esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 2 Related Work
Meanwhile, deep learning-based tracking methods are actively proposed in the literature. [PERSON] et al. [20] presented multi-domain network (MDNet) that consists of shared layers and a domain-specific fully connected layer. Target representation is carried out in shared layers, while binary classification is performed in the domain-specific fully connected layer. MDNet has shown outstanding performance in accuracy in a benchmark performed by [PERSON] et al. [6]. [PERSON] et al. [21] proposed structure aware network (SANet) as an extended version of MDNet. SANet utilizes deep features from CNN and RNN based on the particle filter. Recently, Siamese network-based trackers have been proposed. In the Siamese network, similarity is calculated by matching the target template and candidate samples. [PERSON] et al. [22] proposed generic object tracking using regression network (GOTURN) based on the Siamese network. More recently, [PERSON] et al. [23] proposed lightweight Siamese network based tracker which can perform which can be updated during tracking process, and [PERSON] et al. [24] presented a tracking method based on multi-feature fusion Siamese network with Kalman filter. In addition, a number of different approaches to visual tracking have been proposed, and those can be evaluated by different standards. Generally, the major advantage of CF-based tracking methods is the high processing speed even without using GPU, whereas deep learning-based trackers achieved higher accuracy at the cost of expensive hardware and high computational load. Therefore, it is still worth improving the CF-based tracking methods since low-cost, real-time application is still ubiquitous in visual surveillance applications and numbers of image processing solutions [25; 26; 27; 28]. In the following section, we discuss improvement of KCF-based tracking method by integrating techniques which can handle tracking challenges.
## 4 Case Study and Discussion  
A 50-storey office building in Hong Kong was chosen to demonstrate how to use the proposed design method for optimal sizing of a chiller plant. This building is a typical office with a total gross floor area of about 61,250 m\({}^{2}\). The design value for indoor air temperature and relative humidity are 25 \({}^{\circ}\)C and 50%, respectively. The lighting load and office equipment load are 10.8 W/m\({}^{2}\) and 8.1 W/m\({}^{2}\), respectively. The occupancy density is 18 m\({}^{2}\)/person and the fresh air rate is 30 m\({}^{3}\)/person. According to the proposed design method, the implementation process can be divided into four steps. Firstly, Monte Carlo simulation was employed to generate various sample scenarios for cooling load. Cooling load distribution with required accuracy can be also generated. Secondly, a searching range of total cooling capacity for a chiller plant was specified based on the cooling load distribution. Thirdly, by considering the failure rate difference between constant-speed and variable-speed chillers, a steady probability distribution for various states of a chiller plant was generated by the Markov method. Finally, trials on various total cooling capacities and discrete number/size of chillers were carried out to find the optimum chiller plant with the lowest total cost.
## 4 Experimental Results and Analysis
### LSOD-YOLOv8 Robustness Experiment  
Considering that detection models are often affected by external environmental changes in real-world scenarios, we conducted research on the model's performance under varying brightness and blur conditions. In the experiment, we adjusted the image  
Figure 10: Comparison of loss indicators of ablation experiments.  
Figure 9: Comparison of main indicators of ablation experiments.  
By comparing the detection performance of the models before and after improvements under different brightness and blur levels, we observed that the YOLOv8n model is significantly affected by lighting. As brightness increases, it often results in missed detections, while a decrease in brightness leads to false detections. The improved LSOD-YOLOv8 model, however, only experiences a small number of missed detections in cases of extreme or substantial lighting changes, without any false detections. As the blur increases, the detection performance of both models declines sharply. The YOLOv8n model even shows false and missed detections due to the blur, whereas our model, though not completely immune to missed detections under high blur conditions, demonstrates superior accuracy and stability. This further proves the advantages of our model's performance and interference resistance in complex scenarios.
## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Unveiling Changes in Different Vulnerable Marine Ecosystems Using AI
#### 4.2.1 DL Applications for Coral Reef Monitoring
This phenomenon occurs when corals expel the symbiotic algae living in their tissues, leading to a loss of color and critical energy sources. DL has been instrumental in advancing the detection and prediction of bleaching events. As an illustration, [PERSON] et al. (2004) developed a Bag-of-Features-based method that combined handcrafted descriptors and CNNs for feature extraction, followed by an SVM classifier to detect and localize bleached corals. Their approach achieved an accuracy exceeding 99%. [PERSON] et al. (2005) used RGB drone imagery collected over five sessions between November 2018 and November 2019 around Australia, combined with a DL algorithm to classify bleached and unbleached corals. They implemented a U-Net architecture with a multiresolution block, enhancing the ability to capture both fine details and broader contextual information. Their study demonstrated that combining RGB imagery with DL algorithms offers an effective method for assessing coral health. [PERSON] et al. (2006) investigated the relationship between coral bleaching and various environmental variables using an MLP neural network and a 40-year global coral reef dataset spanning from 1980 to 2020. The results revealed that increased sea surface temperatures are consistently linked to coral bleaching across all oceans but the relationship between bleaching and other environmental factors varies by region. In the Atlantic Ocean, bleaching tends to increase with depth, while the opposite pattern is observed in the Indian Ocean. Moreover,by incorporating temporal information, researchers can accurately forecast future bleaching events based on historical data, aiding in coral reef protection efforts [207; 208]. For instance, [PERSON] et al. [209] used SVM and a five-year dataset from Thailand to model and predict coral reef bleaching under the influence of climate change. However, studies utilizing temporal neural networks for coral bleaching prediction remain relatively scarce [210].
# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
###### Abstract  
PET/CT can scan low-dose computed tomography (LDCT) images with morphological information and PET images with functional information. Because the whole body is targeted for imaging, PET/CT examinations are important in cancer diagnosis. However, the several images obtained by PET/CT place a heavy burden on radiologists during diagnosis. Thus, the development of computer-aided diagnosis (CAD) and technologies assisting in diagnosis has been requested. However, because FDG accumulation in PET images differs for each organ, recognizing organ regions is essential for developing lesion detection and analysis algorithms for PET/CT images. Therefore, we developed a method for automatically extracting organ regions from PET/CT images using U-Net or DenseUNet, which are deep-learning-based segmentation networks. The proposed method is a hybrid approach combining morphological and functional information obtained from LDCT and PET images. Moreover, pre-training using ImageNet and RadImageNet was performed and compared. The best extraction accuracy was obtained by pre-training ImageNet with Dice indices of 94.1, 93.9, 91.3, and 75.1% for the liver, kidney, spleen, and pancreas, respectively. This method obtained better extraction accuracy for low-quality PET/CT images than did existing studies on PET/CT images and was comparable to existing studies on diagnostic contrast-enhanced CT images using the hybrid method and pre-training.  
organ segmentation; PET/CT; U-Net +
Footnote †: journal: Medical Imaging and PET/CT Images
## 4 Instrument Design and Key Technologies
### IRS Based on Miniature Fixed-Temperature Phase-Change Cells  
Aiming at a high precision traceability of measurements with the space reference for the infrared spectrum, an infrared sounder with hyperspectral resolution traceable to international units will be designed. Fourier infrared spectrum detection technology and miniature phase-change cells as temperature standard technology are adopted for the IRS. Schematic diagram of the optical system  
Figure 5: Concept for solar spectral irradiance measurement with Spontaneous Parametric Down-Conversion (SPDC) calibration.  
for the IRS is drawn in Figure 6. Accurate blackbody temperatures (fixed points) are based on 'phase-change cells' technology.  
Traceability chain of the IRS is shown in Figure 7. Three on-orbit absolute radiance IR calibrators realize on-orbit self-calibration of the cavity blackbody. An on-orbit temperature scale from 270 to 350 K is established using ITS-90 miniature phase-change cells traceable to ITS-90 with an uncertainty of better than 10 mK (k = 2) [28]. Based on high precision temperature control and a transfer method, the blackbody temperature is traceable to the SI standard. The blackbody emissivity is measured based on the controlled environment radiation method and is transferred to another blackbody using the laser.  
The infrared interferometer realizes the high spectral resolution measurement over the infrared spectrum. The high sensitivity response over the wide spectral band is achieved by using a small-array detector. The high stability performance is realized by system temperature control technology using multiple temperature zones. High accuracy spectral calibration is achieved by referring to a standard spectral line source (high frequency infrared laser and gas absorption line).  
