esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Power-Domain NOMA Systems: An Overview
## 2 Non-Orthogonal Multiple Access
### Benefits of NOMA over OMA  
The merits of NOMA over OMA are discussed as follows.  
* **High spectral efficiency**: NOMA has a higher SE when compared with OMA, because one RB offers services to many users. In OMA systems, each user is allotted by one RB, resulting in bandwidth loss [57]. NOMA may also be readily discussed as mMIMO, mmWave, HetNets, and device-to-device (D2D) systems to further boost network throughput.
* **Massive connectivity**: Because of its non-orthogonal features, NOMA has the capability to incorporate high plurality of smart devices. Having smaller and irregular size of packets, it is suited for both the tactile internet [58] and the IoT [59].
* **Fairness**: NOMA ensures user fairness by assigning a higher power to weak users (those with poor channel conditions) and a smaller amount to strong users. Then, in terms of throughput, stronger and weaker users are assured QoS.
* **Ultra low latency**: Due to HetNet design of B5G networks, latency requirements are more demanding. Because OMA methods rely on access-grant requests, which increases transmission delay and signalling cost, they are not appropriate for such architectures. To overcome this problem, NOMA is utilised which allows for grant-free transmission, which is highly useful in the uplink scenario. Furthermore, NOMA allows for variable scheduling of many devices based on the application and QoS requirements.
# 185-215 GHz CMOS Frequency Doubler with a Single Row Staggered Distribution Layout Design
## 2 Circuit Design  
As shown in Figure 1, the presented amplifier-doubler chain was fabricated in commercial 40 nm bulk CMOS technology. The maximum cutoff frequency fmax of the NMOS transistor by this manufacturing process was 290 GHz. The supply voltages of VDD and VB were set to 0.9 V and 0.75 V, respectively.  
At the input port of the designed amplifier-doubler chain, the single-ended signal was converted to a differential by an integrated 110 GHz rat race balun. The topology of the DA was given by the pseudo-difference structure, and the topology of the doubler was given by the push-push structure.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 4 Experiments
### Experimental Results
#### 4.5.2 Clustering Ability to Large-Scale Dataset  
We select \(N\) samples randomly for each dataset to train DCRSM, calculate the self-expressive matrix for the entire test samples and evaluate their ACC, NMI, and ARI results to evaluate their generalization ability. Tables 3-6 illustrate the ACC, NMI, and ARI results of different datasets with various training numbers. The best results are marked in red and the second best results are marked in blue. The results show that our DCRSM can achieve relatively good performance even training with a small amount of data. For the Fashion-MNIST dataset, compared with the DMSC-UDL, we only need to use half the samples to achieve the same accuracy. For COIL-100, when the training number is 2000 which is less than a third of the whole data, DCRSM can achieve a reasonably good performance. The result shows that our approach has good scalability and adaptability when dealing with large-scale datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Fashion-MNIST**} & \multicolumn{2}{c}{**COIL-20**} & \multicolumn{2}{c}{**COIL-100**} & \multicolumn{2}{c}{**YTF**} \\ \cline{2-9}  & **ACC(\%)** & **NMI(\%)** & **ACC(\%)** & **NMI(\%)** & **ACC(\%)** & **NMI(\%)** & **ACC(\%)** & **NMI(\%)** \\ \hline DEC [36] & 51.80 & 54.60 & 68.00 & 80.25 & 55.30 & 60.25 & 37.10 & 44.60 \\ DCCA [49] & 52.74 & 53.82 & 55.76 & 64.91 & 57.88 & 63.43 & 45.19 & 60.35 \\ BMVC [61] & 45.36 & 38.05 & 34.31 & 40.33 & - & - & 28.13 & 38.28 \\ GMC [62] & 56.70 & 62.90 & 74.17 & 82.50 & - & - & 55.40 & 74.22 \\ DGCCA [51] & 56.28 & 57.04 & 54.01 & 62.40 & - & - & 47.26 & 61.38 \\ DMJC [63] & 61.41 & 63.41 & 72.99 & 81.58 & 68.53 & 74.96 & 61.15 & 77.40 \\ CMSC-DCCA [64] & 62.95 & 68.33 & 77.84 & 86.53 & - & - & 66.15 & 82.67 \\ EDESC [46] & 63.10 & 67.00 & 75.45 & 87.05 & 71.45 & 85.75 & 66.32 & 80.98 \\ DMSC-UDL [55] & 65.45 & **68.34** & 79.24 & 89.00 & 73.58 & 89.32 & 68.25 & 83.14 \\ Our method & **69.65** & 66.85 & **81.18** & **89.15** & **76.95** & **93.80** & **70.69** & **86.55** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The clustering results on Fashion-MNIST, COIL-20, COIL-100, YTF of different methods.
# Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies
## 6 Discussion and Recommendations
### Recommendations for Research Ethics Review  
Such a trust-based approach would, however, affect how RECs/IRBs review research submissions. Most importantly, reviewers need to consider the main actors involved in any research and their expectations. This suggests a number of main considerations during review:  
1. The research proposal should first describe in some detail the trustworthiness basis for the research engagement. I have used characteristics from the literature--integrity, benevolence, and competence--though others may be more appropriate such as reputation and evidence of participant reactions in related work.
2. The context of the proposed research should be disclosed, including the identification of the types of contextual effects which might be expected. These may include the general socio-political environment, existing relationships that the research participant might be expected to be aware of (such as clinician-patient), and any dynamic effects, such as implications for other cohorts, including future cohorts. Any such contextual factors should be explained, justified and appropriately managed by the researcher.
3. The proposed dialogue between researcher and research participant should be described, how it will be conducted, what it will cover, and how frequently the dialogue will be repeated. This may depend, for example, on when results start to become available. The frequency and delivery channel of this dialogue should be simple for the potential research participant. This must be justified, and the timescales realistic. This part of the trust-based consent process might also include how the researcher will manage research participant withdrawal.  
The intention with such an approach would be to move away from the burdensome governance described in the literature (see [47; 48], for instance), instead focusing on what is of practical importance to enter into a trust relationship and what might encourage a more natural and familiar communicative exchange with participants. Traditional information such as the assumed benefits of the research outcomes should be confined to the research ethics approval submission; it may not be clear to a potential research participant how relevant that may be for them to make a decision to engage. Review ultimately must consider the _Context_ (see Section 3 above) within which a participant develops a _Willingness to engage_.  
The ethics review process thereby becomes an evaluation not only a consideration of the typical cost-benefit to the research participant, but rather of how researcher and research participant are likely to engage with one another to collaborate effectively on an equal footing and sharing the risks of failure. The participant then becomes a genuine actor within the research protocol rather than simply a subject of observation.
## 4 Test Results and Discussion
### Correlation Analysis
Several equations estimating the friction angles based on the correlation with SPT-N values are presented in Table 6, and the measured friction angles in this study are plotted in Figure 9 with the equations. As shown in Figure 9, the measured friction angles of grade IV and V granite are distributed below the friction angles calculated by the previously suggested equations, and they are rarely found to have a specific correlation with the SPT-N value. Therefore, it is expected that the present method can appropriately predict the friction of grade IV and V granite, whose friction angle is hard to evaluate with SPT-N values. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Correlation Equation** \\ \hline [PERSON], [PERSON], and [PERSON] (PHT) (1974) & \(\phi\approx 54-27.6034\times\exp(-0.014(N_{1})_{60})\) \\ as mentioned in [PERSON] and [PERSON] (1990) & \(\phi=\sqrt{20(N_{1})_{60}+20\ for\ 3.5\leq\left(N_{1}\right)_{60}\leq 30}\) \\ \hline [PERSON] and [PERSON] (1996) & \(\phi=\sqrt{20(N_{1})_{60}+20\ for\ 3.5\leq\left(N_{1}\right)_{60}\leq 30}\) \\ \hline PHT (1974) & \(\phi=27.1+0.3(N_{1})_{60}-0.00054(N_{1})_{60}^{2}\) \\ as mentioned by [PERSON] (1989) & \(\phi=\sqrt{15.4(N_{1})_{60}}+20\) \\ \hline [PERSON] et al. (2001) based on data from [PERSON] and [PERSON] (1996) & \(\phi=\sqrt{15.4(N_{1})_{60}}+20\) \\ \hline Specifications for Highway Bridges (SHB) & \(\phi=\sqrt{15(N_{1})_{60}}+15\ for\ (N_{1})_{60}>5\ and\ \phi\leq 45^{\circ}\) \\ Japan, JRA (1996) & \(\phi=\sqrt{15(N_{1})_{60}}+15\ for\ (N_{1})_{60}>5\ and\ \phi\leq 45^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of equations correlating the friction angle to the corrected standard penetration test (SPT)-N value [31]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weathering** & \multicolumn{5}{c}{**Chemical Weathering Index**} \\ \cline{2-5}
**Grade** & **VR** & **MWPI** & **CIA** & **I\({}_{\text{mob}}\)** \\ \hline \(k\) & 0.722 & 0.941 & 0.619 & 0.675 \\ \hline R\({}^{2}\) & 0.58 & 0.45 & 0.66 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Curve-fitting parameter. Figure 8: (**a**) Predicted friction angle with measured friction angle; (**b**) histogram of the predicted friction angle over measured friction angle.
