esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt
## 6 Conclusions and Outlook  
In this study, a novel machine learning algorithm, namely ANN-TLBO, was used to predict the dynamic modulus \(|\)E*\(|\) of SMA, one of the most critical mechanical properties of the asphalt concrete. The proposed model consisted of one well-known machine learning algorithm (ANN) and an effective optimization algorithm (TLBO). The dataset resulting from experiments consisting of 96 entries (average values per 6 of 576 experimental results) was used for the development of the algorithm. The performance of the proposed model was assessed by various common statistical measurements, such as RMSE, MAE, and R.  
The validation results showed that the performance of ANN-TLBO was remarkable with ideal values of R\({}^{2}\) (0.975 for the training part and 0.981 for the testing dataset). Such results were superior to those obtained by a single ANN model (0.907 for the training phase and 0.948 for the testing dataset). In addition, a sensitivity analysis was performed, and the results showed that the testing temperature was the most influential factor in the prediction of the dynamic modulus \(|\)E*\(|\). These results indicated that ANN-TLBO could be a promising predictor of the dynamic modulus \(|\)E*\(|\) of SMA mixtures. It could thus be used in the designing phase of SMA mixtures, aiming at a more effective and efficient process.  
Extending the existing database could be a short-term perspective of the present study. This could improve the quality of the dataset and expand the ranges of input variables used in the prediction process. Besides, despite an astonishing performance of ANN-TLBO, the use of alternative machine learning algorithms or optimization techniques should be examined to improve the effectiveness of the prediction algorithm.  
**Author Contributions:** Conceptualization, T.-H.L., H.-L.N., H.-B.L., and C. -T.P.; data curation, T.-H.L., M.H.N., and N.-L.N.; formal analysis, T.-H.L., H.-L.N., and C.-T.P.; methodology, H.-L.N., H.-B.L., B.T.P. and T.-T.L.; project administration, H.-L.N. and H.-B.L.; supervision, H.-L.N., H.-B.L., B.T.P., and T.-T.L.; validation, H.-L.N., C.-T.P., and H.-B.L.; visualization, T.-H.L., H.-B.L. and T.-T.L.; writing--original draft, all authors.; writing--review & editing, H.-L.N., H.-B.L., and B.T.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Conflicts of Interest:** The authors declare no conflict of interest
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 3 The Proposed Schemes  
In this section, we proposed the 2 LCH and the 2 LCH-based anonymous IoT and D2D authentication schemes. We first introduce the system architecture. As our schemes can be applied in any IoT architectures where there are servers (denoted as \(S\), which could be either cloud servers or edge servers) responsible for managing the devices (denoted as \(D\)); here we avoided using the specific terms from 5G to avoid the possible misconception that the schemes are limited to the 5G domain only. Table 1 lists the notations. The channels between devices and servers and among devices are susceptible to various attacks. This paper focused on the authentications between devices and on the authentications between a device and its server. The servers are trusted, but the devices might be compromised.  
The schemes of this work include three parts. (1) Device-to-server (D2S) anonymous authentication. (2) Server-assist (S-assist) device-to-device (D2D) anonymous authentication, which facilitates anonymous device-to-device authentication via the help of servers. (3) Direct D2D anonymous authentication in which two devices directly authenticate each other. With the authentications, the authenticated entities can share the secure session keys in their transmissions. Each scheme consists of two phases: the registration-initialization phase and the respective authentication phase.
## 4 Experiments
### Tracking Results
As shown in Figure 8, the point cloud produced by our method is cleaner, with noise points from dynamic objects effectively removed. This leads to more precise tracking and pose estimation, as dynamic interference is suppressed without affecting the underlying static structures. The effectiveness of our dynamic object removal strategy is particularly critical in applications where moving objects could otherwise disrupt the accuracy of tracking, such as in autonomous driving or industrial robotics contexts. Our method's performance in dynamic environments highlights its superior ability to suppress interference from dynamic objects, which is one of the key challenges in SLAM. By leveraging both segmentation and sensor fusion techniques, our approach can remove dynamic interference from both LiDAR and visual data, ensuring that the pose estimation remains accurate even in the presence of moving objects. The results on all datasets demonstrate that the combination of these techniques provides a robust solution to the dynamic object problem, making our system highly applicable to real-world dynamic environments. Overall, our tracking results show that the proposed method not only achieves superior performance compared to state-of-the-art techniques but also demonstrates significant versatility and robustness in handling dynamic and complex environments. This positions our method as a reliable solution for real-time tracking and pose estimation in challenging dynamic settings where other methods can struggle to maintain accuracy.
# Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games
###### Abstract  
Accurate and interpretable prediction plays a vital role in natural language processing (NLP) tasks, particularly for enhancing user trust and model transparency. However, existing models often struggle with poor adaptability and limited interpretability when applied to dynamic language prediction tasks such as **Wordle**. To address these challenges, this study proposes an interpretable reinforcement learning framework based on an Enhanced Deep Deterministic Policy Gradient (Enhanced-DDPG) algorithm. By leveraging a custom simulation environment and integrating key linguistic features word frequency, letter frequency, and repeated letter patterns (rep) the model dynamically predicts the number of attempts needed to solve **Wordle** puzzles. Experimental results demonstrate that Enhanced-DDPG outperforms traditional methods such as Random Forest Regression (RFR), XGBoost, LightGBM, METRA, and SQIRL in terms of both prediction accuracy (MSE = 0.0134, R\({}^{2}\) = 0.8439) and robustness under noisy conditions. Furthermore, SHapley Additive exPlanations (SHAP) are employed to interpret the model's decision process, revealing that repeated letter patterns significantly influence low-attempt predictions, while word and letter frequencies are more relevant for higher attempt scenarios. This research highlights the potential of combining interpretable artificial intelligence (I-AI) and reinforcement learning to develop robust, transparent, and high-performance NLP prediction systems for real-world applications.  
interpretable artificial intelligence; reinforcement learning; natural language processing; enhanced deep deterministic policy gradient; **Wordle** prediction
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 3 Model Construction
### Data Normalization and Correlation Analysis  
When predicting the health index of turbofan engines, there is a significant difference in the order of magnitude between operational parameters and sensor parameters. To train neural networks, each feature needs to be weighted and summed. Without normalizing the original data, some features may carry excessively large weights, while others may have excessively small weights. This can complicate the training of neural networks and affect the predictive performance of the model.  
Equation (8) expresses the Min-Max scaling calculation used to normalize both the operational parameters and sensor parameters in the original dataset. The goal is to mitigate the dimensional impact among different features.  
\[X_{i-norm}=\frac{X_{i}-X_{min}}{X_{max}-X_{min}} \tag{8}\]  
Here, \(X_{min}\) represents the minimum of the feature, \(X_{max}\) represents the maximum of the feature, \(X_{i}\) is the original value, and \(X_{i-norm}\) is the normalized value.  
The Pearson linear correlation coefficient is the most commonly used linear correlation coefficient [40]. We calculated the Pearson correlation coefficient in order to analyze the correlation between features. Equation (9) shows the calculation of the Pearson correlation coefficient.  
\[\text{r}=\frac{\sum_{i=1}^{n}\bigl{(}X_{i}-\overline{X}\bigr{)}\bigl{(}Y_{i}- \overline{Y}\bigr{)}}{\sqrt{\sum_{i=1}^{n}\bigl{(}X_{i}-\overline{X}\bigr{)}^ {2}\sum_{j=1}^{n}\bigl{(}Y_{j}-\overline{Y}\bigr{)}^{2}}} \tag{9}\]  
Here, \(n\) is the sample size of the feature, \(X_{i}\) represents the \(i\)-th sample value of the feature, \(\overline{X}\) represents the mean value of the feature, \(Y_{i}\) is the \(i\)-th value of the number of operating cycles, and \(\overline{Y}\) is the mean value of the number of operating cycles.  
