esa-sceva/satcom-chunk-collection huggingface dataset:
# Elite Multi-Criteria Decision Making--Pareto Front Optimization in Multi-Objective Optimization
## 2 Literature Review
### Pareto Front  
A single solution from the given set of solutions is considered Pareto optimal only if there exists no other feasible solution that improves any objective without worsening one or more objectives. The Pareto dominance can be explained using Figure 1.  
In Figure 1, all of the points represent the feasible solutions obtained for the objective functions 1 and 2. Considering points A, B, and C and with the desire to minimize both objective functions, the following conclusions can be made:  
* Point A dominates point B in objective 1 and C in both the objectives.
* Point B dominates point A in objective 2 and C in both the objectives.
* Point C is dominated by both A and B points in both the objectives.  
Here, points A and B are not dominating each other, and there are no other solutions dominating them. Hence, points A and B are in the same Pareto front. Point C is dominated by both solutions, thus pushing it to the next Pareto front.  
To list all of the individuals into various Pareto fronts, first, all of the non-dominated solutions \(PF_{1}\) are found from the entire solution set \(X\). Next, the leftover solutions \(X-PF_{1}\) are considered to find \(PF_{2}\) non-dominated solutions. This process is repeated until all the solutions are assigned a Pareto front number [20].
## 3 Results
### PTEN is a Novel SMN-Interacting Partner
These results suggest that the interaction of PTEN with the C-terminus of SMN comprises at least the residues 239-278. As mentioned above, loss of _SMN1_ is the main cause of SMA. Nevertheless, there are a few patients harboring at least one allele of a non-deleted _SMN1_ that carries missense mutations [60]. We tested two known patient mutations within the C-terminus (hSMN Y272C and T274I) in co-immunoprecipitation  
Figure 4: Endogenous phosphatase and tensin homolog (PTEN) binds to the C-terminus of SMN and dephosphorylates SMN. (**A**) Co-immunoprecipitation of endogenous p-PTEN and SMN from lysates of differentiated NSC34 cells. Beads alone, IgG and tubulin were used as negative controls. HC represents heavy chain. (**B**) Confocal images of NSC34 cells were stained for endogenous SMN and PTEN together with their respective PDM images. Scale bar represents 20 \(\upmu\)m. (**C**) Schematic overview of hSMN constructs used in immunoprecipitation assays. Cells were transfected and prepared for the assay 18 h post-transfection. (**D–F**) Co-immunoprecipitation assays with hSMN deletion constructs (**D**), patient mutations (**E**) and SMNA7 (**F**) 18 h post-transfection. (**G,H**) Isoelectric focusing of SMN from NSC34 cells treated with either DMSO, PTEN inhibitor bpV(HOpic) or shrimp alkaline phosphatase (SAP) for 2 h prior to lysis. Spot #7 (indicated by arrow) was used as reference (mean \(\pm\) SEM, \(n=3\), paired two-tailed t-test, \({}^{*}p<0.05\)). assays with regard to PTEN binding (Figure 4E). The hSMN Y272C and hSMN T274I mutations, which localize to the highly conserved YG-box of SMN (corresponding to the hSMN 235-294 construct) and inhibit SMN self-oligomerization [46], did not alter the interaction with PTEN. Additionally, we tested the phospho-mutant and phospho-mimetics hSMN S290A/D in binding assays. We found that hSMN S290A was strongly bound to PTEN whereas hSMN S290D was hardly precipitated (Figure 4E). Taken together, these results show that PTEN binding to hSMN is not altered by the selected patient mutations Y272C and T274I but that mimicking phosphorylation of hSMN S290 influences PTEN-binding.