Figure 6: Schematic diagram of the optical system for the Infrared Spectrometer (IRS). Accurate blackbody temperatures (fixed points) are based on ‘phase-change cells’ technology.
## 3 Fiducial Reference Measurements (FRMs)
### What Is an FRM?
These principles describe a generic approach which can be adapted to suit the requirements of specific satellite measurements and/or validation methodology but simplistically, FRMs should all, where practical, seek to achieve the following:  
* Validation/measurement instruments are characterised and calibrated in laboratories where the reference instrument is characterised and traceable to standards (from metrology institutes);
* Measurement protocols are established and agreed within the community;
* Field in situ measurements (on sea, land, etc.) follow protocols and uncertainty budgets are derived;
* Analysis and intercomparison between measurements are carried out;
* Open-access websites/data portals and publications are provided to disseminate results and best practices. The first bullet of the mandatory characteristics, i.e., traceability, is one of the most critical and relates to evidencing the metrological quality and rigour of the FRM. It is thus important to be clear on the definition of the terminology. Here, 'traceability' and all other related terms such as 'uncertainty' are as defined in the International Organization for Standardization (ISO) guide on vocabulary for international metrology [11]:  
_Traceability is property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty._  
Note that it is not sufficient to simply purchase an instrument, even with a valid calibration certificate that robustly can evidence traceability to SI through some independent means, but that the use of that instrument in making the measurement can also be fully evidenced. The latter usually involves detailed documentation of the process and some form of independent comparison or calibration. It should also be noted that in some cases traceability to SI is not necessarily easy to achieve or demonstrate, and in those cases, a community agreed reference or protocol can be used. For example, the use of a Langley plot for calibrating a sun-photometer used for Aerosol Optical Depth (AOD) measurements [12] or the Amazon rain forest for calibration assessment of Synthetic Aperture Radar (SAR) imagers [13] would all be acceptable. FRMs, whilst ideally developed to accommodate as wide a range of sensors and applications as possible, will always be limited to a particular scope. It is important that the scope (parameter and type of satellite) to which the FRM is applicable is clearly defined and also that any procedure/tailoring of the data needed to match a particular sensor is also well described; for example, an FRM for top-of-atmosphere radiance for a high-resolution optical imager will need to define the spectral and spatial resolution of the FRM data and how it should be convolved to a satellite sensor, including the assessment of the uncertainty associated with that convolution. In many situations the specific details of how this may be done would be on a case-by-case basis, but the limitations and considerations should be transparent to the user.
## Appendix B Top-\(N\) Prediction Prompt  
As mentioned in Section 4.2, the top-\(N\) prediction prompt is constructed to make the LLM predict \(N\) tail entities sorted by the probabilities. The process is formally represented by:  
\[\mathcal{A}=LLM(\{(h,r,?),\mathcal{I},\mathcal{C}\}),\]  
Here, we use an example to explain this process. Assuming the given query triple is \((h,r,?)\) is:  
([PERSON], lives in country,?)  
Then, the corresponding in-context triples \(\mathcal{I}\) extracted by the proposed method are as follows:  
([PERSON], works at, University of Colorado Boulder),  
(University of Colorado Boulder, is located in, Colorado),([PERSON], was born in, Palo Alto)  
(Palo Alto, is located in, California)  
([PERSON], graduated from, Massachusetts Institute of Technology)  
Besides that, the retrieved set of candidate tail entities \(\mathcal{C}\) is:  
{Germany, Stanford University, Denton, Colorado,  
Sweden, California, USA, University of California, physics,  
scientist, New York City, Benjamin Franklin Medal}.  