For the No. 100 engine in the training set of the FD001, we selected the 15 features with feature importance larger than 0.1 (\(\text{C}_{\text{i}}>0.1\)) from its records. We then normalized the original data using Min-Max normalization to obtain the corresponding normalized values. The results of the normalization are presented in Figure 5.  
From Figure 5, it seems that 10 out of the 15 features exhibit an increasing trend with the number of engine cycles, while the remaining 5 features show a decreasing trend. Analyzing these changes in degradation can enhance our understanding of the relationship between the engine's operational state and the features, providing valuable validation for feature selection result. The Pearson correlation coefficients between the number of operating cycles and the other 14 features are presented in Table 3. All the 10 features in Figure 5 show positive Pearson correlation coefficients in Table 3, while the 5 features in Figure 5 present negative values in Table 3. In this way, the values in Table 3 justify the observed result of normalization.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 5 Experiments
### Algorithm Performance Analysis  
We present the performance results of the neural network algorithm and heuristic algorithm for different application configurations in Figures 6-8. The scheduling algorithm is employed to schedule and execute both classical and real-world applications' DAGs. We plot the trend of the average application execution time with the job DAG arrival rate. The horizontal axis depicts different scale values, the smaller the scale, the faster the application arrival rate; the vertical axis depicts the average application execution time.
# Antioxidant Activity of _Panax ginseng_ to Regulate ROS in Various Chronic Diseases
## 6 _P. ginseng_ in ROS-Mediated Diseases
### Antioxidant Activities of P. ginseng in Neurogenerative Diseases
#### 6.2.1 Parkinson's Disease  
Parkinson's disease (PD) is a chronic neurodegenerative condition that affects approximately 2% of people over 60 years old worldwide. PD depends on the interplay between various genetic and environmental factors and is marked by the development and accumulation of misfolded-\(\alpha\)-synuclein [90]. The hallmark symptoms of PD include motor disorders (rigidity, tremor, and bradykinesia) and non-motor disorders (depression, sleep disturbance, and autonomic dysfunction) resulting from the gradual deterioration of the dopaminergic pathway [92]. Several studies have depicted that ginseng and its bioactive components, ginsengides, have therapeutic actions on PD. _P. ginseng_ extracts can inhibit ROS generation, eliminate Bax/Bcl2, increase the cytochrome C release, and stimulate caspase-3 expression to alleviate cell death [93]. Ginsenoside Rg1 suppressed the oxidative stress to mediate the neuroprotective action in MPTP (1-methyl-4-phenyl-1,2,3,6-twtrahydropyridine)-induced substantia nigra [94; 95]. Rg1 activates total superoxide dismutase (SOD) and inhibits glutathione reduction, reducing c-Jun and N-terminal kinase (JNK) in the substantia nigra of C57 BL/6 mice [94]. Furthermore, Rg1 decreases ROS production and mitochondrial cytochrome C and blocks the activation of caspase 3 and the formulation of the iNOS protein and NO in PC12 cells [96]. Again, Rg1 attenuates ROS generation and NF-\(\kappa\)B translocation in MPP+-induced MES23.5 cells for reducing the expression of DMT1-IRE [97]. Ginsenoside Re shows neuroprotective action against the neurotoxicity of substantia nigra. Ginsenoside Re increases the Bcl-2 mRNA and Bcl-2 protein expression decreases the iNOS, Bax, and Bax mRNA and inhibits the cleavage of caspase-3 to protect the SN neuron from MPTP-induced apoptosis [98].
# Review of Satellite Interferometry for Landslide Detection in Italy
## 6 How InSAR Products Have Been Used for Landslide Studies in Italy
### Landslide Characterization  
Single studies aimed to the recognition of the spatial and temporal pattern of a landslide are the most common applications of InSAR in Italy. Satellite data are the perfect support for landslide characterization activities, where an on-site measurement network has been already deployed, or where the motion of the landslide is known or supposed, and additional information is required.  
One of the very first applications of DInSAR for landslide characterization comes from Northeastern Sicily [141]. The Randazzo landslide (Catania Province) was the target for an integrated analysis between ERS1/2-derived interferograms, topographic surveys, inclinometric data, and groundwater level measures. Although in the early stages of the technique, satellite interferometry provided useful information for the landslide state of activity, confirmed by ground evidence.  
