esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Design
### Study Design and Procedure  
In this study, we devised a task to detect lesions in MRI images and administered a questionnaire about the explanation methods used during the task. Each radiologist was presented with a random MRI image and an accompanying explanation method. To ensure fairness, we balanced this study so that each radiologist saw four samples for each explanation method. We also included a control case where the radiologist was shown an MRI image without any explanation method. Our hypothesis was that the incorporation of an explanation method would lead to improved diagnostic accuracy in comparison to the absence of an explanation method.  
Each of the ten radiologists who participated in the study was provided with twenty unique samples that were randomly selected from the PROSTATEx testing split. For each sample, a corresponding explanation method was also provided. To ensure that the evaluation was fair, each radiologist was presented with four samples for each explanation technique, and the order of presentation was randomized within each subject. Additionally, five unique MRI images were presented to each radiologist without any corresponding explanation method to serve as a baseline for comparison. For all 25 samples, the radiologists were asked to determine whether a lesion was present after each MRI and explanation technique was shown to them. After completing this task, the radiologists were given a questionnaire that requested them to rate each explanation method on a 5-point Likert scale and provide feedback on the aspects they preferred and did not prefer for each explanation method. For the five MRI samples without any corresponding explanation method, the radiologists were only asked to indicate if there was a lesion present or not.  
The researchers did not obtain approval from an Institutional Review Board (IRB) for this study because the data used in the research were publicly available and de-identified; therefore, IRB approval was not required. The IRB states that the analysis of such data does not meet the criteria for human subjects research, as outlined in the Code of Federal Regulations, and therefore, this analysis was not subject to IRB review.
# On the Impact of Particulate Matter Distribution on Pressure Drop of Wall-Flow Particulate Filters
## 3 Experimental Setup
### Tests  
Several types of tests were performed in order to evaluate the pre-DPF water injection technique impact on the DPF performance. Every kind of test was repeated twice, thus comparing the test under baseline engine and DPF operation (without pre-DPF water injection) against the test in which pre-DPF water injection was applied.  
Several DPF soot loading tests were performed to analyze the effects on the DPF pressure drop of the pre-DPF water injection technique under controlled conditions. In particular, a soot loading tests up to 30 g (11 g/L) in soot mass has been selected as a basis for the pressure drop and filtration efficiency study presented in this work. During all soot loading tests, the engine was run at 2500 rpm and 28% in engine load being the exhaust gas recirculation rate 16%.  
After every soot loading test, the engine was subjected to different operating conditions in order to verify the pressure drop decrease and fuel economy benefit in a wider range of operation. The study covered driving cycles, motoring conditions at several engine speeds and steady-state operating conditions of low engine load. Finally, the DPF was regenerated applying an active regeneration based on an in-cylinder post-injection strategy. A regeneration process has been also selected for its modeling in the present work. In the selected test, the DPF was firstly loaded up to 60 g followed by two consecutive New European Driving Cycles (NEDC) before regeneration. This DPF loading process was repeated twice covering baseline operation and use of pre-DPF water injection, thus allowing the comparison of the corresponding regeneration processes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Type** & **HSDI Diesel Passenger Car** \\ \hline Emission standards & Euro 4 \\ Displacement & 1997 cm\({}^{3}\) \\ Bore & 85 mm \\ Stroke & 88 mm \\ Number of cylinders & 4 in line \\ Number of valves & 4 per cylinder \\ Compression ratio & 18:1 \\ Maximum power @ speed & 100 kW @4000 rpm \\ Maximum torque @ speed & 320 Nm @ 1750 rpm \\ Aftertreatment & Close-coupled DOC + Underfloor DOC-DPF \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic engine characteristics.
## 2 Methods
### T0 Difference Method  
The T0 difference method was proposed by [PERSON] et al. [40] in 1959. It defines two time values: \(t_{+}\) and \(t_{-}\).  