## 4 Experiments and Results
### Results of Acoustic Data-Driven Subword Units
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\#Clusters** & **40** & **80** & **120** & **160** \\ \hline News & 14.4 & 14.1 & **13.2** & 13.7 \\ Web & 20.2 & 19.0 & **18.5** & 18.9 \\ SPT & 52.9 & 51.0 & **48.9** & 50.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Word error rate (%) of the Pho-LTV-Kmeans units according the number of clusters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Units** & **AM** & **News** & **Web** & **SPT** \\ \hline \multirow{3}{*}{Pho-LTV-Kmeans-120 [9]} & CI-GMM-HMM & 37.5 & 54.7 & 90.0 \\  & CD-GMM-HMM & 22.3 & 33.6 & 73.9 \\  & CD-LSTM-HMM & 13.2 & 18.5 & 48.9 \\ \hline \multirow{3}{*}{Gra-MFCC-Tree-120 [11]} & CI-GMM-HMM & 35.5 & 54.4 & 89.7 \\  & CD-GMM-HMM & 22.9 & 34.2 & 73.4 \\  & CD-LSTM-HMM & 15.6 & 20.0 & 50.2 \\ \hline \multirow{3}{*}{Pho-MFCC-Tree-120} & CI-GMM-HMM & 34.4 & 51.4 & 88.7 \\  & CD-GMM-HMM & 21.1 & 32.6 & 72.7 \\  & CD-LSTM-HMM & 13.7 & 18.6 & 49.3 \\ \hline \multirow{3}{*}{Pho-LTV-Tree-120 (proposed)} & CI-GMM-HMM & 33.6 & 51.2 & 88.4 \\  & CD-GMM-HMM & 20.7 & 32.2 & 72.3 \\ \cline{1-1}  & CD-LSTM-HMM & **12.9** & **18.0** & **47.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Word error rate (%) of the subword units obtained by various methods. obtained by the proposed method, is more suitable for spontaneous speech recognition than the phoneme unit, in order to handle the acoustically low discrimination problem in spontaneous speech. The proposed method can also be applied to other languages. We confirmed that the proposed unit shows the best performance in Korean spontaneous speech. In Korean, Hangul characters are closely related to their phonetic realizations; a similar language in this regard is Spanish. In other words, Korean grapheme-to-phoneme mapping is close to one-to-one. On the other hand, some languages have a poor correspondence between their graphemes and phonemes, such as English [19]. If the lexicon of the target language exists, we can build an acoustically discriminating subword unit using the proposed method. In addition, even if the lexicon does not exist, the proposed method can be utilized to build a phoneme-like unit from the grapheme units, similar to the previous study [11].
# High-Precision Chip Detection Using YOLO-Based Methods
## 3 Results
### Experimental Results  
#### 3.3.1 Visualization of the Results  
The hardware environment for this experiment was as follows: CPU Intel i5-13500 HX, GPU NVIDIA GTX 4050, memory 16 G. The operating system was Windows 11. The software environment for the experiment consisted of Ultralytics-8.3.7, Python-3.11.0, and PyTorch-2.6.0+cu124. During training, the total number of epochs was set to 100, the batch size was set to 16, the Adam optimizer was chosen, the initial learning rate was set to 0.01, and the weight decay coefficient was set to 0.0005. Moreover, 10% of the data was used for validation. Three sets of anchor boxes were used. The training convergence curves of YOLOv11n, GM-YOLOv11-backbone, GM-YOLOv11-head, and GM-YOLOv11 are shown in Figure 9.  