## 6 Results and Discussions
### Simulation of the Monitoring and Control of Parking Lots in the North of a City
#### 6.2.4 Selecting the Level of Service in a Parking Lot System
Context: Simulation of the Service Operation Time of the Parking Lots in the North of a City  
In this paper, we carried out a mapping of the city of Madrid, which was subdivided into four zones as shown in Figure 25. Each zone of the parking lot system was modeled and simulated with the number of parking lots required. We defined this scenario to show how to scale the simulation for various areas of a city. System simulations for an entire city could become a problem of resource performance when trying to handle large XML files. For this reason, due to the limitations of the resources of the local computer, only the simulation of the scaling of the parking lot service in the north of Madrid was performed. This simulation was performed using two models representing the northwest and northeast areas (see Figure 26). It should be noted that it is possible to simulate the four areas of Madrid using two additional parameterized models that represent the southern area of Madrid on another computer with similar characteristics. In the context of this scenario, the XML files (simulation-northwest.xml and simulation-northeast.xml) were parameterized and dimensioned with 15 parking lots and 9 parking lots respectively. The sensors/actuators of each model send the MQTT messages to the cloud platform at a frequency that depends on the operation of the M/M/c/N queue systems replicated in the models. For simplicity, we parameterized each of the parking lots with \(\lambda\) = 20 cars/h, \(\mu\) = 0.333 cars/h, and queue capacity = 10 cars. Additionally, individually each parking lot was dimensioned with a specific number of parking spaces based on the current amount existing in the city of Madrid. The details are presented in Table 14. Each of the iterations of the experiments simulates a certain number of hours of service operation. The total of MQTT messages generated in each zone and the execution time used by each of the simulators to complete the operations are provided. Figure 25. Modular and hierarchical distribution to model an IoT parking system for the city of Madrid. Figure 27 shows the total number of messages sent by simulator-1 and simulator-2 based on the number of simulated hours of service operation. We can see that:  
(a) Simulator-1 sent 595.07 messages per hour (0.165 messages per second)  
(b) Simulator-2 sent 298.97 messages per hour (0.083 messages per second). In general, given:  
\[\text{Y}_{\text{simulator-1}}=595.07\times\text{x}+1449.8, \tag{1}\]  
\[\text{Y}_{\text{simulator-2}}=298.97\times\text{x}+149.6, \tag{2}\]
## 2 Materials and Methods
### WEave Blockchain Framework Overview (Original Design)
#### 2.1.2 Smart Contracts and Data Model  
WEave's original business logic was implemented via Fabric chaincode on each channel. We developed chaincodes in Go, encoding different asset types and transactions. For the production channel, asset types included CottonBale, CottonYarn, Lot (CottonYarn), Lot (FinishedFabric), and so forth--modeling the transformation of materials through the supply chain. Transactions allowed authorized parties to create, update, or transfer these assets. For example, RM could invoke a CreateCottonBale function (allowed only for RM's role) to record a new cotton bale with attributes like weight, grade, etc. TX could then invoke CreateUnfinishedFabric by consuming certain cotton bale/yarn lot IDs as inputs (representing spinning/weaving), with the chaincode enforcing that only a TX organization can perform that and that the referenced input lots exist and belong to TX. Similar logic propagated through FS, creating finished goods from fabric lots, and BA/RE performing transfer or acceptance transactions. Throughout these updates, ownership and custody of each asset were tracked as state on the ledger (e.g., an asset's Owner field updates when it is transferred).  
On the admin channel, the chaincode handled purchase orders and compliance documents. For instance, a CreateOrder transaction could be issued by RE to BA with item quantities, triggering an event that then allows BA to orchestrate production on the PC (the simulation abstracted some of this for simplicity). The admin channel and production  
Figure 1: Hyperledger Fabric’s execute–order–validate transaction architecture.  
channel data could be linked via common identifiers (like a PO number field on production events for cross-reference), but were kept logically separate in the ledger.
## 3 Multi-Energy Coordination and Optimization Dispatching Strategy of Urban Power Grid
### Robust Dispatching Method of Renewable Energy  
By traditional scheduling method, renewable energy for the power grid is equivalent to \"negative load\", which means active power consumption is negative. Its value depends on the day-ahead renewable energy output prediction [35]. The deviation of renewable energy output prediction is balanced by the AGC (Automatic Generation Control) unit in the system. When the proportion of renewable energy is high, the AGC unit regulation capacity is not enough to balance the prediction error caused by the uncertainty of renewable energy. This will lead to a large amount of wind/light curtailment and load curtailment. Therefore, the risk cost of this scheduling method is high.  
