esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Computing for Healthcare: A Review
## 3 Applications of Quantum Computing for Healthcare
### Precision Medicine  
The domain of precision medicine focuses on providing prevention and treatment methodologies for individuals' healthcare needs [54]. Due to the complexity of the human biological system, personalized medicine will be required in the future that will go beyond standard medical treatments [54]. Classical ML has shown effectiveness in predicting the risk of future diseases using EHRs [34]. However, there are still limitations in using classical ML approaches due to quality and noise, feature size, and the complexity of relations among features. This provokes the idea of using quantum-enhanced ML, which could facilitate more accurate and granular early disease discovery [50]. Healthcare workers may then use tools to discover the impact of risks on individuals in given condition changes by continual virtual diagnosis based on continuous data streams. Drug sensitivity is an ongoing research topic at a cellular level considering genomes features of cancer cells. Ongoing research is discovering the chemical properties of drug models that could be used for predicting cancer efficiency at a granular level. Quantum-enhanced ML could expedite breakthroughs in the healthcare domain, mainly by enabling drug inference models [55; 56].  
Precision medicine has the goal of identifying and explaining relationships among causes and treatments and predicting the next course of action at an individual level. Traditional diagnosis based on the patient's reported symptoms results in umbrella diagnoses, where the related treatments tend to sometimes fail [57]. Quantum computing could help in utilizing continuous data streams using personalized interventions in predicting diseases and allowing relevant treatments. Quantum-enhanced predictive medicine optimizes and personalizes healthcare services using continuous care [58]. Patient adherence and engagement at individual-level treatments could be supported by quantum-enhanced modeling. A use case of quantum-computing-based precision medicine is illustrated in Figure 5.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 3 Proposed Statistical Dimensioning Model  
Given the density of collectors in the network, we propose a statistical method for resource planning in the uplink of cellular IoT networks. We assume the network access is an OFDMA and consider only the Narrowband Physical Uplink Shared Channel (NPUSCH) which carries the uplink user data and uplink control information. In an NB-IoT network or LTE-M, identical to the LTE network, the smallest block of radio resource elements that can be invoked is called the Resource Block (RB) or Radio Resource Block (RRB). It contains several OFDM symbols. Each RR corresponds to 180 kHz in the frequency domain and 0.5 ms in the time domain. In the 5G, different numerologies were defined to reduce the RR duration and hence the latency by increasing the RR bandwidth. In the following, the RR corresponds to the LTE resource block and the RR with numerology 0 in the 5G terminology. For the single-user and multiuser cases, our main objective here is to statistically determine the total number of required RRs to minimize the occurrence of the network outage event. When the total number of RRs required is larger than the number of RRs available on the collector side, this is considered as an outage event.
## 3 Results  
### Recruited Groups  
A total of 46 volunteers (11 females and 35 males) participated in this study, divided into a case group (\(n\) = 28) and a control group (\(n\) = 18). Summary information regarding these groups can be found in Table 1.  
No significant differences were observed in age (\(p\) = 0.12), height (\(p\) = 0.16), and gender (\(p\) = 0.29) between groups; however, the case group exhibited a greater body mass (\(p\) = 0.04). Both groups were predominantly right-limb dominant (\(p\) = 0.99). The case group was further divided into two subgroups based on the type of autograft selected for the ACLR: HT (\(n\) = 15) or PT (\(n\) = 13). The specific characteristics of the participants in each subgroup can be found in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \
\begin{tabular}{c} **Case Group** \\ \(n\) = 28 \\ \end{tabular} &
\begin{tabular}{c} **Control Group** \\ \(n\) = 18 \\ \end{tabular} & \(p\)**-Value** \\ \hline
**Gender** & & & 0.29 \\ Female & 5 (18\%) & 6 (33\%) & \\ Male & 23 (82\%) & 12 (67\%) & \\ \hline
**Age (years)** & 32.5 (18.25) & 31.50 (12.50) & 0.12 \\ \hline
**Body mass (kg)** & 82.0 (20.83) & 75.0 (21.45) & 0.04 * \\ \hline
**Height (m)** & 1.77 (0.09) & 1.71 (0.12) & 0.16 \\ \hline
**Limb dominance** & & & 0.99 \\ Right & 22 (79\%) & 15 (83\%) & \\ Left & 6 (21\%) & 3 (17\%) & \\ \hline \hline \end{tabular} Continuous variables are presented as median (interquartile range). Categorical variables are presented as frequency (percentage). An asterisk (*) denotes a significant difference (\(p\) < 0.05).  
