esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Handle Detection Module
#### 2.1.2 Training  
All images in the dataset are set to a resolution of 64 \(\times\) 480 px to match the images from the Kinect camera. Sample images with applied bounding boxes for handles are shown in Figure 2. The dataset details are summarized in Table 1. The network training utilized the training and validation dataset described in Table 1. The training images underwent heavy augmentation. Localization was measured using the L1 norm (Manhattan metric), and classification employed the Focal Loss [28]. A prediction with an Intersection over Union (IoU) greater than 0.7 was considered a correct detection. The mean Average Precision (mAP) metric, calculated as the average area under the precision-recall curve for all classes, was used for final model evaluation. The optimization employed the Adam (Adaptive Moment Estimation) algorithm, which adjusts the momentum value during training. The learning rate was reduced using cosine descent (cosine decay) [29], with a baseline constant value of 0.001 and a warm-up phase of 1500 steps with a value of 0.00025. Pre-trained weights from the COCO dataset [30] were used as initial weights for the model, enabling fine-tuning specifically for the handle detection task. The training was scheduled for 20,000 steps.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **Number of Images** & **Number of Handles** \\ \hline Train & 1183 & 5145 \\ Validation & 280 & 1275 \\ Test & 100 & 581 \\ Sum & 1563 & 7001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the dataset used to train and verify neural networks for handle detection.  
Figure 2: Example annotations of the handles (**a**) and fronts (**b**) of articulated objects from the training dataset.
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks
### Scheduling Policies
#### 3.3.2 Throttling Regime
Thus, the average resource utilization (\(\overline{U}\)) during a TDD frame is expressed as \[\overline{U}=\sum\
olimits_{n=1}^{N}\frac{n\overline{g}}{max(n\overline{g},N _{s})}\pi(n)\] (29) The average number of active users (\(\overline{Q}\)) is given by \[\overline{Q}=\sum\
olimits_{n=1}^{N}n.\pi(n)\] (30)To calculate the average throughput (\(\overline{X}\)), Little's Law is applied:  
\[\overline{X} =\frac{\overline{x}_{on}}{\overline{t}_{on}}\text{ where }\overline{t}_{on}=\frac{\overline{Q}}{ \overline{D}}\]  
Here, \(\overline{D}\) represents the average number of departures per slot, which can be expressed as  
\[\overline{D}=\sum_{n=1}^{N}{(n)\pi(n)} \tag{31}\]  
Consequently, the average throughput can now be written as  
\[\overline{g}_{k}=\frac{DBR.T_{F}}{m_{k}} \tag{24}\]0  
Theoretical evaluations indicate that this method effectively provides the necessary performance parameters for the successful dimensioning of OFDMA networks, taking into account the various scheduling algorithms considered in this study. However, practical implementation is essential to gain insights into the margins of results obtained through this method compared to those that might be expected in real-world scenarios. This implementation will be carried out using a simulator that adheres more closely to the assumptions and constraints of a real network.
## 5 Delivery  
After assembly and tests, the CubeSat needs to be packed properly and transported to the launch service provider. In general, the place where the satellite is assembled may not necessarily be close to the launch facilities. Satellites are usually taken to an intermediate location first for their final integration to the launcher interface before going to the launch facilities. Service providers take the responsibility of the satellite from the moment it enters their facilities to the orbit insertion, and they sometimes even offer support to developers during the delivery process.
