esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative 2D Tissue Motion Tracking in Ultrafast Ultrasound Imaging
## 1 Introduction
Contrary to phase-sensitive motion estimation methods (e.g., [14; 27]) where the estimated motion must be small to avoid aliasing, our experience shows that the relative motion estimation error decreases for block-matching methods when the length of the estimated motion increases [26]. Since the motion between two frames in ultrafast ultrasound cine loops is often very small and the speckle decorrelation is limited, the risk for the speckle decorrelation over several, e.g., 128, frames is small but the total motion over this number of frames will be larger and easier to accurately estimate using block-matching. Therefore, we propose a temporary down sampling of the frame rate in which a first Lagrangian motion estimation is performed between each \(k\) frame, e.g., initial frame interval \(k\) = 128. The cine loop is thereafter iteratively re-sampled with shorter frame intervals and the position of the kernel in one in-between frame can be estimated using the two kernels of the anteroposterior frames as reference kernels. The tracking scheme is hypothesized to reduce the size of the accumulative errors both by using two separate motion estimations for each estimated position, thus reducing each estimation error, and by using much fewer estimations from the start of the tracking before reaching the investigated frame. The aim of this study was to evaluate the proposed 2D tissue motion estimation tracking scheme in ultrafast ultrasound cine loops. In a phantom evaluation, the proposed tracking scheme was evaluated for a range of initial down-sampling factors (\(k\) = 1-128). The motion estimation errors of the proposed tracking scheme using ultrafast ultrasound cine loops were compared to those obtained in low frame rate cine loops, obtained with conventional beamforming. The motion estimation errors of both velocity and displacement were evaluated. The tracking performance was evaluated using a 100 \(\upmu\)m pitch transducer and a 200 \(\upmu\)m pitch transducer. The feasibility of using the proposed 2D tissue motion estimation tracking scheme in vivo was evaluated in a limited in vivo study.
## 2 Data Quality Analysis
### DQA4: Satellite Position Difference between Almanac and Ephemeris  
Almanac data is not as precise as ephemeris data for the satellite position calculation, but the applicable period of almanac data can cover several months. Almanac data is used to accelerate the time-to-first-fix (TTFF) when one turns on a GNSS receiver. In DQA4, we compare the satellite position calculated from the almanac data with the satellite position calculated from the corresponding ephemeris data, and the distribution of the differences of the calculated satellite positions are listed in Table 4. As shown in Table 4, the satellite positioning performance of BDS almanac data is similar to that of GPS almanac data.
# IoT Technologies during and Beyond COVID-19: A Comprehensive Review
## 4 A Framework of Solutions to Tackle COVID-19 and Emerging Pandemics in the Future Using IoT
### IoT in the Education and Communications Sector  
The pandemic has forced a change in the education system by limiting face-to-face teaching and on-campus learning [50]. Distance learners and students who study at home will be able to continue their education through IoT and advanced technologies. Studies show that some virtual studies can prepare students for laboratory exercises and essential non-cognitive skills [51].  
[PERSON] proposed an IoT framework to facilitate adaption of the studying process to the current situation [52]. This framework administers both teaching and examination processes via IoT. The components used are web camera for attendance, behavioural indicators, and emotional state and GPS tracker to capture movement and monitor location; a wearable device that examines brain signals, heart rate, and blood pressure; sensors that examine the collected data and detect abnormalities and notifies the lecturer; and different algorithms such as face recognition, deep learning, and classification algorithms used for monitoring and management. In addition, eye trackers, EEGs, and web cameras are used in examination for student inspection and to detect cheating. The above study shows the real use of IoT to support distance learners.  
An integrated IoT framework proposed by [PERSON] focuses on human emotion analysis [53]. The analysis of physiological signals is done through Long Short-Term Memory (LSTM) based emotion recognition to identify different emotions. This framework enables communication and recognition of human emotions in real time of above 95%, which can support student engagement, healthcare infrastructure, and educational institutions in approaching an effective distance learning paradigm while minimising COVID-19 pandemic threats.  
Google and Apple recently added a COVID-19 exposure notification system for Android and Apple phones [54]. The system notifies the user when they come into contact with someone who has been reported of having COVID-19. The system also provides instructions of what to do if you have been in contact with someone with COVID-19. The system ensures the user's privacy by not sharing location and by generating random IDs to prevent tracking. The only information shared is the day of contact, the duration of the contact, and the distance between the users.  
