esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spatio-Temporal Characteristics Analysis
#### 2.3.2 SDE Model  
The SDE model delineates the spatial form through major and minor axes and the centroid distribution of the ellipse. The centroid coordinates, standard deviation, rotation angle for the x-axis and y-axis, and barycentric coordinates are calculated as follows:  
\[X=\frac{\sum\limits_{i=1}^{n}w_{i}x_{i}}{\sum\limits_{i=1}^{n}w_{i}},\,Y= \frac{\sum\limits_{i=1}^{n}w_{i}y_{i}}{\sum\limits_{i=1}^{n}w_{i}}, \tag{15}\]\[\sigma_{x}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(w_{i}x_{i}^{*}\cos\theta-w_{i}y_ {i}^{*}\sin\theta\right)^{2}}{\sum\limits_{i=1}^{n}w_{i}^{2}}},\sigma_{y}= \sqrt{\frac{\sum\limits_{i=1}^{n}\left(w_{i}x_{i}^{*}\sin\theta-w_{i}y_{i}^{*} \cos\theta\right)^{2}}{\sum\limits_{i=1}^{n}w_{i}^{2}}}, \tag{16}\]  
\[\tan\theta=\frac{A-B+\sqrt{\left(A-B\right)^{2}-4F}}{2\sum\limits_{i=1}^{n}w_ {i}^{2}x_{i}^{*}y_{i}^{*}}, \tag{17}\]  
\[\overline{x}=\frac{\sum\limits_{i=1}^{n}x_{i}\times w_{i}}{\sum\limits_{i=1}^ {n}w_{i}},\overline{y}=\frac{\sum\limits_{i=1}^{n}y_{i}\times w_{i}}{\sum \limits_{i=1}^{n}w_{i}},i=1,2,\cdots,n, \tag{18}\]  
where \(\left(X,\,Y\right)\) represents the weighted average center of the barycentric coordinates \(A=\sum\limits_{i=1}^{n}w_{i}x_{i}^{*2}\), \(B=\sum\limits_{i=1}^{n}w_{i}y_{i}^{*2}\), \(F=\sum\limits_{i=1}^{n}w_{i}^{2}x_{i}^{*2}y_{i}^{*2};\)\(\left(x_{i},\,y_{i}\right)\) denotes the barycentric coordinates of cell \(i\); \(\overline{x}\), \(\overline{y}\) are the barycentric coordinates of the study object.  
We analyzed the direction and distance of the centroid's movement to determine the changing trend of the study object. The center of gravity travel distance and movement direction were calculated using the following formulas:  
\[D=G\times\sqrt{\left(\overline{x}_{t+1}-\overline{x}_{t}\right)^{2}+\left( \overline{y}_{t+1}-\overline{y}_{t}\right)^{2}}, \tag{19}\]  
\[\theta=\theta_{t+1}-\theta_{t}=[0.5k\times\pi+\arctan E]\times\frac{180^{ \circ}}{\pi}, \tag{20}\]  
where \(G\) is a constant, and \(D\) denotes the distance traveled by the object in period \(t\) relative to period \(t+1\). \(E=\frac{\overline{y}_{t+1}-\overline{y}_{t}}{\overline{x}_{t+1}-\overline{x} _{t}}\), and \(\theta\) denotes the angle of movement of the object examined in period \(t+1\) relative to period \(t\).
## 3. Designed Scheme
### Image Protection  
The image protection stage is described in this section. This process follows two steps: watermarking generation and insertion in the carrier image. In order to explain these steps, let us denote the original image as \(Ih\) of size \(A\times B\) in the color space RGB.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Genetic Approaches in Identification of mTOR Signaling Components
#### 4.2.6 The v-ATPase Stood Out in siRNA Screening as a Lysosomal Protein Regulating mTOR1  
Given that Rag GTPases-mediated mTOR1 activation in responding to amino acids largely occurs at lysosome membrane, [PERSON] et al. hypothesized that lysosomal proteins might participate in regulating mTOR1 activation. Using dsRNAs to deplete a panel of genes with characterized roles in controlling lysosome biogenesis and function in Drosophila S2 cells, [PERSON] et al. found that depletion of only a small set of genes including vhaAC39, vha16, vha100-1, and vha100-2, all of which are components of the vacuolar H\({}^{+}\)-ATPase (v-ATPase), suppressed mTOR1-mediated dS6K phosphorylation [58]. These observations were further confirmed in mammalian cells and v-ATPase was found to be needed for Rag GTPases-induced mTOR1 activation on lysosome [58].  