# Hybrid Quantum Dot as Promising Tools for Theranostic Application in Cancer
## 2 Significance of Hybrid Quantum Dot as Cancer Theranostics  
The significance of the theranostic approach in cancer treatment may utilize the simultaneous delivery of chemotherapeutics and photolytic agents for deep tumor penetration, which effectively damages and inhibits the tumor when treated with single irradiation. It may be utilized for tracking of progress of cancer therapy using incorporated imaging agents [39]. Hybrid QDs have been widely explored for their theranostic application in different types of cancers. However, toxicity issues of QDs due to their composition (heavy or inorganic materials) and nature (ROS generation and strong surface responses) raised concern for their modification/functionalization for biomedical applications. Hence, strategies have been conceptualized to minimize their toxicity and improve their biocompatibility through hybridization/functionalization with other moieties (such as polymers, lipids, polysaccharides, proteins, etc.), providing efficient accumulation in tumor tissues in addition to preventing their accumulation in healthy tissues [40; 41]. Biological molecules are attached to QDs using hydrophilic surfactant shells with reactive groups such as COOH, NH2, or SH. Attachments are made using different methods, such as adsorption, covalent bonding, electrostatic interaction, etc. [39; 40; 41]. It has been reported to be conjugated with a wide range of biological molecules, including biotin [42], folic acid [43; 44], antibodies [45], and peptides [46]. Silanization, which coats QDs with silica, is a good covalent coating method for hydroxyl-rich material surfaces. Silanization makes ligand molecules strongly cross-linked and chemically stable. The end terminal groups of the silane shell can expose thiol, phosphonate, or methyl terminal ends for subsequent QD coating and also make the material more biocompatible. Silanization is favored because it is less toxic than other ligands [47]. Silica shell thickness could control QD light responsiveness. The silica-coated QDs were modified with amino, carboxyl, and epoxy groups and stabilized with PEG segments to assess their applicability. These modified QDs efficiently conjugated with antibodies and were used as fluorescent labels in immunoassay detection [48]. An in vivo study has shown that emissive Si-QDs biodgrade quickly and produce non-toxic silicic acid that may be eliminated by urine [49].  
The perspectives of hybrid QDs for their utility in cancer diagnosis/imaging and delivery of payload specifically to tumor tissues are discussed in the subsequent section.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 3 Opportunistic User Scheduling with Information-Hiding Power Control
### Scheduling Parameters  
For \(n\)-th transmitter and its expected scheduling beam index \(l^{\ast}\in\{1,\cdots,S\}\), we define following scheduling metrics:  
\[\eta_{\text{Q}}^{[n,l^{\ast}]}\triangleq|\mathbf{u}_{0}^{[l^{ \ast}]T}\mathbf{h}_{n}|^{2}, \tag{9a}\] \[\eta_{\text{I}}^{[n,l^{\ast}]}\triangleq\sum_{l=1,l\
eq l^{\ast}} ^{S}|\mathbf{u}_{0}^{[l]T}\mathbf{h}_{n}|^{2},\] (9b) \[\eta_{\text{L}}^{[n]}\triangleq\max_{k\in\mathcal{K}}\|\mathbf{ g}_{\ast k}\|^{2}, \tag{9c}\]  
where \(\eta_{\text{Q}}^{[n,l^{\ast}]}\), \(\eta_{\text{I}}^{[n,l^{\ast}]}\), and \(\eta_{\text{L}}^{[n]}\) indicate a normalized signal quality in main channel, a normalized generating interference of \(n\)-th transmitter at the desired base station, and a maximum of normalized information leakage in the wiretap channel, respectively. Additionally, we devise pre-determined positive threshold values, \(\eta_{\text{I}}^{\star}\) and \(\eta_{\text{L}}^{\star}\), which represent for the maximum of allowable generating interference and information leakage, respectively.  
If we assume the internal eavesdroppers are compromised base stations in the network, it is feasible to acquire channel state information (CSI) of the eavesdroppers. However, it is hard for each user to acquire CSI of the eavesdroppers if eavesdroppers are external nodes. Even in this case, assuming CSI of the eavesdroppers at each user is still meaningful since it provides the theoretical intuitions on the achievable secrecy sum-rate. Including [19; 22; 29], lots of previous studies assumed the perfect CSI of eavesdroppers.  