As shown in Figure 1, \(i\) is the incident angle and the three ray paths are \(O_{1}ABS\), \(O_{2}DCS\), \(O_{1}ADO_{2}\), respectively. \(t_{+}\) and \(t_{-}\) are expressed as:  
\[t_{+}=t_{O_{1}ABS}+t_{O_{2}DCS}-t_{O_{1}ADO_{2}} \tag{1}\]  
\[t_{-}=t_{O_{1}ABS}-t_{O_{2}DCS}+t_{O_{1}ADO_{2}} \tag{2}\]  
We remove the same part of the ray path, \(t_{+}\) can be expressed as:  
\[t_{+}=t_{BS}+t_{CS}-t_{BC} \tag{3}\]  
The interface depth of point \(S\) is \(h_{S}\), the velocity of the upper layer is \(v_{1}\), the refractive layer velocity is \(v_{2}\), \(t_{+}\) and \(h_{S}\) can be expressed as:  
\[t_{+}=\frac{2h_{\rm s}(1-\sin^{2}i)}{v_{1}\cos i}=\frac{2h_{\rm s}\cos i}{v_{ 1}} \tag{4}\]  
\[h_{S}=\frac{v_{1}}{2\cos i}t_{+} \tag{5}\]  
According to [PERSON]'s theorem, we can get the following formula:  
\[h_{S}=\frac{v_{1}v_{2}}{2\sqrt{(v_{2}+v_{1})(v_{2}-v_{1})}}t_{+} \tag{6}\]  
where \(t_{1}\) represents the travel time of the path \(O_{1}ABS\), \(t_{2}\) represents the travel time of the path \(O_{2}DCS\), \(T\) represents the travel time of the path \(O_{1}ADO_{2}\), then  
\[t_{-}=t_{1}-t_{2}+T \tag{7}\]  
Therefore,  
\[\frac{\Delta t_{-}}{\Delta x}=\frac{\Delta t_{1}}{\Delta x}-\frac{\Delta t_{2 }}{\Delta x} \tag{8}\]  
Therefore, the apparent velocity of refraction accepted in the forward direction and the apparent velocity of refraction accepted in the reverse direction can be expressed as:  
\[V_{-}^{*}=-\frac{\Delta t_{2}}{\Delta x} \tag{9}\]  
\[V_{+}^{*}=\frac{\Delta t_{1}}{\Delta x} \tag{10}\]  
From the apparent velocity theorem of refraction wave, Formula (8) can be obtained as follows:  
\[\frac{\Delta t_{-}}{\Delta x}=\frac{2}{v_{2}} \tag{11}\]  
The normal depth of refraction interface of Formula (6) can be calculated by calculating \(v_{2}\).  
Two Layer Undulating Interface Model  
As shown in Figure 2, there are two layers of random undulating interface model. The model parameters are as follows: 200 m in the transverse direction, 20 m in the longitudinal direction, the interface depth varies from 5 m to 10 m. The velocity of the model is 1000 m/s for the first layer, the velocity of the model is 3000 m/s for the second layer. The red star shaped mark is the shot point, the red triangle mark is the detection point, the shot detection distance is 2 m, group interval is 2 m, 100 geophones.  
The red prismatic points are calculated results, which are basically consistent with the random interface fluctuation of the model. The calculated velocity is 1126.9 m/s for the first layer and 2992.9 m/s for the second layer, which has a small error with the original model. Therefore, the results of T0 difference method are very accurate. Therefore, we will use T0 difference method as a prior constraint of refraction travel time tomography.
## 4 Experimental Results and Analysis
### Experimental Results and Analysis of Optical Cavity Boundary Layer Control
#### 4.1.1 Solid Plate Experiment Results
Figure 7 shows the pressure distribution curve along the chemical reaction platform when the SCRP was operating independently without the supersonic diffuser. The first four measuring points were located in the optical cavity, and the latter was located in the connection section between the optical cavity and the vacuum tank. The results in the figure show that with the chemical reaction, the pressure distribution throughout the entire process increases, which also verifies the above analysis from another aspect. Figure 5: Pressure in the optical cavity during SCRP operation without supersonic diffuser. Figure 6: Energy power during independent SCRP operation.
