esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
Across the various feature selection methods, the combination of SVM-RFE with the SVM emerged as the top performer, with the SVM achieving the highest accuracy of 93.54% and Logistic Regression following closely at 92.86%. Elastic Net also proved to be a powerful feature selection technique for the SVM and Logistic Regression, with both models performing exceptionally well. On the other hand, tree-based models like Random Forest, XGBoost, and CatBoost performed better with mutual information, chi-squared, and mRMR feature selection methods, indicating that these techniques better capture the complex, non-linear relationships in the data. Figure 9 illustrates the trends across feature selection methods visually. As shown, SVM-RFE and Elastic Net allow linear models like Logistic Regression and the SVM to outperform non-linear models. Figure 9: Algorithm performance metrics with and without different feature selection techniques. Since the results demonstrate that features selected using SVM-RFE resulted in the best performance based on accuracy, precision, recall, and F1 score, ROC curves were used to further evaluate the performance. In this evaluation, the SVM achieved the highest AUC (0.984), followed by Logistic Regression (AUC = 0.968), indicating strong classification performance. XGBoost performed moderately with an AUC of 0.837, while tree-based models like LGBM (AUC = 0.770) and CatBoost (AUC = 0.704) showed more modest results. Random Forest had the lowest AUC (0.657), as shown in Figure 10.
## 4 Discussion
### Forces at Impact  
According to Newtonian physics, acceleration and velocity are proportional to impact forces. Our data indicate that the statistically significant differences in velocity between throws may correspond to greater impact forces, which would be interpreted as greater velocity equaling greater force and therefore greater risk of injury. A critical factor in reducing the risk of shoulder injuries in judo is the proper execution of ukemi, the breakthrough technique. Ukemi allows judokas to distribute the impact forces over a larger surface area of the body, reducing the stress applied to specific joints, such as the shoulder. Studies have shown that judokas who master ukemi are less likely to suffer from severe shoulder injuries, as the technique minimizes direct collisions between the shoulder and the tatami [24].  
In our analysis, although the acceleration in TG was greater than STG, the difference was found to be statistically insignificant. Discrepancies between acceleration and negative velocity results likely reflect differing biomechanical demands inherent to each technique. Negative velocity is directly influenced by the speed of rotation and the angle of uke's body during the throw, whereas acceleration is affected by the initial force applied by tori. Larger sample sizes or comparisons across athletes of different levels may reveal statistically significant differences in these indicators. On this basis, our analysis cannot be certain that there is a significant difference on impact forces between the two throws, and further research, potentially with a greater sample size, could present additional scientific input on the matter.  
Impact forces are also dependent on the rigidity of colliding surfaces, which, in our case, are uke's shoulder and the tatami. Rigidity determines the stopping distance (d), which is the distance travelled after impact and is inversely proportional to impact forces. This means that during a collision, the greater the rigidity, the less elastic the collision will be and, therefore, greater momentum will transfer at impact, and, hence, greater forces are to be developed to the body [25,26].
## 4 Simulations and Results
### UFLS Design Using the Identified Simplified SFR Model  
In this paper, the main concern is to optimize the UFLS schemes to maximize the imported power under normal circumstances and yield acceptable system performance after islanding for the small receiving-end power system. To ensure the UFLS schemes against accidents or the loss of the maximized active power import, the PSO-based model proposed in Section 3 was employed to minimize the frequency deviations and the total amount of the load needed to be shed.  
In order to illustrate the proposed approach for designing UFLS schemes, we considered the small receiving-end system shown in Figure 10 whose simplified SFR model parameters are identified using the developed model. Other parameters are set as: \(C_{e}=1.0\) pu/pu, \(C_{ls}=0.1\) pu/pu [9]; \(k_{min}=1\), \(k_{max}=6;f_{th\_min}=48.0\) Hz, \(f_{th\_max}=49.5\) Hz; \(f_{e}=0.1\) Hz; \(\Delta d_{max}=0.3\) pu, \(\Delta d_{min}=0.01\) pu; \(t_{d\_min}=0.1\) s, \(t_{d\_max}=0.2\) s;\(f_{ss\_min}=49.8\) Hz, \(f_{ts\_min}=48.8\) Hz, \(f_{ts\_max}=50.2\) Hz.  
