esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The accurate prediction of the compression index (\(C_{c}\)) is crucial for understanding the settlement behavior of clayey soils, which is a key factor in geotechnical design. Traditional empirical models, while widely used, often fail to generalize across diverse soil conditions due to their reliance on simplified assumptions and regional dependencies. This study proposed a novel hybrid method combining Genetic Programming (GP) and XGBoost methods. A large database (including 385 datasets) of geotechnical properties, including the liquid limit (LL), the plasticity index (PI), the initial void ratio (\(e_{0}\)), and the water content (\(w\)), was used. The hybrid GP-XGBoost model achieved remarkable predictive performance, with an \(R^{2}\) of 0.966 and 0.927 and mean squared error (MSE) values of 0.001 and 0.001 for training and testing datasets, respectively. The mean absolute error (MAE) was also exceptionally low at 0.030 for training and 0.028 for testing datasets. Comparative analysis showed that the hybrid model outperformed the standalone GP (\(R^{2}\) = 0.934, MSE = 0.003) and XGBoost (\(R^{2}\) = 0.939, MSE = 0.002) models, as well as traditional empirical methods such as Terzaghi and Peck (\(R^{2}\) = 0.149, MSE = 0.090). Key findings highlighted that the initial void ratio and water content are the most influential predictors of \(C_{c}\), with feature importance scores of 0.55 and 0.27, respectively. The novelty of the proposed method lies in its ability to combine the interpretability of GP with the computational efficiency of XGBoost and results in a robust and adaptable predictive tool. This hybrid approach has the potential to advance geotechnical engineering practices by providing accurate and interpretable models for diverse soil profiles and complex site conditions.  
compression index; clayey soils; genetic programming; XGBoost; hybrid machine learning; soil compressibility; predictive modeling; soil settlement +
Footnote †: journal: Applied sciences  
Article
## 5 Data Fusion
### Multi-Block Regression and Classification
Analogously to MB-PLS, SO-PLS has already been broadly employed in food science (see Table 3), also coupled to LDA for tackling classification tasks [199]; it has been used (i) to correlate sensory perception and chemical composition descriptors [200], (ii) for the quantification of particular compounds in dietary supplements [201], (iii) for the authentication of spirits (i.e., Italian grappa--Italian grape marc spirit) by MIR and NIR spectroscopy [202], and (iv) for the determination of the geographical origin of foodstuff (e.g., red garlic, semolina and saffron) by multiple spectroscopic modalities (UV-Vis, NIR, MIR and ATR-FT-IR) [203; 204; 205]. Furthermore, the robustness and versatility of SO-PLS has recently led to the design of hybrid techniques conceived for very disparate purposes: Sequential Multi-Block PLS (SMB-PLS [206]), merging features of both MB-PLS and SO-PLS; Sequential and Orthogonalized-N-PLS (SO-N-PLS [207]), for the multi-block analysis of multi-way structures; Sequential Preprocessing through ORThogonalization (SPORT [208]), for the pretreatment of the SO-PLS predictor blocks; Sequential and Orthogonalized Covariance Selection (SO-CovSel [209]), for the low-level DF of preselected variables; and all their discriminant extensions. Yet, one of the main drawbacks associated with SO-PLS regards the fact that a deep and complete inspection of the factors shared by the regressor matrices cannot be easily carried out. In similar scenarios, P-ComDim, the predictive version of CCSWA, constitutes the designated approach [210; 211; 212]. The algorithmic procedures behind P-ComDim and CCSWA are strictly resemblant with the difference that, in the former, for each regressor block, the cross-product matrices \(\mathbf{X}_{i}\mathbf{X}_{i}^{\text{T}}\) are replaced by the core matrices \(\mathbf{X}_{i}\mathbf{X}_{i}^{\text{T}}\mathbf{Y}\mathbf{Y}^{\text{T}}\). Common components (together with their salience values) are iteratively retrieved as in ComDim, but, here, also \(\mathbf{Y}\)-scores are computed. Additionally, P-ComDim has been rather often applied for tackling complex food authentication problems, not only due to its generally high prediction accuracy, but also because it may open wide horizons for the identification of commonalities underlying regressors proceeding from very diverse sources.