## 2 Materials and Methods  
### Materials  
6-hydroxy-2,5,7,8-tetramethylchroman-2 carboxylic acid (Trolox), 2,4,6-tripyridyl-s-triazine (TPTZ), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and a medium for lactic acid bacteria (CM039) were acquired from Sigma Aldrich (Sigma-Aldrich Merck Group, St. Louis, MO, USA). Gallic acid and Folin-Ciocalteu reagent were purchased from Merck, Darmstadt (Germany). Acetic acid (99.5%), glycerin, methanol (96%), and phosphate-buffered saline (PBS; pH 7.00 \(\pm\) 0.05) were purchased from Chempur (Piekary Slaskie, Poland). Acetonitrile (J.T. Baker brand) for HPLC was purchased from Avantor Performance Materials Poland S.A. (Gliwice, Poland). Hydroxyethylcellulose was purchased from Mazidla.com (Poznan, Poland). Silymarin, silybin, taxifolin, isopropyl myristate, and Pluronic F-127 were acquired from Sigma-Aldrich (St. Louis, MO, USA). Soy lecithin was acquired from Nanga (Karagen Kappa, Warszawa, Poland). All reagents used were of analytical grade.  
Chempur (Piekary Slaskie, Poland) provided the acetic acid, ethanol (96%), methanol, phosphate-buffered saline (PBS, pH 7.4), potassium hydroxide (KH\({}_{2}\)PO\({}_{4}\)), dipotassium hydrogen phosphate (K\({}_{2}\)HPO\({}_{4}\)), sodium phosphate dibasic dihydrate (Na\({}_{2}\)HPO\({}_{4}\) 2H\({}_{2}\)O), ammonium chloride (NH\({}_{4}\)Cl), magnesium(II) sulfate heptahydrate (MgSO\({}_{4}\) 7H\({}_{2}\)O), calcium chloride dihydrate (CaCl\({}_{2}\) -H\({}_{2}\)O), potassium hydroxide (KOH), sodium hydroxide (NaOH), barium hydroxide (Ba(OH)\({}_{2}\)), orthophosphoric acid (H\({}_{3}\)PO\({}_{4}\)), and iron (III) chloride hexahydrate (FeCl\({}_{3}\) -6H\({}_{2}\)O). Formic acid was acquired from Supelco (Bellefonte, PA, USA) (98-100% for HPLC LiChopurTM). Eucerin was provided by Fagron (Krakow, Poland), and zinc paste was provided by Aflofarm (Pabianice, Poland). Every reagent was of analytical quality.  
A medium for lactic acid bacteria (CM0359) was purchased from OXID (M.R.S. [PERSON], [PERSON], Sharpe). Strains of lactic acid bacteria (_Lactobacillus reuteri_ MI_0168, _Lactobacillus salivarius_ LY_0652, _Lactobacillus brevis_ LY_1120, _Lactobacillus acidophilus_ MI-0078, _Lactobacillus rhamnos_ MI-0272, _Lactobacillus plantarum_ MI-0102, and _L. rhamnos_ LY-0457) were purchased from Probitical (NOVARA). BIO cane molasses (NatVita) was purchased from Mirkow, Poland. Lipase AY30 was purchased from Thermo Scientific (Bialystok, Poland). Sodium dodecyl sulfate (SDS; Sigma-Aldrich, 99.0%) is a substance that is frequently used in biodegradation research as both a benchmark and a positive control [28].