The Assisi-Ivancich landslide is an example of a known hazard whose activity has been determined by several authors using satellite interferometry [54] and references therein. This roto-translational landslide affects a part of the urban area of the city of Assisi [58]. [PERSON] et al. [58,59] report that the landslide motion is constant over time (with an average deformation rate around 1 cm/yr), regardless of the water table fluctuations. Its activity appears to be controlled by viscous factors. The landslide motion over time has been characterized by means of different radar sensors (both C- and X-band), and of various processing techniques. The satellite analysis was supported by ground measures, e.g., inclinometers, and by other remote sensing sources, e.g., Global Navigation Satellite System (GNSS) data.  
In synthesis, landslide characterization is based on:  
* One or more interferometric datasets, including multi-band approaches (e.g., [56; 95; 108; 142; 143]);
* Geotechnical parameters of the landslide mass and bedrock (e.g., [80; 95; 144; 145]);
* Ground survey evidence and geomorphological features, such as ground fissures, cracks or trenches, and/or building damage (e.g., [146; 147; 148; 45; 149; 46; 45; 147]);
* In situ measurements, such as inclinometers (e.g., [96; 149; 150; 151]);
* Additional remote sensing monitoring tools, such as topographic levelling techniques, TLS (Terrestrial Laser Scanning), or GBInSAR (e.g., [76; 92; 151; 152]);
* Geophysical surveys data, such as seismic or geoelectrical surveys (e.g., [122; 152]);
* Photogrammetry (e.g., [108; 153]);
* Thermography (e.g., [144; 154]);
* Rainfall data, hydrogeological conditions, and water table fluctuations (e.g., [123; 155]).
## 3 Background Study
### Data Aggregation  
The wireless sensor networks comprise tiny sources whose primary function is to detect various environmental phenomena and then forward them to the concerned base station. This communication carries a lot of cost, time, energy, accuracy, and reliability constraints [22]. Adopted by the finite source of energy the sensor node devices consume the maximum amount of energy, yielding a decreasing trend of battery life of devices. It may happen that any base station requires any information regarding any phenomenon happening near to it. Hence, in this scenario, there may be present any node to provide such spatio-temporal information related to it [23]. Temporal redundancy happens when there is a repeated phenomenon in various timestamps in a short time interval. Spatial redundancy happens when the phenomenon is correlated in space constraints, i.e., location based. Detection of the required event-based phenomenon which exhibits the spatial redundancy of the received data in the corresponding network fields [24]. They broadcast useful information in the direction of the desired sink after detecting the event. Remote sensor areas such as habitat monitoring, underwater sensor field are energy-consuming areas in the order of event detection and transmission. Repeated event detection phenomena often lead to network overhead in terms of energy, accuracy, latency, and reliability which eventually leads to redundant data transmission to sink [25]. Data aggregation can be chosen as an efficient solution to deal with the lacuna of energy for the sensor devices. This filtering technique can be levied both at the source and sink level to deal with the transmission of the redundant data. Aggregation functions such as MAX, MIN, SUM, COUNT [26] or any other mathematical calculations such as MATCH FUNCTION [27], CORRELATION COEFFICIENT TECHNIQUE [28] can be used to exploit the spatial and temporal data redundancy to achieve the desired goal [29].
# Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN
## 3 Proposed Scheme: SDVN for Foggy Highways
### Case (ii): When the Previous Platoon Is Moving Faster Than the Leading Platoon  
In this case, we consider that when platoon B is moving at a higher speed than platoon A, it reaches the low-visibility region earlier. Platoon A and LCs are constantly sending PAMs to platoon B, letting it know of the low visibility ahead. If, for some reason, platoon B does not respond appropriately, LCs may ask platoon A to change lane immediately as shown in Figure 3. Thus, to prevent collisions between vehicles, Equation (5) involves maintaining a safety distance depending on the speed between vehicles. Equation (5) is applicable only if vis, < 20 m. This means that the scheme does not allow vehicles to change lanes when visibility is <5 m. To keep Equation (5) valid, it is crucial to keep in mind that the new velocity will soon become 0 if visibility drops below 5 m. According to our algorithm, all vehicles are required to stop when visibility is less than 5 m. If platoon B responds to these PAMs, there is no need for platoon A to change lanes.  