\end{table}
Table 1: Descriptive statistics of the case and control groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \
\begin{tabular}{c} **HT** \\ \(n\) = 15** \\ \end{tabular} &
\begin{tabular}{c} **PT** \\ \(n\) = 13 \\ \end{tabular} & \(p\)**-Value** \\ \hline
**Gender** & & & 0.64 \\ Female & 2 (13\%) & 3 (23\%) & \\ Male & 13 (87\%) & 10 (77\%) & \\ \hline
**Age (years)** & 37.00 (15.00) & 29.00 (9.50) & 0.09 \\ \hline
**TAS variation** & 0.00 (\(-\)4.50) & \(-\)2.00 (\(-\)3.00) & 0.88 \\ \hline
**Body mass (kg)** & 84.30 (21.60) & 75.00 (23.60) & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the autograft subgroups of the case group.  
As can be verified in Table 2, no significant differences were observed between HT and PT autograft participants regarding age (\(p\) = 0.09), body mass (\(p\) = 0.49), height (\(p\) = 0.98), limb dominance (\(p\) = 0.65), and TAS variation (\(p\) = 0.88). A significant difference was found (\(p\) = 0.04) in the injured leg distribution, with the HT group showing more cases on the left limb (87%), while the PT group has more participants with lesions in the right limb (54%).  
The levels of physical activity, as measured by the TAS, did not exhibit a significant variation between post- and pre-surgery (post-pre) (\(p\) = 0.88). Nevertheless, the PT group demonstrated a median decrease of 2 levels (\(-\)3.00), while the median for the HT group was 0 (4.50). These findings can be observed in Table 2.
## 4 Conclusions  
A YScO\({}_{3}\) crystal fiber has been obtained through laser-heated pedestal growth (LHPG). According to X-ray spectral analysis (SEM-EDS), the amounts of Sc and Y are almost equal and unchanging across the fiber. The YScO\({}_{3}\) crystal fiber is homogeneous and belongs to the bixbyite structural type (space group \(la\overline{3}\)). The factor group analysis predicted 22 Raman-active phonon modes. However, due to partial overlap, a number of experimental lines are lower than predicted with factor-group analysis. Luminescence excitation and luminescence spectra of Tm\({}^{3+}\):YScO\({}_{3}\) and Nd\({}^{3+}\):YScO\({}_{3}\) show the presence of two active centers of the rare-earth ions in both cases. We suppose that one of rare-earth optical centers forms as a result of the local substitution of the Y\({}^{3+}\) cation by an \(RE^{3+}\) ion, and another one is a result of the local substitution of the Sc\({}^{3+}\) cation by an \(RE^{3+}\) ion in the YScO\({}_{3}\) host. Therefore, the nature of the inclusion of a rare-earth impurity into the YScO\({}_{3}\) bixbyite-type crystal structure does not depend on the ionic radius size of rare-earths.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/cryst12121745/s1](https://www.mdpi.com/article/10.3390/cryst12121745/s1)]([https://www.mdpi.com/article/10.3390/cryst12121745/s1](https://www.mdpi.com/article/10.3390/cryst12121745/s1)), Figure S1: The X-ray pattern of YScO\({}_{3}\) and Nd\({}^{3+}\):YScO\({}_{3}\) crystal fibers (black and red ones, respectively); Table S1: Miller indices (_hkl_), interplanar distances \(d\), and Bragg angles 2\(\theta\) of the pure and Nd\({}^{3+}\)-doped YScO\({}_{3}\) crystal fibers with a bixbyite-type structure; Figure S2: Micro-Raman spectra of YScO\({}_{3}\), Nd\({}^{3+}\):YScO\({}_{3}\), and Tm\({}^{3+}\):YScO\({}_{3}\) crystal fibers (black, red and blue ones, respectively).  
