esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 X-ray Image Key Target Tracking and Extraction
### X-ray Image Key Target Tracking
Its definition is shown in Formula (12):  
\[\text{DIoU = IoU}-\frac{\rho^{2}\big{(}\text{b},\text{b}^{\text{gt}}\big{)}}{ \text{c}^{2}}, \tag{12}\]  
where  
b--the center point of the prediction box;  
b\({}^{\text{gt}}\)--the center point of the real box;  
\(\rho\)--the Euclidean distance between two center points;  
c--the diagonal distance of the minimum closure region that can contain both the predicted box and the real box. Similar to GIoU, DIoU can provide direction of movement for the bounding box when it does not overlap with the target box. DIoU can directly minimize the distance between the two target boxes, which converges much faster than GIoU. As for the case involving two boxes in both horizontal and vertical directions, the DIoU can make the regression very fast, while the GIoU almost degenerates into the IoU. DIoU can also replace the normal IoU evaluation strategy and be applied to the NMS to make the results of the NMS more reasonable and effective. Figure 20: The same IoU in different cases. The aspect ratio of the three elements of bbox regression (bounding box regression) is not considered in the calculation, and CIoU is proposed on the basis of DIoU. Its definition is shown in Formula (13):  
\[\text{CIoU}\ =\ \text{IoU}-\frac{\rho^{2}\big{(}\text{b},\text{b}^{\text{gt}} \big{)}}{\text{c}^{2}}-\alpha v, \tag{13}\]  
where \(v\) is used to measure the similarity of the aspect ratio, as shown in Formula (14):  
\[v\ =\ \frac{4}{\pi^{2}}\bigg{(}\arctan\frac{w^{gt}}{h^{gt}}-\arctan\frac{w}{h} \bigg{)}^{2}. \tag{14}\]  
The above four loss functions are used to judge the degree of coincidence of two targets in target detection and related fields. Since IoU has many shortcomings, DIoU and CIoU loss functions are introduced to improve the target frame regression. In the case of target tracking, GIoU is more suitable for relatively simple use.
## 3 Results and Analysis
### Response Law of Outlet Flow and Output Power of AFT to Rotational Speed
#### 3.1.2 Variation Law of the Output Power of the Axial-Flow Turbine with Rotational Speed  
When \(H\) = 0.14-0.18 MPa and \(n\) = 500-3000 rpm, the \(P\) of the 12 AFTs ranges from 35 to 310 W. \(P\) increases with \(n\), then decreases, increases with \(H\), and decreases as M\({}_{1}\) and M\({}_{2}\) increase (Figure 9).  
Figure 8: Comparison of the simulated value (\(Q_{\text{s}}\)) and measured value (\(Q_{\text{m}}\)) of AFT outlet flow.  
Figure 7: Design flow chart of hydrodynamic fertilizer injector based on CFD numerical simulation and hydraulic performance measurement.  
The maximum value of \(P\) (\(P_{\max}\)) in Figure 9 was extracted for analysis (Table 2). Since \(P_{\max}\) exhibited minimal variation with M\({}_{3}\), a significance analysis was conducted solely on the effects of M\({}_{1}\) and M\({}_{2}\) on \(P_{\max}\). When \(H\) = 0.14-0.18 MPa, \(P_{\max}\) for D33 was the highest, 5.3-55.6% greater than that of the other AFTs. Additionally, \(P_{\max}\) at M\({}_{1}\) = 2 was 9.1% and 27.7% higher than that at M\({}_{1}\) = 3 and M\({}_{1}\) = 4, respectively. The influence of M\({}_{1}\) on \(P_{\max}\) reached a statistically significant level (\(p<0.05\)). The average \(P_{\max}\) of D33 across the five \(H\) values was 6.7-33.5% greater than that of the other AFTs, and the average \(P_{\max}\) at M\({}_{1}\) = 2 was 8.7% and 22.7% greater than that at M\({}_{1}\) = 3 and M\({}_{1}\) = 4, respectively. The influence of M\({}_{1}\) on the mean \(P_{\max}\) also reached a statistically significant level (\(p<0.05\)). Compared with D33, the average \(P_{\max}\) for D34, D43, and D44 decreased by 10.7%, 6.3%, and 15.1%, respectively.  