Based on the query triple, in-context triples, and the set of candidate tail entities, we can construct the prompt as:  
\"Given the triple ([PERSON], lives in country, [mask]), please predict the top 10 tail entities [mask] by probabilities in descending order from the candidates [Germany, Stanford University, Denton, Colorado, Sweden, California, USA, University of California, physics, scientist, New York City, Benjamin Franklin Medal]. Below are triples that might be helpful for answer this question {([PERSON], works at, University of Colorado Boulder), (University of Colorado Boulder, is located in, Colorado), ([PERSON], was born in, Palo Alto), (Palo Alto, is located in, California), ([PERSON], graduated from, Massachusetts Institute of Technology)}. Then, the [mask] is:\"  
The LLM takes the prompt above as input, and conducts the reasoning through in-context learning. Finally, the output of the LLM are the predicted tail entities in the candidate tail entity set which are sorted by the probabilities in descending order. For example:  
\"USA, Colorado, California, Germany, Denton, Sweden, New York City, Stanford University, University of California, physics, scientist, Benjamin Franklin Medal.\"  
Among a series of entities output by LLM, the entity USA ranks the first, indicating that the LLM considers USA as the most likely tail entity for this query.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Constant Bandwidth  
We use the topology shown in Figure 8 to evaluate the performance of CCLF. In this scenario, four consumers request content from four producers.  
Our network model includes a bottleneck link between Router 2 and Router 3 with a bandwidth of 10 Mbps, while all other links have a bandwidth of 100 Mbps. This configuration enables us to study the impact of constrained bandwidth on multi-flow forwarding performance. The detection interval is set to \(\delta=0.01\) s. We simulate and compare the CCLF and PCON methods in terms of the FCT, network throughput, bandwidth utilization, and packet loss performance.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
###### Abstract
[3] categorise radar perception tasks into dynamic target detection and static environment modelling. They also provide brief introductions to radar-based detection, tracking, and localisation. [PERSON] et al. [4] summarise the deep learning models for radar perception based on different radar representations, including occupancy grid maps, range-Doppler-azimuth maps, micro-Doppler spectrograms, and point clouds. They also introduce approaches for radar and camera fusion based on the classical taxonomy of the data-level, feature-level, and decision-level. [PERSON] et al. [5] discuss the information sparsity problem and labelling challenge in learning-based radar perception. Three strategies are recommended to increase radar data density, including the use of pre-CFAR data, the use of high-resolution radar sensors, and the use of polarisation information. In this paper, we differ from other review papers in three aspects: Firstly, we provide a detailed summary and description of the publicly available radar datasets, which is very useful for developing learning-based methods. Secondly, this review does not focus on specific tasks, but aims to present a big picture of the radar perception framework, as illustrated in Figure 2. Thirdly, rather than simply presenting the network structure, we focus on explaining why these modules work from the perspective of radar domain knowledge. In this article, we systematically review the recent advancements in deep radar perception. In Section 2, we introduce the radar signal processing pipeline and different radar data representations. In Section 3, we summarise the publicly available radar datasets (Section 3.1) for autonomous driving, as well as the calibration (Section 3.2), labelling (Section 3.3), and data augmentation techniques (Section 3.4). In the following sections, we introduce different radar perception tasks, including radar depth completion (Section 4.1), radar full-velocity estimation (Section 4.2), and radar object detection (point-cloud-based in Section 5.2 and pre-CFAR-based in Section 5.3). In Section 6, we classify sensor fusion frameworks into four categories: input fusion (Section 6.1), ROI fusion (Section 6.2), feature map fusion (Section 6.3), and decision fusion (Section 6.4). Next, we discuss three challenges toward reliable radar detection, including ghost objects (Section 7.1), uncertainty problems (Section 7.2), and adverse weather effects (Section 7.3). Finally, we propose several interesting research directions in Section 8. Figure 2: Overview of deep radar perception framework.