## 3 Results
### Attention Span
#### 3.1.2 SAT Assessment Test Results
In conclusion, SAT results showed a significant improvement in attention span. The difficulty factor and pressure factor had a significant effect and an interaction effect. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \multicolumn{2}{c}{**95\% C.I.**} \\ \hline
**Difficulty** & **Pressure** & **Pre/Post-Test** & **Mean** & \begin{tabular}{c} **Std.** \\ **Error** \\ \end{tabular} & \
\begin{tabular}{c} **Lower** \\ **Bound** \\ \end{tabular} &
\begin{tabular}{c} **Upper** \\ **Bound** \\ \end{tabular} \\ \hline \multirow{3}{*}{with} & \multirow{3}{*}{with} & pre-test & 8.70 & 0.26 & 8.17 & 9.23 \\  & & post-test & 10.60 & 0.19 & 10.21 & 10.99 \\ \cline{2-6}  & \multirow{3}{*}{without} & pre-test & 8.60 & 0.26 & 8.07 & 9.13 \\  & & post-test & 9.80 & 0.19 & 9.41 & 10.19 \\ \hline \multirow{3}{*}{without} & \multirow{3}{*}{with} & pre-test & 8.20 & 0.26 & 7.67 & 8.73 \\  & & post-test & 9.90 & 0.19 & 9.51 & 10.29 \\ \cline{2-6}  & \multirow{3}{*}{without} & pre-test & 8.60 & 0.26 & 8.07 & 9.13 \\ \cline{1-1}  & & post-test & 8.40 & 0.19 & 8.01 & 8.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Estimated marginal means of SAT- Interaction of difficulty * pressure * pre/post-test. Figure 8: The graph of two-factors interaction in SAT. Figure (**A**) shows the interaction of pre/post-test * difficulty; Figure (**B**) shows the interaction of pre/post-test * pressure. Note: pre/post-test (the horizontal axis), pre-test = 1, and post-test = 2.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Computed Tomography (CT)
#### 4.2.1 DL Techniques and Applications to CT  
CNNs have been widely used in CT analysis tasks. For example, Mask R-CNN [141] has been used for lung nodule detection in CT images [142]. CNNs have also been used for CT image registration [143] and image segmentation [144]. Moreover, DL techniques have been applied to CT angiography for vessel segmentation and centerline extraction [145]. In addition to CNNs, GANs have also been used in CT applications, such as image denoising [146] and image synthesis [147]. For example, GANs have been used for synthesizing low-dose CT images from high-dose CT images [148]. Figure 10 indicates CT image classification using DL techniques.  
Figure 9: Example of MRI brain image segmentation using DL techniques.
## 4 Localized Query Loss Discovery with Distributed Query Resolution  
We propose a localized query loss discovery to efficiently detect query loss problems in sensor networks. Before introducing the details of the proposed approach, we at first describe the basic idea of the proposed approach. Each sensor node in the network is provided with collective loss detectors that are composed of a cluster of local neighbor nodes. The cluster is termed _detector cluster_ and the sensor node being monitored in the cluster center is termed the _object node_. As shown in Figure 2, each sensor node is an object node, around which clusters of detectors are assigned to collectively detect the query loss at the object sensor node.  
By employing a distributed query resolution mechanism at detector nodes, a detector is able to have a localized and selective response to each query that are correlated to the objects that the detector is monitoring. When a detector finds that a query is correlated with one of the objects that it is monitoring, it starts to monitor the query loss by utilizing wireless overhearing of data reply messages from the object. A detector will judge that there is a query loss at the object when there is no data reply overheard, since the object that is correlated to a query should reply sensing data to the query. Note there are multiple detectors for an object to detect the query losses. The success rate of loss discovery is expected to highly increase since there are collective discoveries of query losses by the multiple detectors that have the spatial-temporal diversity in regard to wireless communication.  