Figure 9: The output power (\(P\)) versus rotational speed (\(n\)) curves of different AFTs at \(H\) = 0.14 (**a**), 0.15 (**b**), 0.16 (**c**), 0.17 (**d**), and 0.18 MPa (**e**).
## 3 Research Method
### Inclusion and Exclusion Criteria  
The studies as available in the literature are selected for the mapping study on the basis of their ability to satisfy the inclusion criteria. The studies that satisfies the exclusion criteria or do not satisfy the inclusion criteria are filtered out from the further analysis. The inclusion and exclusion criteria as formulated for the mapping protocol are mentioned below.  
**Inclusion criteria**  
* Research studies written in English language only.
* Research studies with the full text available in the bibliographic database.
* Studies with potential to answer either of the research question(s) as formulated in Section 3.1.  
**Exclusion criteria*** Studies that are editorials.
* Studies that are keynote addresses (if the full text not available).
* Studies that are proposals for tutorials.
* Studies that are academic theses.
* Studies that highlight the initial draft of work in progress type articles.
## 3 Proposed Method
In other words, since classifications using only the information of a specific moment are highly likely to fail, consecutive joint angles were used for interaction predictions in this study. Figure 6 shows an example of deriving consecutive joint angles. The joint angles of five fingers are calculated from each frame (frame 1, frame 2, frame 3, frame 4, and frame 5), and calculated values from 5 consecutive frames become one joint angle set. In general, a human gesture lasts 0.5 to 1 s [26]. In order for the proposed method to have meaning as a prediction, it was judged that the time required for the prediction itself should be about half of the duration of the gesture. Therefore, we tried to perform predictions through 5 frames at 30 fps (about 0.17 s). The dataset used in this study consists of joint angle sets calculated as above. In this study, the joint angle sets were created by performing grab and pinch gestures for virtual objects generated at random locations by 3 users. Joint angle sets included in the dataset were derived through 5 consecutive frames from when each gesture was started. To create the dataset, 3 users performed grab and pinch gestures 100 times each, and through this, a dataset including a total of 300 finger joint angle sets for gesture interactions was obtained. The procedure for performing the proposed interaction prediction described above is shown in Figure 7, and the detailed description is as follows:  
Figure 5: Joint angles (\(\theta_{1}-\theta_{5}\)) for each finger used as representative values: (**a**) back of the right hand; (**b**) palm of the right hand. 1. Original image input: the hand joint raw data is obtained when the original image is input;
2. Joint angle calculation: each joint angle data is calculated from the obtained hand joint raw data;
3. Interaction prediction: the corresponding interaction is predicted using the k-NN algorithm with the joint angle set as the input. The input joint angle set is classified as grab, pinch, or none (which is neither grab nor pinch; see the red box in Figure 7);
4. Prediction result check: by confirming the prediction result, the interaction prediction of the proposed method is completed (see the red box in Figure 7). In this study, 'interaction prediction' means that the user's gesture is classified as a defined specific gesture through the above process. The first frame among the consecutive frames used for classification is considered as the user's gesture starting moment. We examined whether our procedure worked well by comparing the obtained hand gestures with actual ones. It was confirmed that the proposed method could classify users' hand gestures into grab, pinch, and none through the additional test.
# Limitations of Large Language Models in Propaganda Detection Task
## 1 Introduction
### Motivation  
Research on propaganda in online news, especially in under-resourced languages, is crucial due to the significant influence of online platforms on shaping public opinion and attitudes [7]. Understanding propaganda's presence and impact is important for ensuring democratic processes and informed decision-making. With technological advancement, the spread of propaganda has become more sophisticated and challenging to detect [8]. Moreover, speakers of under-resourced languages are more prone to the lack of access to credible information, especially in regions where there is an ongoing conflict or where access to information is restricted or controlled by a regime, making them vulnerable to manipulation [9; 10]. Delving into the use of propaganda in under-represented languages could help people in the critical evaluation of information and reveal the tactics used to influence them.  