## 5 Discussion
At Chonkyzylsu station (42.20\({}^{\circ}\) N, 78.19\({}^{\circ}\) E), apart from the 10.56% of water vapor transmitted from north to south, the rest of the water vapor is transmitted from west to east (Figure 7). There are obvious differences between the two regions, which may be one of the reasons for the differences noted in precipitation. The contents of clay fraction (<4 um) and fine-silty fraction (4-16 um) particles in Lake Ta-Lung-Chi sediments show a significant positive linear correlation (R\({}^{2}\) = 0.85, \(p\) < 0.0001), indicating that these components are influenced by the same factors. Furthermore, it was found that the total content of clay and fine-silty fraction (0-16 um) particles are clearly consistent with the monthly precipitation in July (Figure 6). However, during the 1940s and since 2000, the relationship between this particle content (0-16 um) and July precipitation has been more complicated. Through previous research, it has been found that the sources of lake sediments in arid areas are not limited to surface runoff within the basin, and that atmospheric dust also represents an important source of lake sediments. In addition to this, studies on grain size in arid regions can reveal the frequency and intensity of sandstorms that have occurred in the past [65; 66], e.g., lakes in the Tibetan Plateau [59], Lake Chaiwopu, Xinjiang Province, China [67], Lop Nur, Tarim Basin, northwestern China [68], San Juan Mountains, Colorado [69], and Lake Hongjiannao, Shaanxi Province, China [70], all of which show that the debris transported by wind in arid areas is also a source of lake sediments. A large amount of modern dustfall grain size data shows that the modal grain size of modern atmospheric dustfall materials is about 20 um [69; 88]. Therefore, some of the land-based materials in Lake Ta-Lung-Chi sediments less than  
Figure 7: Possible sources of water vapor identified for Lake Ta-Lung-Chi (42.45\({}^{\circ}\) N, 83.29\({}^{\circ}\) E) and Chonkyzylsu station (42.20\({}^{\circ}\) N, 78.19\({}^{\circ}\) E) using the HYSPLIT [40] and MeteoInfo (TrajStat package) [42] models. The base map was derived from SRTM 90 m Digital Elevation Database v4.1 [87]. The red lines suggested the cluster trajectories of water vapor source for the site of Chonkyzylsu Station, and the yellow for Lake Ta-Lung-Chi. 16 \(\upmu\)m in size may also come from atmospheric dustfall, which may be the reason for the inconsistency observed between the variation curve of sediment content smaller than 16 \(\upmu\)m and precipitation in the study area in July.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 3 Shearer Cutting Unit Tracking in Infrared Thermal Images
### Spatio-Temporal Context (STC) Target Tracking Algorithm
#### 3.1.4 Fast Learning of the Spatial Context Model  
By merging Equations (3)-(5) and (7), the following formula could be obtained.  
\[c(x)=be^{-|\frac{x-x^{*}}{a}|^{\beta}}=h^{sc}(x)\otimes(I(x)\omega_{\sigma}(x -x^{*})) \tag{8}\]  
The convolution operation in the time domain can be converted into product calculation in the frequency domain by fast Fourier transform (FFT) so as to reduce computation. Therefore, Formula (8) can be changed to:  
\[F(be^{-|\frac{x-x^{*}}{a}|^{\beta}})=F(h^{sc}(x))\otimes F(I(x)\omega_{\sigma} (x-x^{*})) \tag{9}\]  
The spatial context model can be obtained through two FFTs and one inverse FFT.  
\[h^{sc}(x)=F^{-1}(\frac{F(be^{-|\frac{x-x^{*}}{a}|^{\beta}})}{F(I(x)\omega_{ \sigma}(x-x^{*}))}) \tag{10}\]  
In the \(t\) frame of a video, the spatio-temporal context model \(H^{sc}_{t+1}(x)\) is updated through training the spatial context model \(h^{sc}_{t}(x)\), and is used to detect the location of the target in the \(t+1\) frame. In the image of the \(t+1\) frame, the local context area is delineated by the tracking position \(x^{*}_{t}\) in the \(t\) frame, and the corresponding context feature set is formed as \(x^{c}_{t+1}=\{c(z)=(I_{t+1}(z),z)|z\in\Omega_{c}(x^{*}_{t})\}\). The target location \(x^{*}_{t+1}\) in the \(t+1\) frame is determined by the maximum likelihood estimation of the new confidence map.  
The updating of the spatio-temporal context model is achieved as:  
\[H^{sc}_{t+1}=(1-\rho)H^{sc}_{t}+\rho h^{sc}_{t} \tag{11}\]  
where \(\rho\) is the learning rate parameter.  
The location of the target in the \(t+1\) frame is:  
\[x^{*}_{t+1}=\arg\max_{x\in\Omega_{c}(x^{*}_{t})}c_{t+1}(x) \tag{12}\]  
where the confidence map \(c_{t+1}(x)\) in the \(t+1\) frame can be expressed as:  
\[c_{t+1}(x)=F^{-1}(F(H^{sc}_{t+1}(x))\otimes F(I_{t+1}(x)\omega_{\sigma}(x-x^{ *}_{t})) \tag{13}\]