2.7 v-ATPase and Arf GTPase Were Identified to Activate mTOR1 in Response to Glutamine by a Genetic Approach  
[PERSON] et al. generated RagA/B KO (knockout) MEFs and unexpectedly found that loss of _RagA/B_ reduced (~30%), rather than abolished, mTOR1 activity in responding to amino acid stimulation [70]. Through stimulating RagA/B WT and KO MEFs with each of individual 20 standard amino acids, [PERSON] et al. observed that RagA/B was required for leucine- or arginine-triggered mTOR1 activation, whereas glutamine stimulation minimally relied on RagA/B. Genetic and biochemical experiments identified Arf-GTPase and v-ATPase as hits that are required for glutamine-induced mTOR1 translocation to lysosome, a process independent of Rag GTPases [70].
# Low Complexity List Decoding for Polar Codes with Multiple CRC Codes
###### Abstract  
Polar codes are the first family of error correcting codes that provably achieve the capacity of symmetric binary-input discrete memoryless channels with low complexity. Since the development of polar codes, there have been many studies to improve their finite-length performance. As a result, polar codes are now adopted as a channel code for the control channel of 5G new radio of the 3 rd generation partnership project. However, the decoder implementation is one of the big practical problems and low complexity decoding has been studied. This paper addresses a low complexity successive cancellation list decoding for polar codes utilizing multiple cyclic redundancy check (CRC) codes. While some research uses multiple CRC codes to reduce memory and time complexity, we consider the operational complexity of decoding, and reduce it by optimizing CRC positions in combination with a modified decoding operation. Resultingly, the proposed scheme obtains not only complexity reduction from early stopping of decoding, but also additional reduction from the reduced number of decoding paths.  
polar codes; successive cancellation list decoding; multiple CRC codes +
Footnote †: journal:
## 3 Metaverse Classroom: A Layered Architectural View
### Management and Support Layer
#### 3.7.2 Content Management  
Content management in a Metaverse classroom ensures that lesson materials, curriculum updates, and resources remain organized, accessible, and up to date [211]. A robust content management system (CMS) provides a centralized hub where educators can create, store, and modify instructional materials such as videos, simulations, readings, and interactive elements [11]. This organization streamlines lesson planning and ensures consistent content delivery, making each lesson engaging and well-prepared with relevant resources [136]. Updating curriculum materials is essential in a Metaverse classroom to keep lessons current with advancements and evolving student needs. With an adaptable CMS, teachers can easily incorporate new findings without overhauling the course. For example, a science teacher might update a virtual lab with the latest research, or a history teacher could modify a timeline to reflect recent discoveries [13]. This flexibility ensures that students always learn from the most relevant information, fostering a culture of continuous learning [17].  
Organizing and categorizing content within the CMS improves accessibility for both students and teachers [7]. By tagging materials according to subject, difficulty level, or learning objectives, users can quickly find relevant resources, reducing downtime and streamlining lesson transitions. For instance, a teacher could tag materials under \"Physics-Energy\" or \"Advanced Algebra\", making it easier for students to access content that aligns with their studies [194]. This system supports self-paced learning by allowing students to independently retrieve resources, promoting ownership of their educational journey within the Metaverse [7]. Customizing lesson materials to suit different learning styles becomes more efficient with a flexible CMS [56]. Teachers can store multiple versions of content, such as simplified explanations, visual aids, or interactive exercises, and match these to individual student needs [17]. For example, a math teacher might create practice problems with varying difficulty levels or include hands-on activities for students who benefit from experiential learning. This adaptability allows educators to personalize learning experiences and address the diverse needs of Metaverse learners [212]. Finally, data security and access controls are critical to maintaining a safe digital learning environment within the CMS [213]. Teachers can set permissions to manage who can view, modify, or share content, protecting student information and intellectual property [214]. For instance, some materials might be restricted to teacher access only, while others are shared with students for collaborative learning [17]. These security protocols ensure that content is managed responsibly, fostering a safe and organized virtual classroom [135].