Note that \(\eta_{\text{L}}^{[n]}\) in (9c) only depends on transmitter index \(n\) since the expected scheduling beam index \(l^{\ast}\) in main channel does not affect information leakage in wiretap channel. In addition, regardless of eavesdropping scenarios, we consider the same scheduling metric for information leakage in wiretap channel. Further, the optimal values of \(\eta_{\text{I}}^{\star}\) and \(\eta_{\text{L}}^{\star}\) can be obtained through simulation based on system parameters such as \(N\), \(K\), \(M\), and \(S\).
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Advantage of Proposed Method over 2D-SSA
#### 3.5.2 Applying SP-SSA on HSI  
The HSI is composed of a stack of 2D images carrying valuable information about each spectral band. To demonstrate the effectiveness of the proposed method, a randomly selected spectral band at 667 nm was considered for our analysis. Figure 16b,c contains the scene as reconstructed by \(2D\)-\(SSA\) and \(SP\)-\(SSA\), respectively. Since the HSI was acquired over a large area, it includes multiple objects with different textural information. This is a typical case where object-specific reconstruction works better than direct reconstruction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Z**} & **Indian Pines** & **Pavia University** & **Salinas** & **Houston 2018** \\ \cline{2-5}  & \multicolumn{4}{c}{**Proposed Method (SP-SSA)**} \\ \hline SVM & 42.534 & 56.342 & 37.215 & 61.512 \\ EPF & 39.152 & 48.186 & 25.113 & 57.084 \\ SCMK & 27.467 & 37.428 & 21.421 & 43.115 \\ R2 MK & 23.615 & 28.521 & 18.472 & 39.721 \\ ASMGSSK & 10.624 & 16.832 & 8.351 & 18.521 \\ MsuperPCA & 21.524 & 19.441 & 11.486 & 35.431 \\
2 DSSA & 34.321 & 31.084 & 15.321 & 51.322 \\
2 DMSSA & 16.819 & 23.217 & 9.091 & 27.634 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics of the McNemar Test for the Indian Pines, Pavia University, Salinas, and Houston 2018 datasets.  
Figure 15: (**a**) Cameraman image (**b**) 2D-SSA Reconstructed image [MSE = 115.8865] (**c**) SP-SSA reconstructed image [MSE = 93.0468] (**d**) Test image (**e**) 2D-SSA Reconstructed image [MSE = 287.5323] (**f**) SP-SSA reconstructed image [MSE = 237.1038].  
Textural information can be highlighted accurately by using local reconstruction as opposed to global reconstruction. The error in SP-SSA-based reconstruction is indeed lower as compared to 2D-SSA-based reconstruction. The same conclusion can also be drawn from Figure 16.  
In the case of 2D-SSA-based reconstruction, the Mean Square Error (MSE) is 612.4349, while, in the case of SP-SSA-based reconstruction, the MSE is 504.5685. Figure 16d,e contains the difference image for 2D-SSA-based reconstruction and SP-SSA-based methods. It can be clearly observed that edge information is preserved with the proposed method. The SP-SSA-based reconstruction is applied to all spectral bands and generates a modified hypercube with preserved local structure information and minimum noise level. These latter features generally lead to better classification performance.
## 2 Related Works
### LOS Angular Rate Extraction  
All the forms of IGC mentioned above require accurate information about the relative motion between the flight vehicle and the object. As for the strapdown seeker, since the detector of the strapdown seeker is fixed with the body of the flight vehicle, the directly measured information is the BLOS angles instead of the LOS angles. For example, as is shown in Figure 1, if only the longitudinal motion is considered, the measured information for the gimbaled seeker is \(q_{z}\) and its time derivative \(\dot{q}_{z}\). As for the strapdown seeker, the measured information is the BLOS angle \(q_{Bz}\). Assume that \(\varphi\) is the pitch angle of the flight vehicle; it has \(\varphi=q_{z}+q_{Bz}\) and \(\omega_{z}=\dot{q}_{z}+\dot{q}_{Bz}\), where \(\omega_{z}\) is the pitch angular rate. Therefore, the unmeasured \(\dot{q}_{Bz}\) indicates that \(\dot{q}_{z}\) cannot be obtained or calculated directly. Therefore, it is useful to discuss the LOS angular rate extraction problem for flight vehicles with a strapdown seeker.  