In order to reduce wind/light curtailment and load curtailment caused by the volatility of renewable energy and reduce the risk cost of the system, the concept of uncertainty set of renewable energy unit output is introduced. The details are as follows:  
\[(1-\beta)p^{f}_{r,t}\leq p_{r,t}\leq(1+\beta)p^{f}_{r,t} \tag{17}\]  
where \(p^{f}_{r,t}\) is the prediction of the output of renewable energy unit at time period \(t\). \(\beta\) is a coefficient between 0 and 1, used to represent the size of the uncertainty set. \(p_{r,t}\) is the actual output of renewable energy units at time period \(t\). When it is within the range demonstrated in the above equation, it can be considered that the output of renewable energy units is concentrated in the uncertainty of the system.  
For the output of renewable energy units located in the uncertainty set, the system can realize the full absorption of renewable energy through the preset rotary reserve. When the volatility of the renewable energy units is large and located outside of the uncertainty set of the system, wind/light curtailment or load curtailment may occur. Therefore, when the range of uncertainty set is large, the power grid has strong absorption capacity for renewable energy and low risk cost. However, the power grid should reserve a large amount of spare capacity, and a large number of generator units cannot be operated under the optimal output, resulting in low power generation efficiency and high operation cost. On the contrary, when the range of uncertainty set is small, the system has high risk cost but low operation cost. To calculate the best range of uncertainty set, an effective method is to calculate the sum of risk cost and operation cost of the system with a different value of \(\beta\) and choose the uncertainty set with minimum overall cost as the optimal uncertainty set. The calculation method of risk cost and operation cost is shown in [28].
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 3 Machine Learning Techniques
### Histogram-Based Gradient Boosting  
Histogram-based Gradient Boosting (HGB) is a gradient boosting framework that is specifically designed to handle continuous numerical features with high cardinality. It is based on decision tree algorithms, but uses histograms to discretize numerical features, which greatly reduces the number of splitting points needed during training. This enables HGB to handle datasets with millions of features and samples while still achieving high accuracy. HGB also uses a novel algorithm for gradient computation, which reduces the computational cost of gradient boosting and improves its scalability. In addition, HGB provides several advanced features such as early stopping, cross-validation, and regularization to further improve model performance.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 4 The Node Placement Algorithm  
When a node detects a problem, it sends a Problem Notification Message (PNM) to the sink. This message contains all the information needed so that the sink can act TO mitigate the problem that appeared in the network. This information includes: its NodeID,  
Figure 1: Block Diagram of the Framework.  
its location and communication range, the number of packets received and forwarded per sample time period, and some neighbor table information. From the neighbor table, the following information is included: neighbor node's NodeID, hop number, number of packets received, and availability flag.  
When the sink node receives the PNM message, it calculates the position for mitigating the problem. This position needs to be a \"clever\" placement for a mobile node in order to provide alternative paths to the sink and help in alleviating the area from its problem. After the calculation of the position, the sink node sends a Moving Notification Message (MNM) to the mobile node that is selected to assist in the problem. The MNM message includes information about the target location, the sender node's NodeID, and its next-hop NodeID.  
When a mobile node receives an MNM message, it switches off its radio while moving towards the target location and switches its radio back on at its new position. The use of the ON/OFF tactic is so that the mobile nodes are not detectable from the static nodes in the network while they travel towards their target locations. Finally, when the mobile node reaches its destination, it establishes a connection with its target nodes to be served.  