Figure 11: Comparison between the full scale model and simplified SFR model response: (**a**) Peak demand; (**b**) Valley demand.  
Table 4 shows the statistical optimization results for different \(k\) by performing ten independent runs. As seen, the most favorable UFLS schemes are for \(k=3\). Table 5 shows the optimized relay setting of the UFLS schemes for the test system on the condition that the imported active power is maximized. Only the first two ranked values of the load shedding stages are shown in Table 5.
## 3 Smart Irrigation System Considering Optimal Energy Management with Predictive Control  
The proposed smart irrigation system considering optimal energy management with model predictive control MPC is shown in Figure 3. It contains two stages. The initial stage focuses on obtaining the daily irrigation demand profile in a smart way through an expert system. This system is programmed to automatically obtain the daily moisture by means of the water balance, i.e., the water gain and loss in the root zone of the crop by means of Equation (1), for which the forecasted data of one day of precipitation (\(\hat{P}\)) are needed as water input to the system. Similarly, it is necessary to know the water losses that occur in the crop by evapotranspiration. Using Equation (6), the evapotranspiration of the crop (\(ET_{c}\)) is determined and by means of meteorological data the referenced evapotranspiration is obtained with Equation (7). The energy demand is the result of the irrigation time for the daily turning on of the pumps throughout the day, this demand profile obtained is transmitted to the next stage which is called energy manager system (EMS) of the microgrid for the irrigation system; in this stage an optimization problem is solved and is responsible for the efficient management of the energy system to determine the energy needed to turn on the water pump, taking advantage of renewable resources for power generation. In this second stage (EMS), the availability of energy from the hybrid energy system formed of photovoltaic panels, storage systems and a diesel generator is guaranteed.
# Photocatalytic Cementitious Material for Eco-Efficient Construction--A Systematic Literature Review
## 4 Discussion
### Influence of Using Cementitious Materials as Catalyst Supporting Media on Photocatalytic Activity
#### 4.1.1 TiO\({}_{2}\) Dispersion  
The effectiveness of TiO\({}_{2}\) dispersion in cement media has a great influence on the photocatalytic performance of the resulting products. In conventional dispersion methods, the TiO\({}_{2}\) particles were usually mixed with cementitious materials, such as dry powders, by mechanically stirring before adding water. This mixing procedure was inclined to strong aggregation of TiO\({}_{2}\) particles in the cement matrix, resulting in the decrease in the available active surface area in hardened cement [75]. It was found that the hydrodynamic size of TiO\({}_{2}\) nanoparticles dispersed in aqueous solution tends to be larger than the primary particle size. [PERSON], [PERSON] [76] shows that probe sonication works very well to significantly decrease the hydrodynamic size. In the next studies, [PERSON], [PERSON] [77] as well as [PERSON], [PERSON] [14] adopted a wet-mixing procedure in order to suppress the aggregation of TiO\({}_{2}\) particles in cement matrix. In wet-mixing procedure, the TiO\({}_{2}\) particles were first mixed with water using probe ultrasonication before adding other materials. The use of ultra-sonication leads to uniform distribution of the nanoparticles and enhances the photocatalytic performance.  
Regardless of the mixing methods, mixing TiO\({}_{2}\) particles with cementitious materials may reduce the active surface area due to the encapsulation of photocatalyst particles by hydration products. Consequently, the photocatalytic activity is seriously limited and sensitive to aging processes [78]. It was observed that dispersion of TiO\({}_{2}\) into coatings can be considered an attractive alternative since the coatings are in direct contact with pollutants and photons and less affected by cement hydration [14; 49]. However, the coating method might be susceptible to harsh weathering processes. Hence, the selection of suitable dispersion methods should be adjusted to their intended applications to maximize the active surface area.
