esa-sceva/satcom-chunk-collection huggingface dataset:
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 6 Conclusions  
SOT in SVs has great potential for remote-sensing ground surveillance. To address object rotation and tracking drift problems, we analyzed the task from a new perspective, where the hybridization of angle and motion information cooperates for SOT in SVs. In addition, an RAMC tracker consisting of rotation and translation branches was created. By decomposing the rotation issue into translation solution, it decouples the rotation and translation motion patterns, achieving adaptive angle estimation. Subsequently, we dug out potential motion information and synergized it with the appearance information to prevent tracking drift. Moreover, an IS strategy was proposed to optimize the evaluation of trackers. Quantitative and qualitative experiments were conducted on space-born SV datasets. The  
Figure 12: Visual comparison of original patches (**a**) and rotated patches (**b**).  
results demonstrate that the proposed method yields state-of-the-art performance and runs at real-time speed. Future work will focus on solving the angle jitter problem.  
Conceptualization, Y.C. and Y.T.; methodology, Y.C., Y.T. and T.H.; resources, T.H. and Y.Z.; writing--original draft preparation, Y.C. and Y.T.; and supervision, Y.T., B.Z. and H.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (No. 41971313).  
We are grateful to anonymous reviewers and Academic Editors for providing valuable comments. We thank Chang Guang Satellite Technology Co., Ltd. (Changchun, China) and the International Space Station for providing the satellite videos.  
The authors declare no conflict of interest.
# A Dynamic Decision Support System for Selection of Cloud Storage Provider
## 5 Results and Discussion
### Performance Ranking  
The CPU core is the major contributor in the ranking of cloud vendor's performance. In this process, the maximum CPU core values of all listed cloud vendors are picked up and calculations are made with the corresponding weights from Table 25. Besides the CPU, all maximum values of parameters are selected for performance ranking as follows:  
If (Param[X] == Param[W]  
Param[X] = Param[W]/100  
e.g., Amazon offers a maximum 24-core CPU, and its weight is 18.04% in Table 24. In order to achieve accuracy, remove the percentage and convert it to 0.1804 which is placed in front of the Amazon row and in the CPU column of Table 25. This table shows a priority list of cloud vendors ranked on the basis of their weights, and combined sums of their weights are shown as rankings in the right-most column. The simple formula of ranking is applied row-wise as follows:  
Ranking = Sum (C21:C31:C41...... C81)  
All the values in the ranking columns are less than 1, called a consistency index (CI), so our final values are validated. The vendor that has the largest value is the best vendor with respect to performance. Figure 19 shows a clear picture of the performance ranking of cloud providers, where Google and IBM have almost the same rank because they provide maximum computing resources. Cloud storage providers are shown on the X-axis and their rankings, calculated by a structured technique, on the Y-axis. Cloud service providers like SafeCopy and iCloudDrive have low rankings due to lower values.  
It is clearly shown from the results that Google Drive and IBM Cloud are leading other cloud storage vendors due to their features. The IBM cloud provides dynamic scalability, monitoring tools and technology integration while Google Drive attracts users with free storage, offline features and attractive user interface. On the other hand, iCloud is at the bottom due to access from Apple devices only and limited features.
## 3 Problem Statement and Input Data Representation Methods
### Common Forms of Input Representations
#### 3.2.4 Sliding Window  
The size of the sliding window is often a critical parameter that needs to be adjusted in forecasting research. Using RNNs for the prediction process allows for recognizing and learning data patterns within time series data. However, the presence of fluctuations in the data can make it challenging to understand these data patterns. The dataset utilized in the study by [PERSON] et al. [64] records the visitor volume at each time point over ten years, showcasing the changes in visitor visitation to attractions over time, characterized by linear trends and periodic fluctuations. The study employs 120 data vectors for prediction processing, representing tourism visitation data for 120 months. Based on these 120 data vectors, the study conducts multiple experiments and determines a window size setting of 3, meaning each window contains data from three time steps. In the study, the window moves forward one time step at a time, and ultimately, the model predicts the visitor volume at the next time point based on the visitor count data from the past three time points. Like other deep learning models, GRUs require careful adjustment of the sliding window size to optimize predictive performance. To explore this tedious process, in the study conducted by [PERSON] et al. [65], they continuously adjust the input window size of the GRU model to enhance its predictive capabilities. The original data are derived from average traffic flow data collected at 5 min intervals. Through experimentation, the researchers tested various window sizes (3, 6, 12, 18, 24) to determine which size most effectively improves the performance of the GRU model. In a GRU model configured with 256 neurons in the first layer and two hidden layers containing 64 and 32 neurons, respectively, they find that a window size of 12 yielded the best predictive performance. [PERSON] et al. [66] combined the ARIMA model and LSTM for traffic flow prediction. In the ARIMA model, regression with a sliding time window is introduced, allowing the model to fit new input data for more accurate predictions continuously. This combined approach merges the projections of the two models through dynamic weighting, where the weights of each model in the final prediction are adjusted based on the standard deviation between the predicted results and the actual traffic flow at different time windows.