Conceptualization, V.T.; methodology, O.A. and S.R.; validation, E.D., O.A. and S.R.; formal analysis, E.D., O.A., V.V. ([PERSON]) and L.B.; investigation, E.D., O.A., D.G., V.V. ([PERSON]), S.R., L.B. and I.N.; resources, S.R., V.K., S.K. and V.V. ([PERSON]); writing--original draft preparation, E.D., D.G., [PERSON], S.R. and L.B.; writing--review and editing, E.D., O.A. and V.T.; visualization, E.D. and O.A.; supervision, V.T.; project administration, E.D.; funding acquisition, E.D. and O.A. All authors contributed equally to this manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation, grant number 22-22-00968.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 5 Discussion
### Feature Importance Analysis  
The FV predictive standalone ML and HML models offer valuable insights into the impact of a DF's FD and MFV characteristics on the FV. Understanding the degree to which each input variable influences the FV predictions can be provided by the Shapley additive  
Figure 11: Radar chart contrasting prediction scores achieved by standalone ML and HML models.  
Figure 12: Relationships between the percentage of added noise to the input variable distributions and R\({}^{2}\) values for FV predictions for the ML and HML models.  
explanations (SHAP) analysis, thereby providing insight into the relative importance of the FD and the MFV in this regard. Such information can assist drilling operations by providing guidance on how best to adjust the DF formulations as the well conditions evolve while drilling. Visualizing the SHAP values improves model interpretability for end-users, building confidence in applying the FV predictions to enhance drilling efficiency and wellbore integrity [26; 42]. Figure 13 displays the SHAP analysis applied to the training subset by the best-performing RBFNN-GO model.  
The mean absolute SHAP values (Figure 13b) are higher for the MFV than the FD, identifying the MFV as the most influential on the FV predictions of the two input variables (Figure 13b). The high values of the MFV variable lead to high SHAP values, which represent predominantly negative impacts on the FV predictions (Figure 13a). On the other hand, the low MFV values generate both positive and negative SHAP values. The high FD values generate predominantly positive SHAP values, whereas the low FD values (the majority of the FD distribution) generate negative SHAP values, representing positive influences on the FV predictions.
# A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection
###### Abstract  
On the high-speed connections of the Internet or computer networks, the IP (Internet Protocol) packet traffic passing through the network is extremely high, and that makes it difficult for network monitoring and attack detection applications. This paper reviews methods to find the high-occurrence-frequency elements in the data stream and applies the most efficient methods to find Hot-IPs that are high-frequency IP addresses of IP packets passing through the network. Fast finding of Hot-IPs in the IP packet stream can be effectively used in early detection of DDoS (Distributed Denial of Service) attack targets and spreading sources of network worms. Research results show that the Count-Min method gives the best overall performance for Hot-IP detection thanks to its low computational complexity, low space requirement and fast processing speed. We also propose an early detection model of DDoS attack targets based on Hot-IP finding, which can be deployed on the target network routers.  