A straightforward approach involves numerically differentiating the measured BLOS angles to obtain the BLOS angular rate, which, however, may amplify the measurement noise. A Kalman Filter (KF) is proposed for the INS/seeker [25] and its observability problem was analyzed. As a promotion, the Extended Kalman Filter (EKF) [26] and Unscented Kalman Filter (UKF) [7] are also applied to extract the LOS angular rate. The reconstruction of the LOS angular rate is researched in [7], by combing the Tracking Differentiator and UKF. The Extended State Observer (ESO) has excellent performance in estimating the states and disturbance, and is expanded to IGC. The IGC strategy of the missile with a strapdown seeker is researched in [27], which, however, only considers the longitudinal-plane movement.  
These mentioned references only discuss how to extract the LOS angular rate from the measured BLOS angles; however, the influence of the estimated LOS angular rate on the system has not been discussed.
## 4 Results
### Computation Time and Efficiency  
The total computation time for the experiments was 5.7 h with training performed on an NVIDIA RTX 4090 GPU (see Table 5). The models were trained for up to 25 epochs, with early stopping applied based on validation loss to prevent overfitting. The models demonstrated efficient convergence, particularly when employing the optimized hyperparameters identified through the Optuna framework.  
The inclusion of multiple SE blocks before the fusing of the mid to high level features led to a slightly improved performance compared to one SE block after the fused features. Nonetheless, both scenarios needed the same training time of 1.60 h as well as the same inference time of 1.33 ms per image in both multi-label settings based on a 32-frame batch size.  
To assess feasibility for real-time deployment, the proposed CNN-SE-FFM-BiLSTM model was also evaluated using a 1-frame batch size, simulating single-frame inference typical in surgical assistance systems. Under this setting, the proposed CNN-SE-FFM-BiLSTM model required only 2.90 GFLOPs per image, had 8.27 million parameters, and consumed 0.27 GB of GPU memory. It achieved an average inference latency of 11.58 ms, corresponding to 86.3 FPS, compared to 25 FPS for the original video recording in the Cholec80 dataset.
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 4 Discussion
### Hemoglobin  
One advantage of fNIRS over techniques such as fMRI is that we can measure the relative change of both oxy-, as well as deoxyhemoglobin at the same time. The balloon model, a model describing the transformation of neuronal activation into a hemodynamic signal, shows a clear pattern of increase in oxy- as well as a slight decrease in deoxyhemoglobin in the region with neuronal activity [68]. Still, various studies use only one or the other hemoglobin change when evaluating the indirect marker of neuronal activity. Whereas oxyhemoglobin is considered to be more closely related to cerebral blood volume and global effects and has a higher signal to noise ratio [69], deoxyhemoglobin is often thought to be more closely linked to regional activity with inherently smaller SNR [70]. We evaluated the sensitivity for oxy- and deoxyhemoglobin as well as the condition in which a significant increase in oxy- has to be accompanied by a significant decrease in deoxyhemoglobin in the same channel.  
We found that, a significantly greater number of channels were considered significant in the oxy- or deoxyhemoglobin only condition in comparison to the combined condition (Figure 11). In addition, results for specific channels were less stable in the oxyhemoglobin alone condition between the different LF de-noising methods (Figure 12, Table 1). Especially for no LF de-noising and PCA, the most activated channel across participants changed from channel 4 to channel 5 between oxy- and deoxyhemoglobin respectively, (Table 1). This shows how results can differ when only one hemoglobin species is considered [48], instead of viewing the combined information provided, even when adjusted for LF noise. This is especially true with single subject analysis and with concentration on single channels instead of brain areas [71].