## 2 Descriptions of Multiobjective DWTA Problems  
### Related Concepts of MOP  
The mathematical model of multiobjective optimization problem (MOP) can be expressed as follows:  
\[\left\{\begin{array}{c}\text{minimize }\mathbf{F}(\mathbf{x})=(f_{1}(\mathbf{x}),f_{2}(\mathbf{x}), \cdots,f_{m}(\mathbf{x}))^{T}\\ \text{subject to }\mathbf{x}\in\mathbf{\Omega}\end{array}\right. \tag{1}\]  
where \(\mathbf{\Omega}\) represents the decision space, \(\mathbf{x}\) is the decision vector, \(\mathbf{x}=[x_{1},x_{2},\cdots,x_{n}]\), \(n\) is the total number of decision variables. \(f_{m}(\mathbf{x})\) denotes the \(m\)th objective function. \(\mathbf{F}(\mathbf{x}):\mathbf{\Omega}\to\mathbf{R}^{m}\) is composed by \(m\) objective functions.  
Given two vectors \(\mathbf{u}=(u_{1},u_{2},\cdots u_{m})\) and \(\mathbf{v}=(v_{1},v_{2},\cdots v_{m})\in\mathbf{\Omega}\), we say that \(\mathbf{u}\) dominate \(\mathbf{v}\) (denoted by \(\mathbf{u}\prec\mathbf{v}\)), if and only if: \(u_{i}\leq v_{i}\) for all \(i\in\{1,2,\cdots m\}\) and \(u_{j}\leq v_{j}\) for at least one component. This characteristic is called as Pareto Dominance.  
Also, only if there is no vector \(\mathbf{x}\in\mathbf{\Omega}\), which makes \(\mathbf{f}(\mathbf{x})\prec\mathbf{f}(\mathbf{x}^{*})\), then the decision vector \(\mathbf{x}^{*}\mathbf{\epsilon}\)\(\mathbf{\Omega}\) can be called Pareto optimal solution or non-dominated solution.  
The Pareto optimal solution set within \(\mathbf{\Omega}\) is called Pareto optimal set, which is denoted as \(\mathbf{P}^{*}\). Furthermore, the Pareto front can be defined as \(\mathbf{P}^{*}=\{\mathbf{f}(\mathbf{x}):\mathbf{x}\in\mathbf{P}^{*}\}\).
## 4 Simulation Assessment of the ARounD Communication Scheme
### Simulation Environment
#### 4.1.2 Performance Metrics  
The aim of this simulation assessment is to evaluate the behaviour of the ARounD communication scheme when compared to the use of cluster-tree routing and its impact over the background communication traffic. For the sake of convenience, we adopted the name _Standard Approach_ for the cluster-tree routing and _ARounD Approach_ for the ARounD communication routing. The following performance metrics were used for the assessment of the network behaviour:  
* _End-to-end Delay_: time interval between the data frame generation at the application layer of the source node and its reception at the application layer of the destination node.
* _Packet Loss Rate_: the percentage of packets lost during the communication, considering the number of data packets successfully received at the destination node and the number of packets generated by the source node.
## 3 Application
### Results and Discussion

Table 3: Summary of evaluator losses (same examples as in Figure 15). Table 4 shows all kinds of indicators that represent the conformity of PEN-generated geometries with conventionally generated geometries, averaged over all 100 examples. SD is the standard deviation. From the data in the Table 4, it can be seen that in the examined cases, 79.9% of the elements of the geometries obtained with the PEN method have density differences of less than 1% as compared to the conventionally optimized geometries.
## 4 Materials and Methods
### Quantification of SCFA  
Supernatants of mice lung homogenates were analyzed by gas chromatography-mass spectrometry (GC-MS) using a 6890 GC and a 5973 A MSD (Agilent Technologies, Milan, Italy) with electron ionization (EI) source. A J&W carbowax GC column (Agilent Technologies) was used, while head space-Solid Phase MicroExtraction (HS-SPME), using a PDMS/CAR/DVB 2 cm fiber, was employed to extract and transfer the analytes in the GC injection port. Deuterium labeled internal standards were used for quantitation.