Media Bias/Fact Check (MBFC) is a website that assesses the _political bias_ and _factual reporting_ of media outlets; they prepared a general analysis of Poland's political orientation and press freedom [11]. In accordance with their internally developed measure, Poland's freedom rating was equal to 74.33--_mostly free_ and Poland's political orientation was rated as _right-center_. In the Press Freedom Index of 2023, Poland was positioned at 57 th out of 180 countries, as reported by Reporters Without Borders [12]. This position was due to changes in the law that happened during the ruling of the Law and Justice party (PiS) that allegedly restricted press freedom and freedom of speech. According to the report on the MBFC website, this government increased its political influence over state institutions, including judiciary and public media. In October 2023, parliamentary elections took place and the Law and Justice party no longer has a majority in the parliament. The currently ruling coalition of parties is undertaking actions to change the order in the country after the Law and Justice ruling, including the national media. This year's report will show whether any real changes are observed.  
Furthermore, research on propaganda in under-resourced languages can contribute to the creation of language-specific detection tools and techniques to effectively identify and combat misinformation [8]. By addressing the distinctive linguistic and cultural characteristics of such languages, researchers can help to promote media literacy. Understanding how propaganda operates is essential for supporting democratic values, human rights and freedom of expression.  
Overall, we think that research on propaganda in online news in under-resourced languages is essential for promoting information integrity, the protection of democracy and the critical understanding of online content. Therefore, studies in this area are important for developing effective tools that enable the identification and tackling the influence of propaganda in online news, eventually popularizing transparency, accuracy and credibility in media reporting.
## 3 Literature Review and Related Work
### Positioning Implementing Time-Series of Spatially Distributed Fingerprints  
We surveyed research works on fingerprinting-based positioning implementing time-series of spatially distributed fingerprints. The works reviewed are listed in Table 4. In all of the works consulted, the fingerprints are collected with the use of a single sensor or receiver by moving a positioning device along a path, also referred to as a trajectory or route in different works. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Fingerprints** & **NN Type** & **Fingerprint Collection Method** & **\# Side Inf.** \\  & & **Used** & & & **Dimensions** \\ \hline
[164] & 2017 & Geomagnetic & RNN & Along a path & 1D \\ \hline
[165] & 2018 & RSS & LSTM & Along a path & 1D \\ \hline
[166] & 2018 & RSS, magnetic & 1D CNN & Along a path of 10.4 m & 1D \\ \hline
[167] & 2018 & RSS, magnetic & 1D CNN & Along a path of 10.4 m & 1D \\ \hline
[168] & 2018 & RSS & 1D CNN & Along a path, spaced at 1.2 m & 1D \\ \hline
[169] & 2019 & RSS & LSTM & Three trajectory paths in an area of 4.0 m \(\times\) 4.0 m & 1D \\ \hline
[170] & 2019 & RSS & LSTM & Along a path & 1D \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fingerprinting-based positioning implementing time-series of spatially distributed fingerprints (downlink case). It is noted that the application of machine learning methods to the processing of the time-series of fingerprints implies that the fingerprints are aggregated in the fingerprint domain, as discussed in Section 2.4. For the processing of the time-series, the literature surveyed adopts 1D CNNs; recurrent neural networks (RNNs); and Long Short-Term Memory (LSTM) NNs [197; 198].
# Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
## 5 Data Processing
### Image Registration  
For the ATI-SAR system, due to the existence of the along-track baseline, the pixels in the front and rear SLCs do not completely correspond, and there are several azimuthal pixel offsets for the position of the same target in the front and rear SLCs. Therefore, accurate registration of SLCs is necessary, to obtain effective ATI phases, and it is generally required that the registration accuracy should reach the sub-pixel level [4], otherwise the interference phases will be mismatched, which has a serious impact on retrieving the sea surface current field. In this paper, the correlation coefficient method [21] was adopted for SAR image registration. The registration processing is shown in Equations (10) and (11).  
Figure 5: Data processing flow.  