Figure 5: Key observations of IoT in the transportation sector.  
Table 4 summarizes the key IoT applications in this domain, anticipated economic and social impact, predicted timeline for wider adaptation, and TRLs of the application for wider deployment (also visualized in Figure 6).
## 3 Results and Discussion
### Volatile Profile of the Samples
This component was probably a result of fermentation. It was not present in other products, in contrast to acetic acid ethyl ester, which usually contributes to the fruity and sweet aroma of fruits [65]. The contribution of acids to the total peak area was typically high in the samples. Acetic acid was present in all samples at varying concentrations. Its proportion ranged between 23.5 and 89.2% in the fruit samples. In cereal grasses and moringa powder, it accounted for more than one-third of the total volatiles, while its contribution did not exceed 20% in the vegetable, microalgae, duckweed, and ginger powders. The proportion of acetic acid was only 2.7% in the turmeric powder. Propanoic, butanoic, pentanoic, and hexanoic acids were frequently detected in the samples, but long-carbon-chain fatty acids, like heptanoic, octanoic, and nonanoic acids, were scarce. Formic acid also occurred in some fruit powders with low peak area ratios. Lactones were not present in most samples at detectable amounts. Butyrolactone was identified in seven samples, but its concentration was relatively low. The contribution of this class of compounds to the total volatiles was below 1% in the samples, except for the sea buckthorn powder, which contained lactones at a peak area ratio of 3.4. A total of 36 hydrocarbons were detected in at least one nutraceutical; however, they contributed less than 10% to the total volatiles in almost all samples. On the other hand, the headspace of spirulina powder showed an exceptionally high proportion (37.9%) of hexadecane. The results of a recent study confirm this observation, as this compound was detected at high peak area ratios in the headspace of several spirulina-based food supplements, though it did not contribute significantly to the odors of the products [66]. According to a recent study, the vivid green color and strong aroma limit the usage of microalgae as a functional ingredient [67]. These products can be characterized by a distinct algae-like aroma, encompassing grassy, fatty, and fishy notes, which are attributed to a combination of dozens of volatiles, including aldehydes, ketones, alcohols, acids, esters, furans, and sulfur compounds [68]. The proportion of hydrocarbons was very low in fruit powders, except for aroma, which contained ten different hydrocarbons at a total contribution of 5.3%. Heptane, 2,2,4,6,6-pentamethyll and nonane, 2,2,4,4,6,8,8-heptamethyll were frequently detected, but other hydrocarbons were present in only a few samples. Based on our findings, volatiles are suitable for use as botanical markers of certain nutraceuticals, because the presence or exceptional proportions of certain compounds were characteristic of specific products. For example, high proportions of dimethyl sulfide in beetroot powder, _trans_-3-penten-2-one in aronia powder, hexadecane in spirulina powder, and turmerones in turmeric powder can potentially help the nutraceutical industry in authenticity testing and quality control.
# Monocular Vision Guidance for Unmanned Surface Vehicle Recovery
## 2 Materials and Methods
### Correction of Circular Ring Center Points  
While a circle is approximately represented as an ellipse on the imaging plane and is easier to recognize, there can still be deviations in the center points of the circles due to the effects of perspective transformation. Therefore, we designed a circular marker which allows us to correct the center points using the circular structure.  
Figure 3 depicts the perspective projection image of an individual circle in the artificial marker. In Figure 3, \(X_{c}Y_{c}O_{c}Z_{c}\) represents the camera coordinate system, \(\pi_{1}\) denotes a circular plane within the artificial marker, \(O\) represents the center of the circle, \(EF\) represents the diameter of the circle, \(\pi_{2}\) indicates the ellipse of the circular marker after undergoing perspective transformation on the imaging plane, \(E_{1}F_{1}\) represents the projection of \(EF\  
and \(P_{2}\), and extends the line \(P_{1}P_{2}\) to intersect with the circumference of the two ellipses at points \(A_{1}\), \(B_{1}\), \(C_{1}\), and \(D_{1}\), as shown in Figure 4.  