## 6 Conclusions  
The problem of multiple UAVs cooperative tracking of a ground moving target is studied. The main contribution of this paper is to develop a motion planning algorithm in which communication and sensing are both optimized. This novel motion planning approach properly integrates the communication and sensing objectives to accomplish the target tracking mission of the multiple UAVs, while maintaining proper connectivity to the remote base station.  
The performance of target fusion and estimation not only depend on overall information contained in given data sets of measurement of the UAVs, but also depend on whether the UAVs could successfully send their measurement packets back to the base station or not. According to this idea, the co-optimization of communication and sensing scheme is designed.  
On one hand, a packet-erasure channel model is applied to model the communication links. In this channel model, the successful transmission probability is a function of the SNR, which is regarded as the communication reliability. On the other hand, the FIM is used to quantify the information gained by observation from the UAVs in target tracking process, which is regarded as the sensing utility. The communication reliability and the sensing utility are combined into a single utility function, which is used to plan trajectories over a finite time horizon for the UAVs. To maximize the overall information gain delivered to the base station, each UAV must make a tradeoff between maximizing the information gain through sensing and improving the probability of a successful transmission with the base station. The results of comparison simulations show that the proposal approach could improve fusion and estimation performance compared to method that does not consider communication optimization.  
Figure 40: The average trace of the FIM for different numbers of UAVs (Scenario 5).  
Figure 39: The average estimation error of target direction for different numbers of UAVs (Scenario 5).  
To produce efficient results in the moving target tracking scenario, the topology optimization strategy based on the minimum spanning tree (MST) is designed. The MST communication topology determines the planning hierarchy, which is the order of the optimization calculation in the distributed frame. Compared to the fix communication topology strategy, the results of simulations verify the rationality and validity of the topology optimization strategy.  
Conceptualization, Z.L. and X.F.; Methodology, Z.L.; Software, Z.L.; Validation, Z.L., X.G. and X.F.; Formal Analysis, X.F.; Investigation, Z.L.; Resources, X.F.; Data Curation, Z.L.; Writing-Original Draft Preparation, Z.L.; Writing-Review & Editing, Z.L.; Visualization, Z.L.; Supervision, X.F.; Project Administration, X.G.; Funding Acquisition, X.G.  
The authors would like to express their acknowledgement for the support from the National Natural Science Foundation of China under Grant No. 61573285.  
The authors declare no conflict of interest.
## 4 Results: Proof of Concept  
This research validates the feasibility of decentralized identity management for IoT devices using the IOTA framework. A Raspberry Pi was used as a test platform to implement Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and Verifiable Presentations (VPs). The setup integrated IOTA Identity for identity operations and IOTA Streams for secure communication and access control. A node was deployed to interact with the IOTA ledger to enable the registration and verification of IoT identities. The diagram of the deployed proof of concept is shown in Figure 1. This figure shows the key interactions within the proposed system, highlighting the flow of data and processes. The \"Synchronization and Consensus\" connection illustrates how the IOTA node interacts with the Tangle DLT to maintain data consistency and validate transactions. The Data Publishing to Tangle link shows how the Raspberry Pi securely publishes data streams or Decentralized Identifiers (DIDs) to the distributed ledger. The \"Data Transfer for Validation\" path shows how the Raspberry Pi interacts with the IOTA node to validate and manage its data streams prior to publication. In addition, the Identity and Access Validation connection outlines the response flow from the IOTA Node back to the Raspberry Pi for identity verification. Internally, the Raspberry Pi manages \"Identity & Stream Management\", allowing seamless integration with the IOTA Identity and Streams frameworks. Finally, the \"Transaction Confirmation and Retrieval\" link between the Tangle and the IOTA node ensures that transaction feedback is efficiently communicated back to the system, supporting transparency and reliability.