DDoS attack detection; Count-Min; Count-Sketch; Group Testing +
Footnote †: journal: _Future Internet_**2016**, \(8\), 52; doi:10.3390/f8040052
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 1 Introduction
[14] established a back-propagation neural network, support vector machine, and XGBoost prediction model by selecting sensitive logging parameters of TOC content. Finally, they determined that the XGBoost prediction model performed best after conducting an accuracy check. [PERSON] et al. [15] predicted the TOC content of shale reservoirs based on a back-propagation neural network and a gradient-boosting decision tree algorithm using density, uranium, acoustic, and resistivity as logging parameters. Finally, they determined that the GBDT model was suitable for TOC content prediction in the study area. In summary, the applications of the above machine learning methods are mostly limited to the algorithms themselves, and there were no in-depth analyses or processing of the logging data before establishing these models. The input variables of the model should not only have a better correlation with the target value, but the covariance between the variables should also be as low as possible for optimum performance. In addition, the proper setting of the model's hyperparameters is very critical. For integrated learning algorithms based on boosting, when the number of weak learners, i.e., the number of decision trees, is small, the model's fitting ability is weak, which can easily lead to underfitting. Furthermore, if the learning rate is low, it requires a more complex iteration process and greater computational volume. A larger depth of the leaf nodes will make the model more complex, allowing it to better fit the training data, but this can also easily lead to overfitting. Therefore, it is necessary to continuously adjust the combination of parameters to achieve the best prediction performance. In this study, based on the understanding of previous researchers and the actual geological background of the study area, the correlation between the TOC content and the logging data was first quantitatively analyzed using Pearson's correlation coefficient and heat mapping to select the sensitive parameters. Then, the selected parameters were processed using principal component analysis to determine the input features. Finally, the model parameters were optimized using the particle swarm optimization algorithm. After analyzing the errors of different models, the most suitable TOC content prediction model for the study area was determined to provide an effective basis for the exploration and development of shale oil in the study area.
For ECD calculation models, the parameters that most significantly affect the precision of the results are the flow rate, fluid rheology, fluid density, wellbore diameter, and eccentricity. The aforementioned calculation models and commercial software all evolved from the classical drilling ECD calculation model framework, focusing mostly on the variations in flow rate, rheology [12; 13; 14], and density [15]; however, the wellbore diameter and eccentricity are typically calculated using fixed values throughout, which, ignoring the variations in wellbore diameter and eccentricity, would lead to discrepancies between the calculated results and field-measured values [16], as shown in Figure 1.  
Figure 1: Impact of the fluid properties and irregular diameter on pressure prediction during cementing. Machine learning methods such as random forest, the simulated annealing (SA) algorithm, and support vector regression (SVR) have been widely used in the field of drilling and cementing, mainly for predicting pressure and optimizing conventional calculation models. Some authors using a combination of the SA algorithm and SVR propose an effective monitoring method for bottom well pressure, and the model integrates hydrostatic column pressure, annulus pressure loss, and surface back pressure data, allowing for accurate monitoring without traditional Pressure While Drilling (PWD) instruments [17]. Other authors considered that traditional models often fall short in both accuracy and computational efficiency and evaluated several machine learning algorithms--Bayesian Neural Network (BNN), random forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM) to improve predictions of pressure loss in concentric and eccentric annuli. The results indicate that RF and BNN provided the best accuracy, with mean absolute errors of 2.57% and 3.2%, respectively [18]. Another researcher found the use of computational fluid dynamics (CFD) and machine learning to model pressure loss and mass transfer in membrane channels, focusing on spacer-filled membranes [19]. In response to the deficiencies in the existing research, this study focuses on a well in the eastern oilfields of China, and based on the rheological experiment of high temperature and high pressure, the irregular eccentric wellbore model was established according to the measured wellbore diameter and eccentricity data to calculate the ECD of the whole cementing process. Then, the data set was constructed and analyzed using the random forest method to quantitatively evaluate the influence of various factors such as displacement, rheology, density, and eccentricity on bottomhole and wellbore ECD, identifying the main influencing factors. The research findings contribute to the precise control of wellbore pressure during cementing operations, further ensuring the safety of cementing operations, reducing the risk of environmental pollution, and laying a technical foundation for the automation and intelligitization of subsequent cementing operations.
# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 5 Conclusions  
We developed an automated method for detecting traumatic bleeding in WBCT images using a 3D object detection model incorporating an ASPP module. The proposed method yielded improved detection performance and shortened processing time compared to our previous method. The main contributions of this study are as follows:  
* The integration of the ASPP module into the 3D object detection model enhanced detection performance by enabling the extraction of multiscale contextual information and long-range dependencies, particularly for bleeding of varying shapes and sizes.
* The proposed method achieved 70.0% sensitivity with 46.9 FPs/case and reduced processing time to 4.0 min, improving the clinical applicability of the CAD software in emergency trauma care.
* The proposed framework enables efficient data labeling with bounding boxes and can be expanded to detect other traumatic injuries, such as fractures.  