Next, the linear equation of the line \(A_{1}D_{1}\) can be derived based on the points \(P_{1}\) and \(P_{2}\). Furthermore, utilizing Equation (2) from Section 2.2, the equations for the two ellipses can be obtained. Finally, by simultaneously solving the linear equation of the line and the equations of the ellipses, the pixel coordinates of points \(A_{1}\), \(B_{1}\), \(C_{1}\), and \(D_{1}\) can be determined.  
Finally, based on the cross-ratio invariance principle [28] in perspective projection, the pixel coordinates of the unique center point can be determined. The cross-ratio invariance states that for any projection transformation, when four points \(A\), \(B\), \(C\), and \(D\) on the ring undergo projection transformation and are mapped to new points \(A_{1}\), \(B_{1}\), \(C_{1}\), and \(D_{1}\) on the image plane, their cross-ratio remains unchanged, i.e., \(C_{R}(A_{1}\), \(B_{1}\), \(O_{1}\), \(C_{1})=C_{R}(A\), \(B\), \(O\), \(C)\). The principle of cross-ratio invariance is depicted in Figure 5, where \(S\) represents the camera position, \(l_{1}\) denotes the image plane, \(l\) represents the plane of the artificial marker, and \(O_{1}\) represents the unique center point of the ring on the pixel plane. The cross-ratio formula is shown as Equation (8).  
\[\left\{\begin{array}{l}C_{R}(A_{1},B_{1},O_{1},C_{1})=\frac{S_{R}(A_{1},B_{1 },C_{1})}{S_{R}(A_{1},B_{1},C_{1})}=\frac{A_{1}O_{1}}{B_{1}O_{1}}\times\frac{B_ {1}C_{1}}{A_{1}C_{1}}\\ C_{R}(A,B,O,C)=\frac{S_{R}(A,B)}{S_{R}(A,B,C)}=\frac{A_{1}O}{B\times B\times B \times C}\end{array}\right. \tag{8}\]  
Figure 4: Circular ring in image plane.  
Figure 3: Perspective transformation of a circle.
# Time-Frequency Attribute Analysis of Channel 1 Data of Lunar Penetrating Radar
## 3 Results  
### Analysis in Frequency Domain and the Time-Frequency Domain  
The FT and the STFT are performed to obtain the frequency spectrum (Figure 4) and the time-frequency spectrum (Figure 5) (et al., N104, N106, N202, and N206, for examples), and, thus, the time-frequency spectra will expose the issue and the kind of processing that should be done.  
It can be seen from the frequency spectra of the sample traces (Figure 4) that in the frequency domain, the maximum energy of all sample gathers is around 12 MHz (as indicated by the arrow), whereas at around 60 MHz (the center frequency of CH-1), the energy is not prominent.  
Take a look at the time-frequency spectra of the sample traces:  
Figure 4: Frequency spectrum of traces. (**a**) N104. (**b**) N106. (**c**) N202. (**d**) N206.  
Figure 5: Time–frequency spectrum of (**a**) N104, (**b**) N106, (**c**) N202, and (**d**) N206 traces.  
1. As the arrows in the N104 time-frequency spectrum of STFT show, there are 150, 100, and 60 MHz electromagnetic energies in the data throughout the entire time delay. Obviously, the energies at 150 and 100 MHz are not the transmitting signals. The electromagnetic energies at those frequencies may have come from the lunar space or the instrument (needs further research).
2. Compared to the shallow signal, the energy of the deep electromagnetic signal is very weak. Gain should be processed to highlight the deep information (Figure 6).
3. Both the energies of the high-frequency and low-frequency information in the shallow part are strong. As the time delay increases, high-frequency energy is absorbed, and only some low-frequency information is left. For this kind of nonlinear frequency aliasing phenomenon, a nonlinear decomposition method must be processed.
# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 2 Related Work  
Early object detection relied on traditional machine learning techniques [18, 19, 20, 21], which utilized hand-crafted features and conventional classifiers. These methods achieved moderate success in simple environments but struggled with complex, multi-scale scenarios. The introduction of Convolutional Neural Networks (CNNs) marked a turning point by enabling end-to-end feature learning, significantly enhancing both accuracy and efficiency. Notable early CNN-based detectors include R-CNN [22] and its successors Fast R-CNN and Faster R-CNN [6], which introduced region proposals and hierarchical feature representations. However, their computational complexity limited their suitability for real-time applications. This challenge led to the development of single-stage detectors such as YOLO [23] and SSD [24], which achieved a better balance between speed and accuracy by directly predicting bounding boxes and class probabilities.  