## 3 Materials and Methods
Figure 3: Example of MODIS (**a**) and ASTER (**b**) pixel extractions. This area is classified as Deciduous Needle-leaf forest (Yakutsk, Russia) on MODIS 2007 IGBP. Each red box in (b) corresponds to a 3 km-by-3 km extraction area, that is, a 201-by-201 pixel area on ASTER and a 6-by-6 pixel area on MODIS. The three ASTER EVIs and MODIS EVI were computed from 1 km surface reflectances (Equation 12(a,d)). We also derived ASTER NDVI and MODIS NDVI (Equation 11(a,b)) in order to compare their relationship with those between ASTER EVIs and MODIS EVI:  
\[NDVI_{ASTER}=\frac{\bar{\rho}_{ASTER,NIR}-\bar{\rho}_{ASTER,red}}{\bar{\rho}_{ ASTER,NIR}+\bar{\rho}_{ASTER,red}} \tag{11a}\]  
\[NDVI_{MODIS}=\frac{\rho_{MODIS,NIR}-\rho_{MODIS,red}}{\rho_{MODIS,NIR}+\rho_{ MODIS,red}} \tag{11b}\]  
\[EVI_{B}=2.5\cdot\frac{\bar{\rho}_{ASTER,NIR}-\bar{\rho}_{ASTER,red}}{\bar{ \rho}_{ASTER,NIR}+\bar{\rho}_{ASTER,red}+1} \tag{12a}\]  
\[EVI_{P}=2.5\cdot\frac{\bar{\rho}_{ASTER,NIR}-\bar{\rho}_{ASTER,red}}{\bar{ \rho}_{ASTER,NIR}+2.4\cdot\bar{\rho}_{ASTER,red}+1} \tag{12b}\]  
Figure 2:0  
Figure 2:1  
ASTERMODIS EVI and NDVI relationships were analyzed by three means. First, ASTER EVIs were plotted and regressed against MODIS EVI to examine overall trends in their relationships. The simple linear model used in previous studies [33,34] were also found reasonable based on our preliminary analysis of this dataset:  
Figure 2:2  
Figure 2:3  
where \(\varepsilon_{0}\) and \(\varepsilon_{1}\) are the unexplained error terms and \(EVI_{ASTER}\) is either \(EVI_{B}\), \(EVI_{P}\), or \(EVI_{C}\). Second, differences (\(D\)) and relative differences (\(RD\)) of VIs between ASTER and MODIS were computed to analyze land cover dependencies of ASTER _vs_. MODIS relationships:  
Figure 2:4  
Figure 2:5  
Figure 2:6  
Figure 2:7  
We also examined the impact of aggregation methods by comparing ASTER VIs computed from aggregated reflectances with MODIS PSF (\(\overline{NDVI}_{PSF}\) and \(\overline{EVI}_{PSF}\)) to those without MODIS PSF (\(\overline{NDVI}\) and \(\overline{EVI}\)). Figure 2:8  
Figure 2:9_Remote Sens._**2012**, \(4\)  
Table 1.0 (17(a)) Table 1.1 (17(b))  
Finally, overall differences between ASTER and MODIS VIs were assessed with root mean square errors (RMSE):  
Table 1.2 (18(a)) Table 1.3 (18(b))  
where \(n\) is the sample size, and \(NDVI_{MODIS,l}^{{}^{\prime}}\) and \(EVI_{MODIS,l}^{{}^{\prime}}\) are the predicted \"MODIS-like\" values from ASTER NDVI and EVIs, respectably, using the linear regression equations derived in the first analysis (Equations 13(a,b)).