## 4 Discussion
### Gender as a Correlate for PA During Weekdays and Weekends  
This systematic review identified two distinct patterns concerning the association between gender and PA across weekdays and weekends. Several studies have identified gender as a significant correlate influencing PA behaviors in ECEC settings, during both weekdays and weekends [33; 41; 45; 49; 51; 60]. Overall, boys participated in higher levelsof MVPA and total PA, and exhibited less sedentary behavior than girls across weekdays, weekends, or both. Specifically, boys engaged in more MVPA and light-to-moderate PA during weekdays, as well as increased MVPA on weekends, compared to girls [33; 49]. Additionally, several studies have reported associations involving parental and child PA differentiated by gender [55], links between public activity facilities, gender, and children's PA [53], as well as gender-related variations in motor competence and daily activity patterns [35].  
By contrast, other research has not found any relationship between gender and PA [20; 42; 43; 48]. These studies point out that other personal factors, rather than gender, may explain PA patterns.
# H2O: Secure Interactions in IoT via Behavioral Fingerprinting
## 4 Behavioral Fingerprint for Reliability
### Object-to-Object Interaction
To better understand the steps above, let us consider the example represented in Figure 4. This figure reports a portion of an H2O network containing the nodes \(n_{j}\) and \(n_{z}\) along with their neighbors. Black edges represent interactions assured by the presence of a fingerprint model. Hence, the two nodes linked by such edges have first met during the _safe starting phase_. Moreover, consider an example configuration in which \(\epsilon=0.3\) and \(r=2\). Suppose again that the node \(n_{j}\) wants to interact with \(n_{z}\), but it has not met \(n_{z}\) during the _safe starting phase_. The dashed edge in Figure 4 represents the possible future interaction between \(n_{j}\) and \(n_{z}\). Before starting the communication, \(n_{j}\) checks whether \(n_{z}\in R(n_{j})\). If \(n_{j}\) had a direct access to a fingerprint model for \(n_{z}\), it could check if \(fr(n_{j},n_{z})\geq 1-\epsilon\). However, in this example, \(n_{j}\) has no direct access to a behavioral model for \(n_{z}\). Therefore, \(n_{j}\) contacts the objects in its neighborhood (\(\Gamma_{n_{j}}=\{n_{a},n_{i},n_{y},n_{w}\}\) in the example reported in Figure 4) to obtain information about \(n_{z}\). In particular, \(n_{j}\) looks for objects in its neighborhood, who can leverage a behavioral model of \(n_{z}\) to assess its reliability, which, as a consequence, belong also to the neighborhood of \(n_{z}\). In the example, two nodes, namely \(n_{i}\) and \(n_{w}\), belong to the interception set \(\Gamma(n_{j})\cap\Gamma(n_{z})=\{n_{i},n_{y},n_{w}\}\) and have access to a behavioral fingerprint of \(n_{z}\) (\(\Delta(n_{j},n_{z})=\{n_{i},n_{w}\}\)). These two nodes answer to \(n_{j}\) with \(fr(n_{i},n_{z})\) and \(fr(n_{w},n_{z})\), respectively. Having this two values, \(n_{j}\) computes the formula in Equation (3), which becomes:  
\[dr(n_{j},n_{z})=\frac{fr(n_{i},n_{z})+fr(n_{w},n_{z})}{2}=\frac{0.74+0.84}{2}=0.79\]  
At this point, \(n_{j}\) will consider \(n_{z}\) reliable because \(dr(n_{j},n_{z})\geq 0.70\) and \(r\leq 2\). Algorithm 1 summarizes the steps of our approach for object-to-object reliability assessment  
As for the overall computational complexity of the above consensus mechanism, we can express it in terms of number of messages sent during the object-to-object reliability assessment. Therefore, leveraging asymptotic analysis, we can state that this solution guarantees a linear computational complexity, \(O(m)\), in the dimension, say \(m\), of the number of neighbors for a node. It is worth noting that, in this analysis, we do not consider the computational cost of the fingerprint rate \(fr(n_{i},n_{j})\), that depends on the fingerprint algorithm used during the _safe start phase_ (see Section 4).