As detection tasks became more complex--particularly in multi-object and multi-scale contexts--there was a growing need to model long-range dependencies and contextual relationships. Attention mechanisms and transformer-based architectures were introduced to address this limitation, fundamentally reshaping object detection. [PERSON] et al. [25] proposed DETR, which formulates detection as a set prediction problem using global attention and a bipartite loss. [PERSON] et al. [10] presented TNT, which enhances local feature extraction by dividing image patches and computing intra-patch attention. [PERSON] et al. [26] developed the Swin Transformer, a hierarchical vision transformer with shifted windows, achieving state-of-the-art performance in both classification and detection tasks.  
More recently, multimodal learning has emerged as a powerful strategy for improving object detection by integrating complementary information from diverse sources, such as RGB images, LiDAR, and radar. For example, [PERSON] et al. [3] introduced a fusion method using a shared backbone for RGB and LiDAR data, yielding significant gains in autonomous driving scenarios. [PERSON] et al. [13] proposed a cross-modal attention framework that dynamically selects relevant modalities, improving the detection of small and occluded objects. In addition, [PERSON] et al. [27] demonstrated the use of transformer-based architectures for multi-sensor fusion, enhancing feature alignment and object localization.
## 2 The Proposed Methodology
### Feature Selection  
Machine learning techniques construct models of labeled objects in order to distinguish them from each other. These objects are described by feature vectors. The accuracy of these models depends upon the quality of these features, i.e., the more discriminative the features, the more accurate the machine learning model. However, not all the features used to describe a given object may be useful in distinguishing it from other objects. That is, some features may be irrelevant or redundant and hence may adversely affect both the classification accuracy of the machine learning model and the time required to construct it [25, 28, 29]. Therefore, feature selection algorithms are an essential part of many machine learning applications as these algorithms help in removing the irrelevant and redundant features to improve the classification accuracy, training time required to construct the model and the reduce the required number of training for better generalization [28, 29, 30, 31, 32, 33].  
Feature selection methods can be broadly divided into the following three categories [28]:  
1. **Filter Based Methods:** These methods use some sort of fitness function to first evaluate and rank different features, and then select a subset of features that have fitness function values above a certain threshold. In essence, these methods filter out the bad features first and then construct the machine learning model. This approach is usually more efficient [28, 29], but its performance depends upon the quality of the fitness function. The feature selection method used in this study can be categorized as a filter based method.
2. **Wrapper Based Methods:** These methods do not filter out the bad features before constructing the machine learning model. Rather, they use the classifier to filter out the bad features. For example, different combinations of features may be used by the classifier, and the combination of features that yields the highest classification accuracy may be selected as the best set of features. This approach can be very time consuming and may only result in a sub-optimal solution [28, 29].
3. **Embedded or Hybrid Methods:** As the name suggests, these methods use an embedded or a hybrid approach. Unlike wrapper based methods, which iterate through different combinations of the features and may select the best subset of features on the basis of the accuracy of the classifier, these methods do not involve such iterative use of the classifier, which improves their speed.  
Figure 1: The proposed methodology for detecting failing disk drives using a genetic algorithm and a feature significance function.  
Similarly, unlike the filter based approaches, these methods do not use a separate fitness function to rank different features. Rather, these methods may use the output of the classifier to select the best subset of features. For example, the weights assigned to different inputs (features) in logistic regression or neural networks may be used to rank them, and select the best subset among them.