As can be seen from Figure 9, the convergence curves of the YOLOv11n, GM-YOLOv11-backbone, GM-YOLOv11-head, and GM-YOLOv11 models are very close, indicating that their performance during the learning process is basically consistent after replacing the CNN with the GM. The results of chip detection by GM-YOLOv11-DNMS are shown in Figure 10.
## 1 Introduction
[15] integrated an ANFIS model with a teaching-learning based optimization (TLBO) and a biogeography-based optimization (BBO) model. According to the authors, the two novel data mining methods could be useful in solving non-linear and high-dimensional problems and overall could be useful for groundwater management and exploration development projects. In this context, the current study presents a novel hybrid integration approach of FR with artificial intelligence-based kernel logistic regression (KLR), alternating decision tree (ADTree), and RF models for groundwater spring potential mapping, having as a test site the Ningtiaota region, China. A hybrid integration approach of FR, KLR, ADTree, and RF is a relatively new contribution that has been seldom used for modeling of groundwater spring potential areas. It should also be mentioned that limited studies have been conducted concerning groundwater spring potential mapping in China, therefore, this research aims to fill this gap in the literature.
# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 5 Conclusions  
Deep learning plays an essential role in recent neuroimaging studies. Advancements in applications of deep learning to pediatric neuroimaging have been illustrated in this review. Complex deep learning models such as CNN and GAN have shown superior performance in neuroimaging recognition, prediction, segmentation, and generation tasks. Semi-supervised learning demonstrated great potential in the utilization of weakly labeled or unlabeled data. Challenges such as overfitting, preprocessing variations, and interpretation issues remain in many neuroimaging studies, but data-sharing platforms, standardization of preprocessing protocols, and advanced interpretation approaches have been proposed to tackle such difficulties. Future neuroimaging research on large scales will not only achieve high accuracy but also benefit the understanding of the brain functions and neurodevelopment.  
Writing--original draft preparation, M.H.; writing--review and editing, K.-K.A., H.Z. and C.N. All authors have read and agreed to the published version of the manuscript.  
The research is supported by Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, and also by the A*STAR Strategic Programme Funds Project No. C211817001 Brain Body Initiative.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ABCD The Adolescent Brain Cognitive Development  
ABIDE Autism Brain Imaging Data Exchange  
ADHD Attention deficit hyperactivity disorder  
ASD Autism spectrum disorder  
ASL Arterial spin labeling  
CNN Convolutional neural network  
dHCP Human Connectomme Project Development  
DTI Diffusion tensor imaging  
fMRI functional MRI  
GAN Generative adversarial network  
HBN Human Brain Network  
HC Healthy control  
ICBM International Consortium for Brain Mapping  
IMPAC Imaging Psychiatry Challenge  
MAE mean absolute error  
MLP Multi-layer perceptron  
MRI Magnetic resonance imaging  
MSE mean squared error  
NDAR National Database for Autism Research  
PNC Philadelphia Neurodevelopmental Cohort  
PRISMA preferred reporting items for systematic reviews and meta-analysis  
PSNR Peak signal-to-noise ratio  
rs-fMRI resting-state fMRI  
sMRI structural MRI
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
## 4 The Security of the Public-XOR-Key Scheme
### The Security Analysis of the Public-XOR-Key Scheme
According to Bayesian formula, the conditional probability of \(QK_{1}=0\) in Equation (16) is  
\[p(QK_{1}=0|B=b_{w})=\frac{p(B=b_{w}|QK_{1}=0)\cdot p(QK_{1}=0)}{p(B=b_{w})}. \tag{17}\]  
According to the formula of total probability,  
\[p(B=b_{w})=p(B=b_{w}|QK_{1}=0)p(QK_{1}=0)+p(B=b_{w}|QK_{1}=1)p(QK_{1}=1). \tag{18}\]  