The ideas in the proposed approach take some insights from the immune system (IS), which adopts the teamwork of immune cells to efficiently detect and eliminate pathogens [16; 17; 18]. The natural immune system is a complex adaptive system that protects the body (organism) from invading pathogens such as viruses or bacteria, _etc_. An immune system consists of two major procedures: _pathogen detection_ and _pathogen elimination_. All these are performed in a distributed and localized manner. Pathogen detection operates by distinguishing \"self\" and \"non-self\" cells in the immune system. When a pathogen is detected, the corresponding immune cells are activated to eliminate the pathogen. Pathogen elimination in the adaptive immune response copes with the problem of choosing the right receptors for the particular  
Figure 2: Distributed detector clusters in sensor networks.  
kind of pathogen to be eliminated [16]. For example, the activated B-cell will produce antibodies that have a Y-shaped structure, and can collectively bind to the pathogens.
# Uncertainty Analysis of Multisource Land Cover Products in China
###### Abstract  
Satellite-based land cover products play a crucial role in sustainability. There are several types of land cover products, such as qualitative products with discrete classes, semiquantitative products with several classes at a predetermined ratio, and quantitative products with land cover fractions. The proportions of land cover types in the grids with coarse resolution should be considered when used at the regional scale (e.g., modeling and remote sensing inversion). However, uncertainty, which varies with spatial distribution and resolution, needs to be studied further. This study used MCD12, ESA CCI, and MEASURES VCF land cover data as indicators of qualitative, semiquantitative, and quantitative products, respectively, to explore the uncertainty of multisource land cover data. The methods of maximum area aggregation, deviation analysis, and least squares regression were used to investigate spatiotemporal changes in forests and nontree vegetation at diverse pixel resolutions across China. The results showed that the average difference in forest coverage for the three products was 8%, and the average deviation was 11.2%. For forest cover, the VCF and ESA CCI exhibited high consistency. For nontree vegetation, the ESA CCI and MODIS exhibited the lowest differences. The overall uncertainty in the temporal and spatial changes of the three products was relatively small, but there were significant differences in local areas (e.g., southeastern hills). Notably, as the spatial resolution decreased, the three products' uncertainty decreased, and the resolution of 0.1\({}^{\circ}\) was the inflection point of consistency.  
land cover; uncertainty; ESA CCI products; MCD12 products; MEaSURES VCF products 2021  
1  
[PERSON]  
1 College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China; [EMAIL_ADDRESS]  
2  
[PERSON]  
1 College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China; [EMAIL_ADDRESS]
## 3 Results and Discussion
### Selection of the BA\(\mu\)E Coating Phase  
One of the great advantages of the BA\(\mu\)E technique is that we can easily choose the most convenient sorbent phase, according to the best selectivity for the analytes under study. Therefore, two polymers (HLB and PS-DVB) and two activated carbons (AC1 and AC2) were evaluated, and the results are depicted in Figure 1. From the data obtained, PS-DVB presented the best extraction efficiency among all sorbents tested. The polymers used are reversed-phase-type, retaining the analytes according to the particle size, surface area, and mechanisms involved. Thus, the presence of aromatic ringsboth in their network and in the molecules involved seems to favour \(\pi\)-\(\pi\) and dipole-dipole-type interactions between them. Furthermore, the PS-DVB coating phase presented a better extraction efficiency, probably due to its higher surface area (1200 m\({}^{2}\) g\({}^{-1}\)) when compared with the HLB polymer (810 m\({}^{2}\) g\({}^{-1}\)). On the other hand, the activated carbons are porous solid materials that retain the solutes through electrostatic and/or dispersive interactions, according to the textural adsorption properties, as well as surface area and pore dimensions involved. Moreover, the interactions between the analytes and the sorbent are strongly influenced by the pH of the bulk solution. When the matrix pH is equal to the pHr2C, the activated carbon surface becomes neutral, with equal number of positive and negative charges. If the value is lower than the pHr2C, the activated carbon surface becomes positive and, if higher than the pHr2C, turns into negative. At pH 2.0, the sulfonated groups of the analytes are deprotonated (pK\({}_{\text{a}}\) < 1), wherein AC1 and AC2 are neutral and positively charged, respectively, in which electrostatic interactions may occur between both analytes and AC2. However, the attractive forces are probably, in some cases, so strong that both analytes are still retained in the activated carbon network, even after the back-extraction stage under ultrasonic treatment, resulting in lower extraction efficiency.