\[s_{azi}=IFFT\bigg{(}FFT(s_{a})\cdot\exp\bigg{(}-j\cdot 2\pi\cdot f_{d}\cdot \frac{\Delta d}{v_{s}}\bigg{)}\bigg{)}, \tag{10}\] \[s_{ar}=IFFT\bigg{(}FFT(s_{azi})\cdot\exp\bigg{(}-j\cdot 2\pi\cdot f_{r} \cdot\frac{\Delta R}{v_{c}}\bigg{)}\bigg{)}, \tag{11}\]  
where \(s_{a}\) is the rear SLC image data, \(s_{azi}\) is the azimuth registration result, \(s_{ar}\) is the range registration result after the azimuth registration is completed. FFT signifies fast Fourier transformation in the azimuth direction in Equation (10). In Equation (11), FFT is in the range direction. IFFT means inverse fast Fourier transformation, and the transformation direction is the same as in the FFT operation. \(f_{d}\) is the Doppler frequency, ranging from \(-\)PRF/2 to PRF/2, and \(f_{r}\) is the range frequency ranging from \(-\)B/2 to B/2 (B is the bandwidth of the radar system). \(v_{s}\) is the satellite velocity, and \(v_{c}\) is the light speed. \(\Delta d\) is the azimuth distance of the two channels. \(\Delta R\) is the range distance of the two channels. \(j\) is the imaginary unit.  
After image registration processing, the sliding window method [15] is employed for processing data, and phase filtering is performed to obtain the neighborhood average. In this study, the window size was 64 \(\times\) 64 pixels. For ATI-SAR, inherent phase error correction [20] is a vital step in the procedure. The phase errors are corrected every 64x64 pixels along the range direction. Then, these phase errors are eliminated, to complete the phase error correction.
## 4 Experimental Section
### Metrics
#### 4.1.1 Load Distribution  
The first metric, a cost/efficiency related criterion, measures the load distribution among Anchor Nodes. In a computer network, load distribution or load balancing is used to distribute workloads across multiple computing resources, such as computers, central processing units (Sink Nodes), or wireless nodes. Load distribution (balancing) aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any of the resources.  
In this research, we are interested in measuring how efficient the three clustering approaches are at distributing the load among Anchor Nodes. To measure load distribution among Anchor Nodes, we propose utilizing the statistical variance of the number of times each anchor is employed to estimate a distance to a Mobile Node. The variance explains how the workloads are distributed among Anchor Nodes by the cluster. In other words, the lesser the variance the more distributed the workloads among wireless nodes, _i.e.,_  
\[\begin{array}{c}f^{lds}=Var\left(X\right)=\ \frac{\sum_{i=1}^{N}\left(X_{i}- \mu\right)^{2}}{N-1}\\ X_{i}=number\ of\ times\ anchor\ i\ is\ utilized\end{array} \tag{1}\]  
where \(N\) is the number of Anchor Nodes and \(\mu\) is the mean of \(X_{i}\ i=1,2,\ldots,N\).
## 3 The ASLM Architecture
### Model Description
#### 3.3.1 Long-Term Preference Layer  
For the short-term intent layer, it learns about the user's intent throughout the session, but discards all the information at the end of the session. Similar to the model proposed in [7], we employ a GRU to take the user's \(k\) most recent history sessions into account. At the beginning of each session, the GRU models the user's long-term preference representation \(P_{L}^{u}\) from the user \(u\)'s history session representations and provides it for the GRU-based decoder in the attention-based short-term preference layer as the initial hidden state.  
The graph inside the dark green box in Figure 2 illustrates the work flow of the Long-term Preference Layer. For each session \(S_{t}^{u}\) in \(u\)'s interaction history \(S^{u}\), let \(s_{t}^{u}\) be an embedded vector representation of that session. The Long-term Preference Layer takes a series of vector representation of \(u\)'s \(k\) most recent history sessions \(\{s_{t-k}^{u},s_{t-k+1}^{u},\dots,s_{t-1}^{u}\}\) as input, where \(s_{t-1}^{u}\) is the most recent history session. Afterwards, the layer produces the initial hidden state, \(H_{0}\), of the Attention-based Short-term Intent Layer before the short-term intent layer starts making predictions. As a result, it enables the Attention-based Short-term Intent Layer to combine both the user's short-term intent and long-term preference at the attention process.