## 6 Discussion  
In the present paper, given \(\{\mathbf{\beta}^{(t)},\alpha\}\), to avoid directly calculating \(\mathbf{\beta}^{(t+1)}\) in the maximization of the original log-likelihood function \(\ell(\mathbf{\beta},\alpha)\), we successfully constructed a surrogate function \(Q_{1}(\mathbf{\beta}|\mathbf{\beta}^{(t)},\alpha)\), which is equivalent to the log-likelihood function in a weighted Poisson regression, so that we can compute \(\mathbf{\beta}^{(t+1)}_{*}\) directly by using the \(\mathsf{VGAM}\) R package. By projecting \(\mathbf{\beta}^{(t+1)}_{*}\) on the convex set \(\mathbb{C}^{(t)}\), we calculated \(\mathbf{\beta}^{(t+1)}\) as shown in (14). Besides, given \(\{\mathbf{\beta},\alpha^{(t)}\}\), we obtained an explicit expression for \(\alpha^{(t+1)}\) by maximizing a surrogate function \(Q_{2}(\alpha|\mathbf{\beta},\alpha^{(t)})\). The simulation and real data analysis results showed that the proposed MM algorithms could stably obtain the MLEs of parameters for the GP\({}^{(0)}\) distribution without/with covariates for various parameter configurations, while the built-in 'genpoisson1' function in the \(\mathsf{VGAM}\) R package may converge to a wrong estimate of parameters. Besides, the results of the comparison between the proposed model and the existing CMP regression model reflected that the two models possess similar performance from the aspect of the goodness-of-fit. However, the proposed model outperforms the CMP regression model regarding computational efficiency and statistical meanings.  
Conceptualization, X-J.L. and G.-L.T.; Methodology, X-J.L. and G.-L.T.; Formal analysis, X.-J.L.; Investigation, M.Z., G.T.S.H. and S.L.; Resources, M.Z., G.T.S.H. and S.L.; Data curation, M.Z., G.T.S.H. and S.L.; Writing--original draft, X.-J.L.; Writing--review & editing, X.-J.L. and G.-L.T.; Supervision, G.-L.T.; Funding acquisition, G.-L.T. and G.T.S.H. All authors have read and agreed to the published version of the manuscript. National Natural Science Foundation of China: 12171225; Research Grants Council of Hong Kong: UGC/FDS14/P05/20; Big Data Intelligence Centre in The Hang Seng University of Hong Kong.  
Publicky available datasets were analyzed in this study. This data can be found here: [[[https://www.dhsprogram.com/data](https://www.dhsprogram.com/data)]([https://www.dhsprogram.com/data](https://www.dhsprogram.com/data))].  
[PERSON]'s research was partially supported by National Natural Science Foundation of China (No. 12171225). [PERSON] would like to thank the Research Grants Council of Hong Kong for supporting this research under the Grant UGC/FDS14/P05/20. Furthermore, this research is also supported partially by the Big Data Intelligence Centre in The Hang Seng University of Hong Kong.  
The authors declare no conflict of interest.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 4 Experiments and Evaluation
### Comparative Experiments  
Table 2 compares the data of the lightweight model proposed by us after pruning with the current mainstream target detection models Faster-RCNN, SSD, Centernet, YOLOv3, and YOLOv4. From the data in the Table 2, we can see that the mAP of the YOLOv4-MobileNetv3 model is 83.03%, 14.28% higher than Faster-RCNN, 4.85% higher than SSD, 4.05% higher than Centernet, 2.93% higher than YOLOv3, and 1.44% lower than YOLOv4. The parameter size is 9.42 M, which is 93.13% less than Faster-RCNN, 63.98% less than SSD, 71.17% less than Centernet, 84.72% less than YOLOv3, and 85.29% less than YOLOv4. Compared to Faster-RCNN, SSD, Centernet, and YOLOv3, our improved model achieves the best mAP with minimal parameters. The accuracy of the pruned and fine-tuned YOLOv4-MobileNetv3 model remains lower compared to the original YOLOv4 network. This disparity can be attributed to the inadequate feature expression capability resulting from the utilization of a lightweight feature extraction network. Consequently, the model struggles to capture abundant information and target features in the input image, leading to a decrease in the detection accuracy. In contrast, we made a compromise by sacrificing 1.44% of the mAP in order to reduce the model parameters to only 0.15 times the size of the original YOLOv4 network. While this reduction in accuracy is within acceptable limits, our main focus lies in achieving optimal performance on the FPGA platform. This strategic decision holds significant importance for our specific application requirements in terms of computational efficiency and resource utilization.  