Since \(b_{i}=Qk_{1}\oplus Qk_{i+1}\), the distribution of \(b_{i}\) depends on \(Qk_{i+1}\) and \(Qk_{1}\). When the random variable \(QK_{1}=0\), there is \(B_{i}=QK_{i+1}\). Then  
\[p(B=b_{w}|QK_{1}=0)=p(QK^{\prime}=b_{w}|QK_{1}=0)=p(QK^{\prime}=b_{w}), \tag{19}\]  
where \(QK^{\prime}\equiv(QK_{2}\ QK_{3}\...\ QK_{n})\) is a random variable with the length of \(n-1\). The difference between the random variables \(QK\) and \(QK^{\prime}\) is that \(QK^{\prime}\) does not contain \(QK_{1}\). Therefore, the random variables \(QK^{\prime}\) and \(QK_{1}\) are independent of each other and the second equality of the above equation holds. Similarly, when \(QK_{1}=1\), there is \(B_{i}=QK_{i+1}\oplus 1\). Then  
\[p(B=b_{w}|QK_{1}=1)=p(QK^{\prime}=\overline{b_{w}}|QK_{1}=1)=p(QK^{\prime}= \overline{b_{w}}), \tag{20}\]where \(\overline{b}\equiv\left(\overline{b_{1}}\...\ \overline{b_{i}}\...\ \overline{b_{n}}\right)\), \(\overline{b_{i}}=b_{i}\oplus 1\). According to Equation (18),  
\[p(B=b_{w})=p\cdot p(QK^{\prime}=b_{w})+(1-p)\cdot p(QK^{\prime}=\overline{b_{w}}). \tag{21}\]  
When \(QK^{\prime}=b_{w}\), there is \(w\) number of ones, and \(n-w-1\) number of zeros in the random variable \(QK^{\prime}\). When \(QK^{\prime}=\overline{b_{w}}\), there are \(w\) zeros and \(n-w-1\) ones. Then  
\[p(B=b_{w})\] \[= p\cdot p(QK^{\prime}=b_{w})+(1-p)\cdot p(QK^{\prime}=\overline{b_ {w}}) \tag{22}\] \[= p^{n-w}(1-p)^{w}+p^{w}(1-p)^{n-w}.\]  
Therefore, the conditional probability of \(QK_{1}=0\) is  
\[p(QK_{1}=0|B=b_{w})=\frac{p^{n-w}(1-p)^{w}}{p^{n-w}(1-p)^{w}+p^{w}(1-p)^{n-w}}. \tag{23}\]  
Similarly, the conditional probability of \(QK_{1}=1\) is  
\[p(QK_{1}=1|B=b_{w})=\frac{p^{w}(1-p)^{n-w}}{p^{n-w}(1-p)^{w}+p^{w}(1-p)^{n-w}}. \tag{24}\]  
Then the conditional entropy \(H\left(QK_{1}|B\right)\) is  
\[p(B=b_{w})=p(B=b_{w}|QK_{1}=0)p(QK_{1}=0)+p(B=b_{w}|QK_{1}=1)p(QK_{1}=1). \tag{18}\]0  
The equivocation \(H(QK_{1}|B)\) measures the uncertainty of the QKD key \(Qk_{1}\) when given the public XOR keys. If and only if \(p=0\) or \(p=1\), the conditional entropy is null. Actually the security of point-to-point QKD systems guarantees that \(0<p<1\). Therefore, \(H(QK_{1}|B)\) cannot be null and [PERSON] can never confirm the value of \(Qk_{1}\). The public-XOR-key scheme is secure against the adversary under the above assumption.
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 2 Materials and Methods
### Model Architecture Design
#### 2.2.3 Stage-Wise Residual Stacking  
To progressively extract hierarchical features and enable the ResNet-50 model to capture disaster image characteristics with greater precision and comprehensiveness, we employ a stage-wise design. In the shallow stages, the model primarily focuses on fundamental features such as edges and textures, capturing fine-grained details. As the network deepens, it learns more semantic and abstract representations, such as local and global structures of various disaster types, enhancing classification accuracy. Moreover, stacking residual blocks plays a crucial role in improving the feature learning capacity of the disaster classification model. In this study, we adopt the Bottleneck structure, which not only reduces the number of parameters and enhances computational efficiency but also improves the model's nonlinear representation capability, making it more suitable for complex disaster classification tasks. For implementation, we leverage the make_layer function in the PyTorch 2.3.0 framework to construct residual blocks across four distinct stages, as shown in Table 1. To optimize computational efficiency, we apply a stride of 2 in the first residual block of each stage for down-sampling, while keeping the stride at 1 in the subsequent blocks. This approach effectively reduces computational complexity, expands the receptive field, and balances changes in the number of channels, ensuring that the model can efficiently learn and extract disaster-related features at multiple levels.