# Detection of High-Temperature Gas Leaks in Pipelines Using Schlieren Visualization
###### Abstract  
This paper investigates the application of Schlieren flow visualization for detecting leaks in pipelines carrying high-temperature fluids. Two experimental setups were constructed: one with a 25 mm PTFE tube featuring a 2 mm diameter perforation, and another with a 100 mm diameter pipe insulated with an aluminum jacket and featuring a 12 mm leak gap. A single-mirror-off-axis Schlieren system, employing a 150 mm diameter parabolic mirror, was used to visualize the leaks. The temperature of the leaking air varied between 20 and 100 \({}^{\circ}\)C, while the ambient temperature was maintained at 14 \({}^{\circ}\)C. To quantify the leaks, the coefficient of variation for pixel intensity within the leak region was calculated. Results showed that for the PTFE tube, leaks became detectable when the temperature difference exceeded 34 \({}^{\circ}\)C, with the coefficient of variation surpassing 0.1. However, in the insulated pipe, detecting clear leak patterns was challenging. This research demonstrates the potential of Schlieren visualization as a valuable tool in enhancing pipeline leak detection.  
Schlieren imaging; flow visualization; leak detection; nuclear power plant; coefficient of variation +
Footnote †: journal: Medical Engineering  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
2024148567[[https://doi.org/10.3390/app14188567](https://doi.org/10.3390/app14188567)]([https://doi.org/10.3390/app14188567](https://doi.org/10.3390/app14188567))
## 5 Performance Evaluation of FVCOM
### Ideal Experiment: FVCOM-Benchmark  
An ideal ultra-high resolution grid for the FVCOM-Benchmark experiment was constructed with approximately 10 million grid points (nodes = 10,398,925). The right image in Figure 15 shows the detailed experimental configuration. The interval of every grid node (= point) is about 10 m for the integration efficiency and stability of the simulation. The internal angles of the triangle grid elements were set close to the angles of an equilateral triangle, at approximately 60\({}^{\circ}\), for a more stable simulation (see the left image in Figure 15). The ideal sea surface height (SSH), at 1 m, was set to the southern side of the grid for an open boundary condition (OBC).  
The vertical grid was divided into 31 layers. The simulation period was 4 h with a time interval of 1 s in consideration of the grid size. FVCOM-Benchmark experiments were also configured for allocated cores and nodes (refer to Table 3). The benchmark experiment for an ideal case is suitable for comparing the performances of computational resources due to the low I/O data loads. We used 128, 256, and 512 cores to compare the performances of parallel systems according to the experimental results. In addition, a 1024 ultra-high resolution experiment was performed on the KNL system to test its performance limits.  
The results of each experiment are summarized in Figure 16. The KNL system performed at an approximate 44% rate compared to the performances of SKL and CLS. The 1024 core experiment showed a continuous increase in the parallel performance of the KNL system. FVCOM also clearly improved the performance of the 1024 core configuration compared to WRF and ROMS. The speed of the 1024 core experiment in KNL is close to those of the 512 core experiments in SKL and CLS.  
Figure 15: Enlarged unstructured triangular grids (left) and the experimental configuration (right) for the FVCOM ideal case. The total number of grid points (=nodes) is 10,392,925.
###### Abstract  
Fluidized bed applications where the bed material plays an active role in chemical reactions, e.g. chemical looping combustion, have seen an increase in interest over the past decade. When these processes are to be scaled up to industrial or utility scale mass transfer between the gas and solids phases can become a limitation for conversion. Confined fluidized beds were conceptualized for other purposes in the 1960's but are yet to be applied to these recent technologies. Here it is investigated if they can prove useful to increase mass transfer but also if they are feasible from other perspectives such as pressure drop increase and solids throughflow. Four spherical packing solids, 6.35-25.4 mm in diameter at two different densities, were tested. For mass transfer experiments the fluidizing air was humidified and the water adsorption rate onto silica gel particles acting as fluidizing solids was measured. Olivine sand was used in further experiments measuring segregation of solids and packing, and maximum vertical crossflow of solids. It was found that mass transfer increased by a factor of 1.9-3.8 with packing solids as compared to a non-packed reference. With high-density packing, fluidizing solids voidage inside the packing was found to be up to 58% higher than in a conventional fluidized bed. Low density packing material favoured its flotsam segregation and with it higher fluidization velocities yield better mixing between packing and fluidizing solids. Maximum vertical cross-flow was found to be significantly higher with low density packing that fluidized, than with stationary high-density packing. Conclusively, the prospect of using confined fluidized beds for improving mass transfer looks promising from both performance and practical standpoints.  
confined fluidization; fluidized-packed bed; fluidized bed; gas-solids mass transfer; active bed material; cold flow modelling; oxygen carrier; chemical looping combustion +
Footnote †: journal: Applied Physics Letters