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 5 Experimental Analysis
### Results of Data Set \(\widetilde{\Psi}_{3}\)  
Figure 15 shows the position error of ACNA, EKF and RIO in the east and north directions after GNSS slow-change attacks (data set \(\widetilde{\Psi}_{3}\)). After GNSS attacks on data set \(\widetilde{\Psi}_{3}\), the 1\(\sigma\) positioning accuracy of the GNSS increases to 34.27 m. Figure 16 shows the corresponding movement trajectory of the three algorithms under GNSS attacks. Their east, north and relative positioning accuracy (\(\sigma_{\text{east}}\), \(\sigma_{\text{north}}\) and  
It can be seen from the results that EKF is unable to detect GNSS slow-change attacks and its 1\(\sigma\) positioning accuracy increases to 32.76 m, with an improvement of 4% compared with the GNSS. The RIO algorithm detects slow-change attacks after it occurs for 26 s. Therefore, its 1\(\sigma\) positioning accuracy increases to 6.77 m, which is improved by 80% compared with GNSS. On the other hand, the ACNA algorithm can detect GNSS slow-change attacks in time and its 1\(\sigma\) positioning accuracy is 1.17 m, with an improvement of 97% compared with the GNSS.
## 5 Interference Rejection Performance of CRPA Software Receiver
### Single Interferer with High J/S  
A single interferer specified by 40 dB J/S, 30\({}^{\circ}\) elevation angle, and 60\({}^{\circ}\) azimuth angle is simulated to examine the software receiver performance. The interferer is added into dataset starting from 60 th second. Figure 13 (left) shows the filtered C/N\({}_{0}\) of all-in-view CRPA. All channels are still in tracking when high J/S interferer is injected. Figure 13 (right) shows the C/N\({}_{0}\) of three WAAS GEOs to compare performance of CRPA and single antenna. Before injecting the interferer, there is about 5 dB gain in C/N\({}_{0}\) of CRPA over single antenna. When the interferer is injected, the C/N\({}_{0}\) of all WAAS GEO channels in the single antenna lose lock, but the channels of CRPA continue tracking without losing more than 5 dB of the original C/N\({}_{0}\). Figure 14 shows Earth-North (EN) plots with and without interference. The interferer affects the positioning result with worse Circular Error Probable (CEP) because the interferer increases the noise level.  
Figure 12: Power spectral density of broadband and narrowband interferers.  
Figure 13: **(left)** C/N\({}_{0}\) of all-in-view CRPA channels. **(right)** C/N\({}_{0}\) of three WAAS GEO channels (CRPA and single antenna cases).
# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
## 2 Related Work  
### U-Net Model  
The full convolutional neural network [23] proposed by [PERSON] can accept input images of any size and uses a deconvolution layer to up-sample the feature map of the last convolution layer to restore it to the same size as the input image. Therefore, a prediction can be generated for each pixel while retaining the spatial information in the original input image, finally performing pixel-by-pixel classification on the up-sampled feature map. Based on fully convolutional networks (FCNs), [PERSON] et al. proposed the U-Net model [24], which has achieved great success in the field of cell segmentation in microscopic tissue sections, as the U-Net achieves acceptable results.  
The model has been widely used in various applications of semantic segmentation, such as for satellite image segmentation and industrial defect detection. The use of data augmentation can train some relatively small samples of data, especially medical-related data. The emergence of U-Net has been very helpful for deep learning for medical images with fewer samples. U-Net consists of an encoding path and a decoding path. The encoding path gradually reduces position information and extracts abstract features through the pooling layer. For accurate positioning, the local pixel features extracted from the decoding path will be combined with the new feature map during the upsampling process to retain some important feature information from the previous downsampling process to the greatest extent (see Figure 2a). In 2016, [PERSON] et al. proposed that the 3D U-Net model [25] is a simple extension of U-Net and can be applied to 3D image segmentation. The U-Net proposed in [26] is essentially a deeply supervised encoder/decoder network, in which the encoder and decoder subnetworks are connected through a series of nested, dense skip paths. The redesigned skip path aims to reduce the semantic gap between the feature maps of the encoder and decoder subnets.  
[PERSON] et al. [27] introduced an attention mechanism into U-Net. Before stitching the features at each resolution of the encoder and the corresponding features in the decoder, an attention module was used to readjust the output features of the encoder. Because U-Net's encoder/decoder structure and skip connection are a very classic design method, many papers have continued the core idea of U-Net, adding new modules or incorporating other design concepts, and all have achieved excellent results.