## 3 Materials and Methods
### Model Preparation  
#### 3.2.1 Parametric Design  
In this project, a numerical OMSS simulation is proposed as a case, and it was designed for cast-in-place box girder bridge deck construction. The maximum span of the bridge was required to be 110 m. For such a long-span bridge deck construction, special designs should be considered, and several precedents are referenced. In recent years, the construction ability of the OMSS has reached 90 m. MSS M1-90-S, an OMSS model designed by BERD, has successfully been applied in a cast-in-place high-speed rail (HSR) post-tensioned box girder bridge deck construction project in Turkey [4]. The OMSS model took advantage of the natural geometry of a steel trussed arch and the innovative Organic Prestressing System (OPS). The OPS can actively control mid-span deflections; however, this system was not considered and simulated in this project. Other design philosophies, such as the arch embedded in M1-90-S, were also borrowed to develop the new OMSS prototype for 110-metre-span bridge superstructure construction.  
To effectively obtain the most appropriate model, the parametric design method was applied using Rhinoceros 3D Version 8 (Rhino 8) with Grasshopper, a powerful three-dimensional CAD modelling software with parametric design features. Preliminary structural optimisation was conducted through Karamba3D, a finite element analysis (FEA) tool within Grasshopper. Subsequently, a line structure model was produced and imported into Strand7 for detailed numerical analysis.  
Like the typical modelling process using other software, a clear modelling logic should be derived in advance, as shown in Figure 2. The whole OMSS model can be divided into six components: (1) primary girder (main girder); (2) secondary girder (sub-girder); (3) primary arch (main arch); (4) secondary arch (sub-arch); (5) wing; and (6) substructure.  
Figure 1: Assumed post-tensioned bridge deck design: (**a**) section; (**b**) elevation.  
The main girder, sub-girder, main arch, and sub-arch are composed as the primary load-bearing structure, and each component can be further divided into truss and transverse bracing members. The substructure can be divided into hanger and platform.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 3 System Model
### Energy Consumption Model  
According to [24,25], we assume that the energy of sensor nodes is consumed by sending, receiving, and fusing data. Figure 1 shows the relation among the energy consumption by each part.  
The energy consumed by receiving \(k\) bits data is  
\[E_{R_{x}}(k)=k\times E_{elec}, \tag{4}\]  
where \(E_{elec}\) represents the energy consumed on per bit of data by the transmitter or the receiver.  
When a node transmits \(k\) bits data to a receiver with the distance \(d\), the energy consumption can be written as  
\[E_{T_{x}}(k,d) =E_{T_{x}-elec}(k)+E_{T_{x}-amp}(k,d) \tag{5}\] \[=\left\{\begin{array}{l}kE_{elec}+kE_{fs}d^{2},d<d_{0}\\ kE_{elec}+kE_{mp}d^{4},d\geq d_{0}\end{array}\right.,\]  
where \(E_{T_{x}-elec}(k)\) is the consumed energy of transmitting data at the transmitter and \(E_{T_{x}-amp}\) is the energy that is consumed by the transmitting amplifier [26]. According to the distance \(d\) between the sending node and the receiving node, the channel model can be divided into the free space model and multi-path fading model. \(E_{f_{s}}\) and \(E_{mp}\) are the energy loss coefficient of power amplifier in two models, respectively. \(d_{0}\) is the distance threshold, \(d_{0}=\sqrt{E_{fs}/E_{mp}}\). If the distance \(d\) is less than the threshold \(d_{0}\), the free space model is used for power amplification loss. Otherwise, the multi-path fading model will be used.  
For the CH, it is also responsible to fuse data comparing with other common nodes. The energy that is consumed on fusing \(k\) bits data by the CH can be expressed as  
\[E_{deal}=k\times E_{DA}, \tag{6}\]  
where \(E_{DA}\) is the energy consumption of the CH fusing one bit data.  
Figure 1: Energy consumption model.
# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 5 AI and Machine Learning Algorithms in Energy Retrofitting
Although RL is not yet widely adopted for retrofitting decisions, its potential for dynamic and real-time optimization makes it a promising tool for future applications in this area. Optimization Algorithms: Optimization algorithms play a critical role in the energy retrofitting process, especially when it comes to selecting the most cost-effective and energy-efficient retrofitting measures. These algorithms are designed to explore a vast space of potential solutions, balancing multiple objectives such as cost, energy savings, and environmental impact. The use of optimization techniques ensures that retrofit strategies are not only effective in reducing energy consumption but also feasible within the constraints of budget, time, and operational requirements. The authors of [PERSON] et al. [11] present an energy performance prediction model using machine learning to assist in multi-objective optimization of retrofit planning and they use the SMAC (Sequential Model-Based Algorithm Configuration) algorithm, an advanced evolutionary algorithm that provides a reliable tool for building analysts to explore the large solution space of retrofit options. [PERSON] et al. [48] present a data-driven framework that integrates machine learning,multi-objective optimization, and multi-criteria decision-making techniques to streamline this process. A Pareto optimization algorithm is employed to identify the most effective retrofit strategies for the selected building. By integrating this optimization approach with a well-trained Artificial Neural Network (ANN) model within a Python environment, the process efficiently handles multiple objectives, such as energy efficiency, emissions reduction, and cost. The ANN model predicts the building's energy performance before and after retrofits, and these predictions are then used as input for the optimization. The Pareto algorithm systematically evaluates the trade-offs among various retrofit strategies, ultimately identifying optimal solutions that balance the competing objectives, ensuring the most efficient and cost-effective outcomes are selected. Table 3 below presents a detailed overview of the key studies reviewed, categorized by the type of machine learning algorithm utilized. This categorization illustrates the breadth of applications within the energy retrofitting process, highlighting the diverse approaches taken to optimize energy efficiency, predict consumption, and support decision-making. By examining the specific algorithms employed and their impacts, this summary provides insights into the current state of research in AI-driven energy retrofitting.
## 4 Experimental Results and Analysis
### Physical Experiment Results and Analysis
#### 4.2.3 Multiple Defects Experiment  
The detection results in the x and z directions of four different defects are shown in Figure 11a-d, respectively. The lift-off is 2.0 mm. The distance between two sensors is 2.2 mm.  
As shown in Figure 11a-d, the amplitude of the signal will increase when the depth of the defect increases. When the distance between a pair of sensors is 2.2 mm in Figure 11a,b, one sensor detects the maximum in the x direction and the other detects the minimum in the z direction, and the absolute value of the difference between the outputs of the two sensors is the maximum. As shown in Figure 11c,d, the width becomes larger when a sensor is directly above the larger defect and its output is the maximum in the x direction; the other does not measure the minimum in the z direction, but the absolute value of the difference between them is greater than that of a small defect. Therefore, the distance calculated according to the minimum defect is also applicable to the detection of large defects.  
Figure 10: Detection results with different distances. (a) 1.8 mm; (b) 2.2 mm; (c) 2.6 mm.  
A used rail with eight artificial defects is detected, and a wire is tied to the rail also, as shown in Figure 12. The width and depth of each defect are shown in Table 1. The lift-off of the sensor is also fixed at 2.0 mm. The distance between the two sensors is 2.2 mm.  
Figure 11: Detection results with different defects. (**a**) width = 2 mm, depth = 1 mm; (**b**) width = 2 mm, depth = 2 mm; (**c**) width = 3 mm, depth = 1 mm; (**d**) width = 3 mm, depth = 2 mm.  
Figure 12: A rail with multiple artificial defects.  
The filtered signal is shown in Figure 15. Compared with the original signal in Figure 14, the defect signal is basically unchanged, while the vibration signal decreases significantly. Figure 16 shows the three-dimensional image of the multichannel filtered signal. After filtering, the vibration interference is obviously reduced.