The comparison of the partial image detection effect of the pruned YOLOv4-MobileNetv3 algorithm framework proposed in this paper and the mainstream target detection algorithm on the dataset DIOR is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Faster-RCNN** & **SSD** & **Centernet** & **YOLOv4** & **YOLOv3** & **Ours** \\ \hline Backbone & VGG16 & SSD & Resnet50 & CSPDarkNet53 & Darknet53 & MobileNetv3 \\ airplane & 0.78 & 0.91 & 0.91 & 0.95 & 0.87 & 0.94 \\ airport & 0.87 & 0.88 & 0.92 & 0.93 & 0.86 & 0.95 \\ baseball114 & 0.89 & 0.94 & 0.95 & 0.97 & 0.94 & 0.96 \\ basketball114 & 0.83 & 0.85 & 0.83 & 0.86 & 0.87 & 0.83 \\ bridge & 0.40 & 0.45 & 0.48 & 0.57 & 0.50 & 0.50  
Figure 11: Comparison of mainstream model detection effect. (**a**) Faster-RCNN; (**b**) SSD; (**c**) Centernet; (**d**) YOLOv3; (**e**) YOLOv4; (**f**) ours.
# Screening Mechanism and Properties of a Cantilevered Vibrating Sieve for Particles Processing
## 2 Construction of a CVS  
Traditional vibrating screens mostly contain an enclosed screening hole structure, such as the rectangular screen hole and circular screen hole [2; 18]. In contrast, the proposed CVS is composed of multiple cantilevered sieve rods, which are inserted into the mounting base in parallel to achieve open screen holes, as shown in Figure 1. The length and diameter of the cantilevered sieve rod are L and q, respectively. The spacing between two adjacent rods is s.  
Here we consider the property of particles going through the two kinds of screen holes. As illustrated in Figure 2, for a traditional rectangular screen hole with size of \(\rm L\times H\), if a spherical particle with d (d \(<\rm H<L\)) in diameter falls into it, it freely passes through the screen hole to be an undersized product. On the other hand, a spherical particle that fails to pass through this screen hole may successfully pass through the next one, or eventually become an oversized product. Thus, for a spherical particle to successfully pass through the screen hole, the center of the falling particle must be within the dashed box in the middle, in which case the area of the dashed box is \(\rm(L-d)\times(H-d)\). For the CVS, screen holes are similar to open rectangular holes, which are not subject to the boundary constraint on the right. Thus, the particle falling area that allows smooth screening is also the dashed box in the middle, with an area of \(\rm(L-0.5d)\times(H-d)\).  
Figure 1: Mechanical structure of a cantilevered vibrating sieve (CVS).  
It can be conceived that the number of times of particles penetrating through screen holes is directly proportional to the area. Also, the number of times of particles being fed to screen holes is directly proportional to screen hole area L \(\times\) H. For this reason, the probability of particles penetrating through screen holes can be determined by the ratio of the two areas:  
For traditional rectangular screen holes, the screen-penetrating probability is:  
\[p_{1}=\frac{(L-d)(H-d)}{LH}, \tag{1}\]  
while for the CVS, the screen-penetrating probability is:  
\[p_{2}=\frac{(L-0.5d)(H-d)}{LH}, \tag{2}\]  
In view of the following relationship:  
\[\frac{p_{2}}{p_{1}}=\frac{L-0.5d}{L-d}>1, \tag{3}\]  
thus, the CVS has a higher screen-penetrating probability relative to traditional sieves, and this fundamentally determines that the CVS can also achieve a high screening efficiency.