Case (iii): Low Connectivity: When the Network Is Sparse, a Platoon May Find It Difficult to Locate an LC Within Its Range  
There is a possibility in such a highly dynamic network with such critical weather conditions that no platoon finds another platoon or LC in its vicinity, as can be seen in Figure 4. For instance, if a querying platoon is looking for a leading node to inform it about the current visibility in its proceeding path, it becomes challenging for a source platoon to find a relay to reach the destination platoon. In this scenario, the HV stores and carries the packet until it reaches the communication range of some LCs or other nodes on the highway. This may increase the time to convey any message in the network, but it is better to store the packet rather than drop it. To sum up, we ensure safe and stable driving during foggy weather with the help of SDN controllers that ensure connectivity and form stable communication links by instructing platoons according to visibility conditions, leading to optimal routing paths for exchanging urgent messages. We will demonstrate this using simulation results for each case in the following section.  
Figure 4: A sparse network scenario in a foggy SDVN.  
Figure 3: A lane change scenario in a foggy SDVN.
# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 4 Experiments
### Cross-Attention Weights Temporal Stability Experiment  
To further validate the temporal stability of cross-attention weights, we conducted experiments using the Absolute Trajectory Error (ATE) metric. ATE measures the absolute difference between the estimated trajectory and the ground truth trajectory. To calculate it, we first align the estimated trajectory with the ground truth using a rigid-body transformation (usually via the Procrustes algorithm), then compute the root mean square error (RMSE) of the point-wise distances between the two trajectories.  
Tests on consecutive frames of Replica and ScanNet datasets show ATE ranges of 12.1-12.5 and 13.2-13.6, respectively. The experimental results show that the ATE values are stable within a certain range. These results indirectly demonstrate that the cross-attention weights have good temporal stability, and the model can maintain consistent performance in dynamic environments.
# Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things
## 2 Related Work
In FGSM, the number of active transmitter antennas is used as an index to transmit data by the same data signal constellation [15; 16] where the active antennas indexes are used to send information bits, and also their varying quantity r is used as an index to carry additional information bits. For example, one or more antennas can be activated to send data using the same data signal constellation, hence the number of activated antennas carries information as well. In other words, it is a combination of SM and GSM where if one antenna is activated, FGSM is similar to SM, while if multiple/all transmit antennas are activated, the FGSM is equivalent to GSM. In this way, FGSM achieves greater data bit transmission, and its achievable data rate \(R_{FGSM}\) is given by:  
\[R_{FGSM}=\ log_{2}(M)+\big{\lfloor}\ log_{2}\sum_{k=1}^{N_{t}}\Bigg{(} \begin{array} []{c}N_{T}\\ k\end{array} \Bigg{)}\big{\rfloor}=\ log_{2}(M)+\ N_{T}-1, \tag{4}\]  
As in the FGSM combination technique, the varied number of active antennas has been applied to QSM in [16] resulting in FGSM. FGSM transmits the real and the imaginary components of the signal constellation in one or more transmitter antennas in orthogonal dimensions, and its achievable data rate \(R_{FQSM}\) is expressed as follows:  
\[R_{FQSM}=\ log_{2}(M)+\ 2\big{\lfloor}log_{2}\sum_{k=1}^{N_{T}}\left( \begin{array}{ c}N_{T}\\ k\end{array} \right)\big{\rfloor}=\ log_{2}(M)+\ 2(N_{T}-1). \tag{5}\]  
Despite the achievable data rate enhancement in FGSM and FGSM compared with conventional schemes, transmitting the same data by multiple transmitter antennas increases the average energy per transmitted symbol which effects power saving dramatically.