## 2 Related Work  
In this section, we present the recent docking and recharging methods, based on Lidar and computer vision techniques, for mobile robot systems in the manufacturing field. [PERSON] et al. [5] proposed a vision-based docking and recharging method that can be applied in a warehouse environment. This method used AprilTag for the detection and identification of the robot's pose. It achieved a docking success rate of approximately 97.33%. In [17], the authors proposed a Faster RCNN model to detect and localize the designed markers mounted on a docking station, combining it with the solvePnP algorithm to allow the mobile robot to navigate in a ROS simulation environment. This model achieved an accuracy of 96.3% based on thirteen testing images. The detector took around 35 ms to process each image. [PERSON] et al. [21] adopted a single-shot detector (SSD) to identify moving people and then dock to the target person for human-robot collaborative tasks in an unstructured environment. In [23], an SSD was developed to detect the charging stations in obstacle-free scenarios. This method could achieve a performance of 99.8% for successful docking to the charger. It took an average of 12 s to complete the docking procedures based on the designed scenarios.  
Although these methods have made great contributions to autonomous docking and recharging applications, some limitations are observed. Most methods are evaluated in a simulation or laboratory environment instead of a manufacturing environment. In addition, two-stage deep learning models, such as Faster RCNN, are inefficient compared to one-stage real-time models. Considering these limitations, a state-of-the-art real-time deep-learning-based model, YOLOv7, is developed to distinguish and identify the target wireless charger from a complex manufacturing environment; it is integrated with the proposed Lidar-based approach to achieve efficient, low-cost, and robust docking and recharging.
## 2 Model Presentation
### Delay Model

Table 3: Distribution process parameters with nominal values. We define the sensitivity of the current to the process parameter \(P_{i}\) as the partial derivative of the current with respect to the process parameter \(P_{i}\). According to Equation (19) the current sensitivity is given by  
\[\frac{\partial I_{DS}}{\partial P_{i}}=\frac{1}{1+g_{m}Rsd}.\frac{\partial f_{ IDS}}{\partial P_{i}} \tag{20}\]  
where the change in series resistance \(Rsd\) caused by \(P_{i}\) variation is ignored and \(g_{m}=\frac{\partial f_{ IDS}}{\partial V_{DI}}\), \(\frac{\partial f_{ IDS}}{\partial P_{i}}\) is computed using the proposed current model. Based on (20), the sensitivity and higher-order derivatives of delay and the effective current with respect to \(P_{i}\) can be calculated according to (17) and \(I_{eff}=(I_{H}+I_{L})/2\). A process-variation-aware model for predicting delays with \(P_{i}\) variations is defined as follows:  
\[T_{d}(P_{i})=T_{d}\Big{(}P_{i}^{(0)}\Big{)}+\sum_{m=1}^{n}\left[\frac{\partial ^{m}T_{d}\Big{(}P_{i}^{(0)}\Big{)}}{\partial P_{i}{}^{m}}\frac{\Big{(}P_{i}-P_ {i}^{(0)}\Big{)}^{m}}{m!}\right] \tag{21}\]  
where \(P_{i}^{(0)}\) is the nominal value for \(P_{i}\) and \(n\) is the order of the prediction model. HSPICE transient simulation is performed once to obtain \(T_{d}\Big{(}P_{i}^{(0)}\Big{)}\). The remaining coefficients are calculated using the proposed current model and (20). It can be seen that the \(T_{d}(P_{i})\) for n = 1 is linear with respect to the process parameter while the terms for \(n\geq 2\) can compensate for the nonlinear relation, especially for circuits operating in the subthreshold region. It is worth noting that the same approach can be used to predict the effective current, the results of which are substituted into (17) for delay prediction. Figure 2 shows the complete flow of delay prediction. The specific steps are: (1) Set the number of samples to \(N_{sample}\) in the netlist of the cell to generate \(N_{sample}\) randomly sampled seeds of the process parameter \(P_{i}\). (2) A single-point HSPICE transient simulation is performed once with the nominal value of \(P_{i}\) to obtain \(T_{d}\Big{(}P_{i}^{(0)}\Big{)}\) and \(S_{out}\Big{(}P_{i}^{(0)}\Big{)}\). \(I_{eff}\Big{(}P_{i}^{(0)}\Big{)}\) is obtained through a single-point HSPICE DC simulation with \(P_{i}^{(0)}\). (3) Compute nth derivatives \(\partial^{n}T_{d}\Big{(}P_{i}^{(0)}\Big{)}/\partial P_{i}^{n}\), \(\partial^{n}S_{out}\Big{(}P_{i}^{(0)}\Big{)}/\partial P_{i}^{n}\) and \(\partial^{n}I_{eff}\Big{(}P_{i}^{(0)}\Big{)}/\partial P_{i}^{n}\) as the coefficients of the delay prediction model. (4) The predicted values of delay \(T_{d}\Big{(}P_{i}^{(j)}\Big{)}\) and \(S_{out}\Big{(}P_{i}^{(j)}\Big{)}\) are calculated with the \(j\)th random sampling value \(P_{i}^{(j)}\). This step is repeated until \(N_{sample}\) calculations are completed. Figure 2: Delay prediction flowchart.
# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 2 Materials and Methods
### Ai-Qca
#### 2.5.2 Branch Separation  
In the processing of segmentation and QCA, the concerned vessels were always the coronary arteries, including LAD, LCX, RCA, and their primary branches. The resulting CAT data often includes multiple branches. However, when analyzing the stenosis region in the process of QCA, it is important to focus on a single vessel pathway. Therefore, a branch separation algorithm is necessary. A simple method is presented to distinguish left and right branches below the bifurcation point. For three points on the CAT, assumed as P\({}_{1}\) = (\(x_{1}\), \(y_{1}\)), P\({}_{2}\) = (\(x_{2}\), \(y_{2}\)), and P\({}_{3}\) = (\(x_{3}\), \(y_{3}\)), P\({}_{1}\) is a point before the bifurcation point, P\({}_{2}\) is the bifurcation point, and P\({}_{3}\) is a point after the bifurcation point. The amount of area of three points in the plane is calculated as follows:  
\[\text{S}=(x_{1}-x_{3})\;\times\;(y_{2}-y_{3})\;-\;(y_{1}-y_{3})\;\times\;(x_{2} -x_{3}) \tag{5}\]  
P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\) are clockwise when S is positive, counterclockwise when S is negative, and collinear when S is 0. According to the results of direction judgment, the branches below the bifurcation point can be distinguished as left or right. By combining the diameter value trends and segmentation results, we can identify the branches that are closest to the extension direction and have diameter values of the main vessels before bifurcation. This allows us to separate the main branches and their primary branches.
## 3 Results and Discussion
#### 3.1.2 Laser Fluence  
Laser fluences below 1.30 J/cm\({}^{2}\) did not produce a significant number of nanoparticles judging by the little to no colloid colour changes and limited/completely absent UV-Vis absorbance peaks. Three levels of laser fluence were studied: 1.83, 1.88 and 1.91 J/cm\({}^{2}\).  
Effect of Laser Fluence on Colloidal Density (mg/mL) and Nanoparticle Yield (UV-Vis Absorbance)  
Increase in fluence increases the nanoparticle yield (UV-Vis absorbance) as shown in Figure 1 (refer to Table 1 for DOE). There is no obvious trend between laser fluence and colloidal density. The changes in laser fluence seems to not have a significant influence on the colloidal density when one of ablation time or scan speed are low while the other is  
Figure 5: Effect of ablation time on nanoparticle mean diameter (nm).  
high. Additionally, there is no significant influence of fluence on the density at an ablation time of 5 min with scan speeds of either 3000 or 3250 mm/s. Conversely, there is a clear direct proportionality between laser fluence and colloidal density at an ablation time of 25 min and a scan speed of 3250 mm/s. This signifies that the colloidal density is controlled by a combination of factors (ablation time, scan speed, fluence).  