## 3 Theoretical Framework
### Artificial Intelligence and Technology Acceptance Model
#### 3.2.2 Technology Acceptance Model (TAM)  
The Technology Acceptance Model (TAM) provides a theoretical foundation for understanding how users accept and use a technology. Developed by [PERSON] in 1989, TAM has been widely used to predict and explain user behavior regarding technology adoption. The model asserts that two main factors influence technology acceptance: perceived usefulness and perceived ease of use. In language learning, especially within systems like the CILS, TAM becomes essential for designing interfaces and functionalities that users find beneficial and user-friendly. For a comprehensive understanding of TAM and its application in various fields, including language learning technologies, readers are referred to the foundational work by [PERSON], which outlines the model's parameters and its empirical validation: [PERSON] (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.  
Perceived usefulness in the context of the CILS refers to the extent to which a user believes the system will help improve their language skills effectively and efficiently. For the CILS, this involves demonstrating how AI-driven adaptive learning can personalize language learning experiences in ways that traditional classroom settings cannot, thereby enhancing the learning process [24]. On the other hand, perceived ease of use concerns the degree to which a user expects the system to be free of effort. This includes intuitive design, straightforward navigation, and the minimization of complexity in user interactions, ensuring that learners can focus more on learning the language rather than navigating the system.  
The application of TAM in designing CILS involves iterative testing and feedback loops with actual users to refine the system's features and interface. By integrating TAM principles, developers can create a more engaging and effective learning environment that encourages continual use and deepens learning outcomes. Additionally, understanding TAM helps address potential barriers to technology adoption, such as resistance to replacing traditional learning methods with AI-based solutions, by demonstrating clear advantages and ease of integration into existing learning habits.
## 4 Discussion
### Characterization of Root Canal sealers through EDX  
The results of the EDX microanalysis from our study showed that all the tested sealer materials contained oxygen, calcium, and carbon. This finding is consistent with findingsfrom earlier studies [6; 16; 32; 33; 34; 35], which reported that calcium plays an active role in the periapical repair process. Calcium ions are responsible for the formation of calcite crystals, which play a direct role in the construction of a mineralized barrier [32]. According to [PERSON] et al., 1991 [34], fibronectin has an affinity for calcite crystals, which in turn promotes cell adhesion and differentiation, which ultimately leads to the accumulation of hard tissue. Therefore, materials with high calcium content may result in better filling. On the other hand, the capacity of the sealer to induce periapical repair should be expected to be lower if it contains significantly lower levels of calcium. To induce periapical repair and the formation of a mineralized barrier in the root apex, calcium hydroxide is often used prior to root canal filling. Its higher calcium and hydroxyl ion content and mechanical action as a matrix may protect against overfilling and justify its use [6]. When in contact with simulated body fluids such as [PERSON]'s balanced salt solution, calcium silicate cement has been shown in previous research to be capable of producing a surface layer of apatite [36; 37]. This apatite layer might be able to help improve the biological activity at the bone-periapical level by fostering the formation of a barrier and inducing the activation and differentiation of the apical cells [38]. In addition to this, the apatite deposits might fill the porosity and any gaps that are still present, which would result in an additional layer of external sealing [39]. According to the results of our study, none of the sealer materials tested contained bismuth or other heavy metals such as lead, chromium, cobalt, copper, zinc, or manganese. This finding is in line with a study by [PERSON] et al., 2022 [16], which found that none of the sealers tested contained any bismuth or other heavy metals such as lead, chromium, cobalt, copper, zinc, or manganese. Furthermore, OneFill, iRoot, and CeraSeal contained higher contents of zirconium and oxygen. In order to reduce the amount of harmful heavy metals present in calcium silicate-based products, a new radiopacifier called zirconium oxide has been developed and implemented. Zirconium oxide's adequate radiopacity and lack of interference with the hydration of calcium silicate-based materials has made it a popular choice [31]. When compared to bismuth oxide, zirconium oxide is more biocompatible and does not result in tooth discoloration [29]. Controlled amounts of zirconium oxide are reported to increase radiopacity without affecting the setting of tricalcium silicate cements [40].