the internal and external calibration parameters, the error compensation is carried out according to the two schemes proposed in this paper. The positioning results before and after error compensation are compared with the theoretical values to obtain the optimal compensation scheme. For external parameter compensation, the image point positioning error before and after compensation is directly calculated. For internal parameter compensation, the two-dimensional pointing angle corresponding to the probe on the area array camera is calculated with the solved intrinsic calibration parameters, and the deviation between the two-dimensional pointing angle of the probe and the two-dimensional pointing angle after adding distortion parameters is counted. For the temperature error, the calibration results of the installation angle at different temperatures are calculated, the change curve of the installation angle and time is established, polynomial fitting is performed, and then it is applied to the compensation of external parameters.
## 3 Results and Discussion
### Drying Shrinkage  
Figure 6 shows the change in the drying shrinkage of mortar according to the replacement ratio of fly ash. The drying shrinkage of the FA20 FN00 sample including only fly ash was the highest, reaching approximately 0.148% after 56 days, whereas the FA00 FN20 sample without fly ash presented the lowest shrinkage, 0.124%.  
Figure 4: Mortar flow.  
Figure 5: Compressive strength.  
In addition, the drying shrinkage of the FA15 FN05, FA10 FN10, and FA05 FN15 samples containing from 5 to 15% of fly ash was similar, ranging from 0.132 to 0.136%. In general, the drying shrinkage of the samples containing ferroickel-slag powder was slightly lower than that of the samples including fly ash. This is supposed to be due to the contribution of the tight pore structure of the hardened mortar [13] by mixing the ferroickel-slag powder. Therefore, if ferroickel-slag powder is used as a cement substitute, it is likely to be effective in controlling the drying shrinkage of cement mortar and concrete.