## 2 Related Work
### Task Scheduling Algorithms in Other Distributed Computing Systems  
Because effective task allocation algorithm can improve the performance of distributed systems, many researchers have done a lot of works in this area. These task scheduling algorithms can be roughly divided into two categories, static scheduling algorithms and dynamic scheduling algorithms.  
Generally speaking, static algorithms use directed acyclic graphs (DAG) [14] to represent task priorities in task scheduling. [PERSON] et al. [15] proposed two static task scheduling algorithms for heterogeneous distributed computing, which are called earliest-finish-time (HEFT) algorithm and the critical-path-on-a-processor (CPOP) algorithm. The HEFT algorithm chooses tasks with the highest priority at each step to allocate tasks and the CPOP algorithm calculates task priority according to DAG. The ultimate goal of the HEFT is to minimize the completion time of all tasks. The Min-min algorithm [16] extends the HEFT algorithm and uses sophisticated heuristics at each level to reduce the probability of catching local minima. [PERSON] et al. [17] proposed a robust task scheduling algorithm based on deadline and budget constraints for cloud computing, which can minimize the total completion time and the cost under deadline constraint. However, the key issue of these prior studies is that they didn't consider the dynamic of distributed computing. Because of the dynamic characteristics of volunteer computing platforms, the static scheduling algorithms can't be fully applied to such platforms, and cannot make full use of the computing resources of the system.  
To improve the utilization rate of system resources, researchers have proposed many dynamic scheduling algorithms. [PERSON] et al. [18] proposed a dynamic load-balancing algorithm to improve the utilization of computing resources; the experimental results show that their algorithm can achieve a near-optimal task allocation. On this basis, [PERSON] et al. [19] proposed an improved dynamic scheduling algorithm which operates in a batch fashion and uses a genetic algorithm to minimize the total completion time. In addition, the greedy algorithm [20] is used to task allocation. However, these algorithms did not take the deadline into account, so it is necessary to design a heterogeneous dynamic scheduling algorithm with the deadline.  
The MOC algorithm [13] was proposed to solve the dynamic scheduling problem with deadline constraint in heterogeneous distributed computing, as mentioned before. However, the objective of MOC is that each task must be completed before its deadline, which is different from our work: To complete as many tasks as possible within their respective deadline constraints. Moreover, there are some dynamic task assignment methods with deadline constraints for special application scenarios [21; 22; 23]. Since application scenarios differ from volunteer computing, these algorithms can't be applied to volunteer computing.
## 2 Fundamental Concepts and Background
### Trade-off between Interpretability and Performance  
There is often a perceived trade-off between the performance (predictive accuracy) of a model and explainability [3, 18, 19]. The algorithms that currently often perform the best (e.g., deep learning) are the least explainable, creating a demand for explainable models which can achieve high performance [18]. Simple models are often preferred for their ease of interpretation despite a general trade-off between model performance and explainability that is often assumed [68, 69]. However, linear models, for example, are not strictly more interpretable than, for example, neural networks, especially when high-dimensional or heavily engineered features are used. In these cases, the interpretability or the explainability of the model can be lost [46]. Likewise, more complex models may not be more accurate.  