In this section we present the Node Placement Algorithms (NPA) that consists of two variations. These algorithms are described below and can be used for determining the number and position of mobile nodes.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 3 Results
### Validation of the CAAW Method
It receives sunlight for a long time, so the temperature in this area is the highest in the whole basin. The results show that the method based on classification and adaptive window is able to recover invalid and abnormal pixels affected by clouds over a large area, and generate an LST spatial distribution map. Because the input data are continuous, this method is able to provide a continuous spatiotemporal graph. A few regions have different spatial distributions due to the use of different methods, but overall, LSTs reconstructed using different methods still have similar spatial distribution characteristics. However, due to the low spatial resolution of MODIS data, the details of the image cannot be easily distinguished. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multirow{2}{*}{**Data**} & \multicolumn{4}{c}{**Statistics**} \\ \cline{3-6}  & & \(\mathbf{R^{2}}\) & **RMSE** & **NSE** & **PBIAS** \\ \hline \multirow{2}{*}{Arou} & ERA5 & 0.57 & 8.54 & 0.39 & 1.35\% \\  & AMSR2 & 0.57 & 8.56 & 0.38 & 1.35\% \\ \hline \multirow{2}{*}{Daman} & ERA5 & 0.80 & 5.75 & 0.78 & 0.28\% \\  & AMSR2 & 0.80 & 5.75 & 0.78 & 0.28\% \\ \hline \multirow{2}{*}{Dashalong} & ERA5 & 0.58 & 7.69 & 0.51 & 0.30\% \\  & AMSR2 & 0.58 & 7.69 & 0.51 & 0.31\% \\ \hline \multirow{2}{*}{Huazhaizi} & ERA5 & 0.76 & 7.40 & 0.70 & \(-\)0.53\% \\  & AMSR2 & 0.76 & 7.4 & 0.71 & \(-\)0.84\% \\ \hline \multirow{2}{*}{Hunhelin} & ERA5 & 0.84 & 7.26 & 0.81 & \(-\)0.87\% \\  & AMSR2 & 0.84 & 7.24 & 0.81 & \(-\)0.86\% \\ \hline \multirow{2}{*}{Sidaoqiao} & ERA5 & 0.89 & 5.35 & 0.88 & \(-\)0.37\% \\  & AMSR2 & 0.90 & 5.27 & 0.88 & \(-\)0.36\% \\ \hline \multirow{2}{*}{Yakou} & ERA5 & 0.62 & 7.62 & 0.51 & \(-\)0.89\% \\  & AMSR2 & 0.62 & 7.57 & 0.51 & \(-\)0.88\% \\ \hline \multirow{2}{*}{Zhangye} & ERA5 & 0.86 & 5.48 & 0.78 & \(-\)0.91\% \\  & AMSR2 & 0.86 & 5.47 & 0.78 & \(-\)0.91\% \\ \hline \multirow{2}{*}{Overall} & ERA5 & 0.79 & 6.96 & 0.77 & \(-\)0.20\% \\  & AMSR2 & 0.79 & 6.95 & 0.77 & \(-\)0.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation of reconstructed LST using classification and adaptive window from 2016 to 2019.
## 4 Results and Analysis
### Subsidence of Tunnel Side Wall  
The subsidence markers at the side walls of both tunnels have all subsided; the accumulative settlement increased from the southeast to the northwest (Figure 10), which is consistent with the subsidence trend of the bedrock marker in the goat, indicating that the settlement of the tunnel side wall is also caused by the subsidence of the goat. Figure 10 shows the curve of the subsidence of the left line side wall of the tunnel, and Figure 10 shows the curve of the right line. The horizontal axis is the tunnel chainage.  
As can be seen by comparing the numerical calculations results that [PERSON] forecasted [34] (the red curve in Figure 11) with the results of the settlement monitoring at the present stage (the black curve in Figure 11), the measured ground subsidence is consistent with the estimated total residual settlement of the surface (Figure 11). At the present stage, the maximum settlement of the side wall is 23.11 mm, which is smaller than the result of the prediction. The slope of the ground is almost negligible (the slope of the left and right sidewalls is 0.05 to 0.06%). Therefore, the tunnel is safe at the current operation stage in the serviceability condition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bedrock Group**} & \multirow{2}{*}{
\begin{tabular}{c} **No. of Bedrock** \\ **Marker** \\ \end{tabular} } & \multicolumn{3}{c}{**Subsidence Speed Variation (mm/month)**} \\ \cline{3-5}  & & **The First Stage** & **The Second Stage** & **The Third Stage** \\ \hline \multirow{3}{*}{The First Group Bedrock} & 1-11 & 0.931 & \(-\)0.009 & 0.536 \\  & 1-21 & 0.957 & 0.004 & 0.558 \\  & 1-31 & 1.026 & 0.003 & 0.590 \\  & 1-41 & 0.809 & 0.023 & 0.560 \\ \hline \multirow{3}{*}{The Second Group Bedrock} & 2-11 & 0.425 & \(-\)0.099 & 0.002 \\  & 2-21 & 0.647 & \(-\)0.387 & \(-\)0.021 \\  & 2-31 & 0.710 & \(-\)0.359 & \(-\)0.032 \\  & 2-41 & 0.457 & \(-\)0.111 & 0.031 \\ \hline \multirow{3}{*}{The Third Group Bedrock} & 3-11 & \(-\)0.071 & \(-\)0.002 & 0.028 \\  & 3-21 & \(-\)0.044 & \(-\)0.038 & 0.074 \\ \cline{1-1}  & 3-31 & \(-\)0.039 & \(-\)0.170 & 0.331 \\ \cline{1-1}  & 3-41 & 0 & 0 & 0 \\ \hline \multirow{3}{*}{The Fourth Group Bedrock} & 4-11 & 0.065 & \(-\)0.404 & 0.279 \\ \cline{1-1}  & 4-21 & 0.160 & \(-\)0.310 & \(-\)0.013 \\ \cline{1-1}  & 4-31 & 0.180 & \(-\)0.114 & 0.261 \\ \cline{1-1}  & 4-41 & 0.187 & \(-\)0.369 & 0.241 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Subsidence speed variation for bedrock markers in the goat.