Effect of Laser Fluence on Nanoparticle Mean Diameter  
The nanoparticle mean diameter is of major importance as the end-use of the produced nanocolloids depends on the mean size of the nanoparticles. For instance, a certain nanoparticle mean size (and distribution) is required for use in an inkjet printer for the production of flexible sensors [30]. To avoid printing problems, different inkjet printers will work best with different nanoparticle mean sizes. At all combinations of scan speed and ablation time studied herein, the laser fluence has some influence on the resulting nanoparticle mean diameter, as shown in Figure 6. There is a general linear decrease in nanoparticle size with increasing fluence at long ablation times (25 min, far right column). A similar general trend is also observed at low ablation times (2 min) and moderate scan speeds (3250 mm/s, middle column). At 5 min of ablation time (middle column), a general pattern exists whereby there is a decrease in nanoparticle mean diameter as the fluence increases from 1.83 to 1.88 J/cm\({}^{2}\), followed by an increase in diameter with increasing fluence from 1.88 to 1.91 J/cm\({}^{2}\), making a crooked 'v' shape. Although the laser fluence does not have one obvious trend (linear trend) in influence over any of UV-Vis absorption, colloidal density and mean diameter, a threshold fluence is required for ablation to commence.
## 5 Conclusions  
Aiming at the task of 3D reconstruction of unstructured scenes with differing intensity texture information, this paper proposed a 3D scene reconstruction method that can meet the requirements for high precision and good real-time performance. A new stereo-matching system was first used on the preprocessed 2D images; that is, the SAD-FAST feature detection with an improved self-adaptive threshold value was used to find the key points, and then the SURF descriptor and FLANN matcher were used with the Mahalanobis distance to reduce the mismatching to obtain the disparity maps. Next, the GVDS algorithm was used to adjust the distribution area of the feature points to retain the 3D information for the flat area with weak depth variation, which made the reconstruction effect more realistic and the reconstruction process more efficient. Experiments proved that our method had high accuracy and good real-time performance unmatched by traditional algorithms when facing a large range of unstructured scenes for reconstruction, and the proposed algorithm had strong robustness and wide applicability, making it capable of performing most 3D scene reconstruction tasks.  
**Author Contributions**: [PERSON] performed experiments in the stereo-matching steps, obtained the results for the corresponding experimental steps, and analyzed them. [PERSON] conceived the feature extraction for the depth value calculation and performed comparative experiments with the corresponding algorithms. [PERSON] set the research direction and wrote some of the content. [PERSON] wrote some sections and made final corrections. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**3D Reconstruction Steps**} & \multicolumn{4}{c}{**Traditional Algorithms/ms**} \\ \cline{2-5}  & [62] & [63] & [64] & Our Algorithm/ms \\ \hline Camera and image pretreatments & 47.361 + 51.12 & 47.361 + 51.12 & 47.361 + 51.12 & 47.361 + 51.12 \\ Stereo-matching experiment & 137 & 263.2 & 243 & 64.301 \\ Triangulation and texture mapping & 32.17 & 35.784 & 37.263 & 35.134 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Real-time testing of different deep learning algorithms.  
**Funding:** This research was funded by the Natural Science Foundation of Sichuan, grant numbers 2023 NSFSC1987 and 2022 ZHCG0035: the Key Laboratory of Internet Information Retrieval of Hainan Province Research Found, grant number 2022 KY03: the Opening Project of International Joint Research Center for Robotics and Intelligence System of Sichuan Province, grant number JQZN2022-005: and Sichuan University of Science and Engineering Postgraduate Innovation Fund Project, grant number D10501644.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 4 Applications and Target Industry
### Advertising  
Advertising in an FRS identifies and targets potential customers by delivering customized messages and offers. This is usually accomplished through personalization and contextualization, leveraging user data and preferences, geography, and other demographic characteristics [170; 171; 172]. In FRSs, the main goal of advertising is to increase the visibility of products and services and to increase the likelihood of conversion [173]. Recent research has highlighted the potential of FRSs to facilitate better advertisement targeting. In [174], it was found that a recommendation system using a machine learning approach can improve the accuracy of ads by up to 20%. This improvement was attributed to the ability of recommendations to model user preferences and characteristics more accurately, allowing for better targeting of ads. Moreover, FRSs can help reduce the cost associated with advertising campaigns, as well as the amount of effort needed to build and maintain the campaigns. On the other hand, privacy preservation is an essential component of any recommendation system. This is due to the fact that recommendation relies on the collection and analysis of user data [175]. Without proper privacy measures, user data can be easily exposed to malicious actors, leading to privacy violations and potential data misuse [176]. Therefore, FRSs must employ privacy measures such as data anonymization, data encryption, and access control in order to ensure the privacy of users.