One could argue that it would not be ethical to apply in clinical practice a model that does not have the best possible performance, as the ultimate goal is to provide the best possible assistance to patients [92]. [PERSON] et al. [93] provided an example comparing advanced laboratory testing and AI-based CDSS, which are similar in terms of the fact that they support clinical decisions and that accuracy is important. In the case of the first, there is some general understanding on behalf of clinicians but not for each result. Some level of understanding for AI-based CDSS is also possible in terms of \"the agent view of AI, i.e., what it takes as input, what it does with the environment, and what it produces as output, and (2) explaining the training of the mapping which produces the output by letting it learn from examples, which encompasses unsupervised, supervised, and reinforcement learning\" [93] and might suffice for certain scenarios. The authors also consider the fact that the first requirement of AI systems in medicine is clinical validation, while explainability is a second aspect. Medical certification comes after the system is compliant with regulatory standards and prediction accuracy is usually the main measurement of clinical validation. However, as perfect performance is not possible, while from a patient perspective there is more trust towards clinicians and less tolerance for \"machine\" error, explainability is required, making this a difficult dilemma for developers. With the availability of larger datasets there are increasing benefits of using more complex models which allow for more complex functions to be approximated [18, 46, 79] and future developments in XAI may allow for an optimal balance between the explainability and performance of more complex models.
## 3 Review Results
### Application in Benign Conditions  
SRT can be used in a range of non-malignant neurological conditions, and therefore, reducing the dose to nearby eloquent WM tracts to the pathology would likely reduce the risk of complications. DTI integration is most widely explored in the benign condition, arteriovenous malformation (AVM). A control study included 24 AVM patients, which used tractography in SRT treatment planning and 28 subjects with AVM without using tractography [22]. The subjects were followed up for 4 years, and the study found there were no significant differences in the success of treatment on angiography and that the use of tractography to model the CST significantly reduced the risk of motor complications. A limitation of this study is that it was retrospective with the control group being subjects from before the institution introduced tractography into SRT treatment planning. Therefore, the groups were not randomised, and the improved outcomes may be due to other factors such as improved operator skill over time. The same institution released a review of all patients they treated for AVM since the routine implementation of tractography with SRT (from 2004 to 2009), which included 144 patients. They found that 46% of cases utilised tractography, showing that it can be introduced effectively to a clinical setting, and a large proportion of patients may benefit from its integration. However, the use of tractography integration was operator dependent and may have not been necessary in some of the patients it was used in--or necessary in some of the cases when not integrated. Although it was found that in 60% of cases utilising tractography, the tract was within 5 mm [31].  
DTI-integrated SRT has been explored in a small number of other neurological conditions, which include meningiomas and vestibular schwannomas. These studies include cohorts with multiple conditions and find that integrating tractography reduces the maximum radiation dose to the eloquent tracts [20, 23]. However, the evidence for tractography implementation in the individual conditions is limited.
### Urban IoT Architectures and OUP  
Open urban platforms often exist in certain integrated networks and architectures. These architectures usually differ within their structure. Therefore, an explanation of the used architecture and its construction--as depicted in Figure 1--is necessary:  
* _Data sources and Actors_: IoT devices and sensors are utilized for collecting and analyzing data. The collected and analyzed data are transmitted to explicit gateways via, e.g., LoRaWAN or NB-IoT networks;
* _IoT Platform and Connectivity:_ From the gateways, the data are forwarded across the network through different communication channels (e.g., mobile network cells) to IoTplatforms. These platforms support the management of the IoT devices through their complete operational life cycle and are usually operated by the IoT device vendors;
* _Urban Data Platform (UDP) (Smart City Core Services)_: Connectors receive the data from the IoT-platform, normalize them, and usually enrich them by data stored in a UDP database. For example, a connector can receive a message with an IoT device ID and a date--in this case, the message on the output of the connector is extended with the geo-location of the IoT-device. In some cases, the IoT devices are connected directly to the UDP and are managed by an IoT module on the UDP. From the connectors the data are sent via a message bus to the storage and to a _Complex Event Processing_ engine (CEP), which applies rules on the events and produces new messages. By doing this, the CEP can be considered as a virtual sensor and the new produced messages are stored in the _Storage_ as well. The _Analytics_ module combines analytic services and libraries. The analytic services are often machine learning/AI-based services, e.g., for predictions;
* _APP/APIs_: The UDP is connected to many APPs or provides outbound APIs. _Dashboards_ or _Cockpits_ are the most used APPs and provide information to users. Data laboratories use analytic services of the UDP and enable experts to perform sophisticated analysis. Marketplaces are used to provide data. Other data are provided by _Open APIs_ on the outbound layer of the UDP/OUP.