# Metamaterial Based Design of Compact UWB/MIMO Monopoles Antenna with Characteristic Mode Analysis
## 5 Diversity Analysis of the MIMO Antenna
A comparison between the proposed and the UWB-MIMO antennas presented in the literature regarding isolation approach/structure,  
Figure 31: Diversity gain of the MIMO antenna. Figure 30: Channel capacity loss (CCL) of the MIMO antenna system. size, frequency range, antenna spacing, envelope correlation coefficient (ECC), peak gain, and total efficiency is classified in Table 3. It is perceptible that the proposed MIMO antenna has high gain, high efficiency, and high isolation. Moreover, we extracted characteristic mode analysis at 5.2 and 10.6 GHz to gain more physical insight into antenna operating principles.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Modelling Techniques
#### 3.3.2 Deep Learning Approaches  
Convolutional Neural Network (CNN): several studies employ CNNs as core components of multimodal systems, integrating them with complementary techniques such as Long Short-Term Memory (LSTM) and Transformer networks to capture both spatial and temporal features [72; 73]. CNNs have been used for emotion recognition tasks in classroom contexts, often achieving high accuracy by leveraging multimodal inputs like speech and facial expressions [8; 90]. In music education, CNNs were employed to extract features from audio signals, forming part of complex architectures that also incorporate recurrent layers and reinforcement learning modules [91]. Additionally, adaptive deep CNNs have been applied to facial expression recognition using entropy-based optimisation and transfer learning (TL) strategies [71]. Some studies have used CNNs in combination with fuzzy logic to model students' emotional states in language learning [90], while others embedded them within pipelines for analysing learner artefacts in learning analytics platforms [79]. Overall, these approaches demonstrate the versatility of CNNs in handling various data modalities and modelling needs across educational research.  
Multi-Layer Perceptron (MLP): for instance, MLPs have been used to classify student behaviours in special education contexts, alongside other classifiers within standard ML pipelines, including balancing, cross-validation, and hyperparameter tuning [13]. In another study, MLPs were employed at the decision-fusion level, integrating facial and EEG features to infer cognitive and emotional states, showcasing their effectiveness in multimodal engagement detection frameworks [35].  
Deep Neural Network (DNN): DNNs have been utilised in MMLA to address both classification and regression tasks. In one study, DNNs were implemented as part of an ML pipeline to model behaviour changes in students with special education needs, demonstrating the capacity of deep models to capture complex patterns in multimodal data [13]. Another investigation employed a two-stage approach where DNNs were applied to predict artefact quality in project-based learning, outperforming traditional models and confirming their potential for evaluating educational outcomes based on diverse data sources [92].  
Autoencoders: autoencoders function as unsupervised feature reduction mechanisms in predictive student modelling, especially when processing multimodal data from game-based learning environments. In one study, autoencoders were used alongside TL and multi-task learning (MTL) to enhance the accuracy of models predicting post-test performance and student interest [93]. The approach integrated diverse modalities such as gameplay logs, eye gaze, facial expressions, and reflective writing, enabling a comprehensive representation of student learning behaviours.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 3 Localization Analysis of Double Arrays
### Precision Analysis for Localization
The range error stays below 5 m under a range condition of 100 m in most areas, and the azimuth error remains below 0.2\({}^{\circ}\) for all conditions. Considering the environmental factor (\(c\)) and calculated factor (\(\sigma_{\tau}\)), it is advisable to choose large array sizes to improve localization precision. However, larger array sizes result in higher costs and increased complexity of the system.