Future work will focus on further reducing FPs and expanding the detection targets to include other types of traumatic injuries.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15158123/s1](https://www.mdpi.com/article/10.3390/app15158123/s1)]([https://www.mdpi.com/article/10.3390/app15158123/s1](https://www.mdpi.com/article/10.3390/app15158123/s1)), Table S1: Specifications of the trauma CT dataset.  
Conceptualization, T.-a.N., T.N. and Y.N.; Methodology, R.N., T.N. and Y.N.; Software, R.N. and Y.N.; Validation, R.N. and Y.N.; Formal Analysis, R.N., T.N. and Y.N.; Investigation, R.N., T.N. and Y.N.; Resources, A.B., T.-a.N. and Y.N.; Data Curation, A.B., T.-a.N. and Y.N.; Writing--Original Draft Preparation, R.N.; Writing--Review and Editing, A.B., T.-a.N., T.N. and Y.N.; Visualization, R.N. and Y.N.; Supervision, Y.N.; Project Administration, Y.N.; Funding Acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by JST SPRING, Grant Number JPMJSP2109.  
All procedures involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki Declaration, as revised in 2008(5). The requirement for formal consent was waived, considering the nature of the study. This study was approved by the Institutional Review Board of Chiba University Graduate School of Medicine (approval number: 2972; approval date: 27 February 2018).  
Informed Consent Statement: Informed consent was waived by the Institutional Review Board owing to the retrospective study design.  
Owing to the ethical restrictions imposed by the Research Ethics Committee, the bleeding detection dataset cannot be made publicly available. The original data from the TotalSegmentator dataset [23] are available at [[https://zenodo.org/records/10047292](https://zenodo.org/records/10047292)]([https://zenodo.org/records/10047292](https://zenodo.org/records/10047292)), accessed on 17 July 2025.  
The authors declare no conflicts of interest.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 6 Future Trends
### Generative Adversarial Networks (GANs)  
Generative Adversarial Networks (GANs) is a novel class of deep generative models in which training is a minimax zero-sum game between two networks: a Generator (G) and a Discriminator(D) [136]. These networks compete in a unified training process where the generator uses its neural network to produce samples and the discriminator tries to classify these samples as real or fake [137]. The game is played until Nash equilibrium using a gradient-based optimization technique (Simultaneous Gradient Descent), i.e., G can generate images like sampled from the true distribution, and D cannot differentiate between the two sets of images [136]. GANs has gained a lot of attention recently for different applications and seem to be a potential solution to various challenges. For example, the authors in [138] employ a GAN approach to pre-train a deep-RL framework to provide resource allocation for ultra reliable low latency communication (URLLC) in the downlink of a 6G wireless network, with results showing near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements.  
Furthermore, the authors in [139] propose GAN based joint trajectory and power optimization (GAN-JTP) algorithm for a UAV trajectory prediction and power optimization, with results being close to optimal with high convergence speed. In the context of a complex 6G network system, the development of GANs seems crucial for the upcoming challenges.
## 2 Materials and Methods
### Application Studies: River Samples  
To evaluate the performance of the method in real water matrices, samples from two rivers in Catalonia, namely the Llemena River (conductivity 603 \(\upmu\)S cm\({}^{-1}\), pH 8.49, total organic carbon (TOC) 1.57 mg C L\({}^{-1}\)) and Osor River (conductivity 247 \(\upmu\)S cm\({}^{-1}\), pH 8.58, TOC 2.03 mg C L\({}^{-1}\)). The samples were stored in a deep freezer at \(-18\)\({}^{\circ}\)C until use. Before testing, the samples were spiked with 0.20 mg L\({}^{-1}\) for CPS and AHTN and 1.26 mg L\({}^{-1}\) for TCS.  
The results obtained after the application of the TFME method were analysed (i) by plotting peak areas from the Osor and Llemena rivers and comparing them with the peak areas obtained with an 0.01 M NaCl matrix used throughout the work and (ii) by calculating absolute recoveries (AR) following the guideline recommendations [42].