## 3 Results
### 4 HR Increased SIRT Activity  
To clarify the cellular survival mechanism against mitochondrial and ER stress after 4 HR administration, the expression level of SIRTs and SIRT activity were evaluated. The administration of 4 HR did not change the expression of SIRT1 and SIRT3 in HUVECs (Figure 5a). Interestingly, the administration of 4 HR did increase the expression of SIRT6 in HUVECs (Figure 5b). Quantitative RT-PCR results also demonstrated that the _SIRTs_ mRNA expression level was increased at 24 h after 4 HR administration (Figure 5c). A pan-SIRTs activity assay kit can detect the activity of all SIRT enzymes (SIRT1-7). Pan-SIRTs enzyme activity increased significantly with 10 and 100 uM 4 HR after 8 h and with 10 uM 4 HR  
Figure 4: IP-HPLC analysis. Expression of antioxidant- and protection-related proteins in 4 HR-treated HUVECs. The antioxidant proteins GSTO1 and SOD-1 were upregulated by 4 HR compared to the untreated control, while the antioxidant regulating transcription factor NRF2 and the protection-related proteins HO-1, HSP-70, and HSP-90 were downregulated. On the other hand, Sp-1 expression gradually increased by 5.7%, 15.1%, and 17.8% at 8, 16, and 24 h, respectively, while Sp-3 expression increased up to 122.4% at 8 h but gradually decreased to a normal level at 24 h. The line graph (**a**) shows protein expression (%) at 8, 16, and 24 h after 4 HR administration, whereas the star plot (**b**) shows the differential expression levels (%).  
after 24 h (Figure 5d, \(p<0.05\)). The data for the quantification of Western blot are shown in Supplementary Figure S9.  
Nicotinamide adenine dinucleotide+ (NAD+) is a substrate for SIRTs. NAD+ and the NAD+/NADH ratio were increased after 4 HR administration (Figure 5e,f). NADH levels were increased significantly upon the administration of 100 uM 4 HR after 8 and 24 h (Supplementary Figure S10, \(p<0.05\)).
## 3 Results
### Model Updating and Validation
[12], they are relatively close together. However, only four characteristic quantities are used in this paper. Figure 6 shows the point FRF curves at node 4. As can be seen from Figure 6, the FRF curve obtained by using the resonance and antiresonant frequencies as the characteristic quantities of the objective function is very close to the target value, not only in the updating frequency band but also outside the updating frequency band up to 700 Hz, indicating a good correlation between the two FRF curves. However, the point FRF curve obtained by using only resonance frequencies as the characteristic quantities of the objective function is in good agreement with the target value of only the first three-order resonance frequency 230 Hz, and then the frequency response function curves no longer coincide with each other, the deviation increases significantly and the correlation becomes worse, indicating that the dynamic characteristics outside the updating frequency band cannot be accurately described. Figure 6: The point FRF curves at node 4 of four conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Characteristic Quantities**} & \multirow{2}{*}{**Target Value (Hz)**} & \multicolumn{2}{c}{**Result I**} & \multicolumn{2}{c}{**Result II**} \\ \cline{3-6}  & & **Updated** & **Error** & **Updated** & **Error** \\  & & **Value (Hz)** & **(\%)** & **Value (Hz)** & **(\%)** \\ \hline
1 st resonance frequency & 10 & 10.25 & 2.5 & 10.5 & 5 \\
2 nd resonance frequency & 53 & 53.5 & 0.94 & 54.5 & 2.83 \\
3 rd resonance frequency & 232.5 & 232.5 & 0 & 231.5 & \(-\)0.43 \\
4 th resonance frequency & 403 & 400.8 & \(-\)0.55 & 423.3 & 5.04 \\
1 st antiresonant frequency & 33 & 33.25 & 0.76 & 32 & \(-\)3.03 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of resonance–antiresonant frequencies for target value and two groups.
## 4 Results
### Evaluation Result of Design Concept Scalability
#### 4.5.2 Complexity Analysis  
During integration, there is also an impact on the overall reduction in complexity. In the case of the slot-type design, the individual PCBs and payload components are independently mounted in their designated slots using a special spacer, avoiding interdependency between the subsystems, which increases easiness of accessing components quickly. This effect can be easily observed with the value of the liaison parameters, which shows a 78.45% reduction. The total complexity of the conventional design of BIRDS-3 is 8.59, whereas it is only 2.73 for the slot-type concept, which is, overall, 68.22% drop. Figure 21 below illustrates the general comparison of the complexity of both 1U designs.