## 5 Conclusions  
In this study, a hierarchical optimization algorithm is proposed to solve the Taiji program's system-level frequency setting scheme. The optimization model considered the effects of six main factors, namely \(f_{\rm lower}\),\(f_{\rm upper}\),\(f_{\rm ADC}\),\(f_{\rm PT}\),\(f_{\rm USO}\), and \(m\). Two optimization objectives were used, including minimizing the total readout displacement noise and maximizing the feasible beat-note frequency range. Considering the characteristics involved in solving multi-objective optimization problems, 20 Pareto-optimal solutions were obtained. The minimum total readout displacement noise was 4.12 pm\(/\sqrt{\rm Hz}\), which corresponded to a beat-note frequency feasible range of 21 MHz, with (\(f_{\rm lower}\), \(f_{\rm upper}\),\(f_{\rm ADC}\),\(f_{\rm PT}\),\(f_{\rm USO}\), \(m\)) = (3 MHz, 23 MHz, 96 MHz, 94 MHz, 4512 MHz, 0.44). The maximum feasible range of the beat-note frequency was 23 MHz with a total readout displacement noise of 4.21 pm\(/\sqrt{\rm Hz}\), with (\(f_{\rm lower}\),\(f_{\rm ADC}\),\(f_{\rm PT}\),\(f_{\rm USO}\), \(m\)) = (3 MHz, 25 MHz, 92 MHz, 90 MHz, 4140 MHz, 0.44). Hence, different values of the parameters \(f_{\rm upper}\),\(f_{\rm ADC}\),\(f_{\rm PT}\), and \(f_{\rm USO}\) result in different final optimization results. Therefore, these two objectives were not simultaneously optimized, and a trade-off between these two objectives needs to be made in practical applications of this system. Moreover, we analyzed the effects of \(P_{\rm tel}\) and \(f_{\rm upper}\) on \(P_{\rm receive}\) and \(P_{\rm local}\), and then explored the effects of  
Figure 6: Variations of the optimal \(P_{\rm local}\) value and the minimum total readout displacement noise with \(f_{\rm upper}\). The x-, left-hand y-, and right-hand y-axes show \(f_{\rm upper}\), the optimal \(P_{\rm local}\), and the minimum total readout displacement noise, respectively.  
these two factors on the total readout displacement noise. The results provide a reference for setting the frequency setting strategy during laser transmission and readout, determining the power ratio between the sidebands and carrier and selecting the relevant equipment parameters of laser interferometry systems in the Taiji program.  
Conceptualization, X.M., Z.Y. and X.P.; methodology, J.Z., C.G. and M.Z.; validation, X.M., Z.Y. and X.P.; writing--original draft preparation, J.Z. and X.M.; writing--review and editing, X.M., Z.Y. and W.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2020 YFC2200100) and the Chinese Academy of Sciences Strategic Pioneer Program on Space Science (Grant No. XDA1502110201).  
Not applicable.  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
We would like to thank [PERSON] and [PERSON] in Institute of Mechanics for the helpful discussion.  
The authors declare no conflict of interest.
## 3 Methodology  
The structure of the proposed network is illustrated in Figure 2. It comprises both an encoder and a decoder path, both based on Swin-Transformer. Each MRI volume is processed in the encoder path as a patch. After passing through the encoder, we repeatedly apply upsampling followed by a Swin-Transformer block to generate high-resolution segmentation results and the corresponding \(SDM\). The semantic gap problem is alleviated by the skip connections between the encoder and decoder. In addition, an attention-gating mechanism is added between the encoder and decoder features to suppress the features of irrelevant background regions. Finally, after the decoder, the segmentation result and the signed distance map of the liver and the tumor are, respectively, output through the \(Softmax\) layer and the \(Tanh\) layer. In addition, we used self-supervised learning to pretrain the model. The details are as follows:  
Figure 2: The schematic of our segmentation model is as follows. It includes an encoder that extracts and compresses features and a decoder that reconstructs and restores them. Skip connections with attention gates are used between the encoder and decoder at each stage. Both components are built upon the Swin-Transformer block. After feature extraction and reconstruction, the resulting features are used separately for segmentation tasks and SDM regression tasks. The segmentation block includes a \(2\times 2\) deconvolution layer and a \(1\times 1\) convolution layer.