## 2 System Modeling
### Idr  
A generalized IDR model is proposed in this section, considering not only the electrical load demand response, but also the heating and cooling loads' demand responses. As shown schematically in Figure 1, we denote by \(\mathbf{H}\) the set of energy hubs, and each energy carrier--i.e., the electricity carrier, heat carrier and cooling carrier, respectively--is attached to each energy hub \(\hbar_{K}\)\(\in\)\(\mathbf{H}\). Let \(\mathbf{D}\) denote the set of real energy demands. Demand at each energy hub \(\hbar_{K}\) is aggregated and denoted by \(P_{K}^{D}(t)\)\(\in\)\(\mathbf{D}\). Let \(\ell_{K,t}^{d}\) be a proportion of the load delivered at energy hub \(\hbar_{K}\) at the time period \(t\). Then, the flexibility interval of multiple energy demand is defined around \(\ell_{K,t}^{d}=1\). Suppose \([\Re_{K,t}^{-}\Re_{K,t}^{+}]\) is the flexibility interval of the demand at energy hub \(\hbar_{K}\) at the time period \(t\). The flexibility of multiple energy demands comes from the responsive loads participating in demand response programs at each energy hub \(\hbar_{K}\). If the demand at energy hub \(\hbar_{K}\) in the time interval \(t\) is flexible, \(0\leq\Re_{K,t}^{-}\leq 1\) and \(\Re_{K,t}^{+}\geq 1\) are used to denote the scaled-down load and the scaled-up load, respectively. If the demand at energy hub \(\hbar_{K}\) is not flexible, then \(\Re_{K,t}^{-}=\Re_{K,t}^{+}=1\). Therefore, the model of the proposed IDR can be formulated in a linear form.  
\[P_{K}^{D^{\prime}}(t)=(1+\Delta\gamma_{K,t})P_{K}^{D}(t) \tag{13a}\] \[\Delta\gamma_{K,t}=\gamma_{K,t}^{+}-\gamma_{K,t}^{-}\] (13b) \[0\leq\gamma_{K,t}^{+}\leq\Re_{K,t}^{+}-1\] (13c) \[0\leq\gamma_{K,t}^{-}\leq 1-\Re_{K,t}^{-} \tag{13d}\]  
where \(\gamma_{K,t}^{-}\) and \(\gamma_{K,t}^{+}\) represent the decrease and increase of proportion in the amount of real energy load delivered to the demand at each energy hub \(\hbar_{K}\), respectively. The subscript \(K\) represents the energy type, which can be electricity, heating or cooling. \((1-\Re_{K,t}^{-})\) and \((\Re_{K,t}^{+}-1)\) indicate the upper limit proportions of the demand \(P_{K}^{D}(t)\) that can be decreased or increased, respectively.  
This paper solves the optimization problem over a 24-h period on an hourly basis. To ensure conservation of the demand at each energy hub \(\hbar_{K}\), total decreased load must be equal with increased amount over the considered time horizon (i.e., a 24-h period), which is described as follows:  
\[\sum\limits_{t=1}^{T}P_{K,t}^{D,dec}=\sum\limits_{t=1}^{T}P_{K,t}^{D,inc} \tag{14}\]
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 3 The Progress of Energy Storage Technologies
### Sodium-Sulfur Batteries  
NaS battery is one of the most typical secondary batteries with sodium metal as the electrode, and it is a large-scale static energy storage technology with very successful applications [21]. By 2015, sodium-sulfur batteries were leading the way with 40% to 45% of the global electrochemical energy storage. NaS batteries use molten sodium and molten sulfur as the two electrodes and \(\beta\)-alumina as the solid electrolyte. Sodium-sulfur batteries have high specific energy (760 Wh/kg), high capacity (up to 600 Ah), high power density, high Coulomb efficiency (almost zero self-discharge and almost 100% efficiency), pollution-free battery operation, a long life (10-15 years), and a simple battery structure. However, its limitations are the high annual operating costs ($80/kW/year) and the need for additional systems to ensure their operating temperature [72]. Research and development have focused mainly on improving the performance index of the battery and reducing/eliminating the limitations of high-temperature operation [73; 74; 75; 76]. During the Shanghai World Expo 2010, the Shanghai Institute of Silicate, the Chinese Academy of Sciences, and Shanghai Electric Power Company collaborated to achieve grid-connected operation of a 100 kW/800 kWh sodium-sulfur battery energy storage system. [PERSON] et al. [77] proposed a new sodium-ion battery for the need for large-scale energy storage due to the use of electric vehicles and renewable energy sources today. They emphasized that sodium-ion batteries are potentially less expensive, safer, and more environmentally friendly than lithium-ion batteries. In the search for a suitable host material, Na\({}_{4}\)Mn\({}_{9}\)O\({}_{18}\) accommodates sodium ions and allows reversible and fast ion insertion and extraction.