esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise
## 4 Analysis of SOC Prediction Results of Ni-Cd Battery Using APSO-GRNN
### SOC Prediction Algorithm Results and Discussion
#### 4.2.2 Model Robustness Test Results for Noise  
To further verify the superiority of the APSO-GRNN model, Gaussian white noise was introduced into the original DST working condition data, and the processed data were used as input in the trained model for prediction. The prediction results are shown in Figure 8a,b. After adding Gaussian white noise, each model's error and mean square error increased. To further observe the prediction errors of the three prediction algorithm models, the average relative error, root mean square error, and Pearson correlation of the three average models were calculated, and the data are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\delta\) & \(\delta_{RMSE}\) & \(\delta_{p}\) \\ \hline SVR & 4.58 & 0.0237 & 0.9597 \\ XGBoost & 3.59 & 0.0184 & 0.9656 \\ GRNN & 3.86 & 0.0189 & 0.9534 \\ APSO-GRNN & 1.70 & 0.0107 & 0.9789 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction errors of noisy data.  
Figure 8: Prediction results based on test set (**a**), prediction error based on test set (**b**).  
Figure 7: Convergence curve of polymerization degree (**a**), smoothing factor distribution diagram (**b**).  
Table 4 shows that the model developed in this study produced better prediction results on the noisy DST dataset than the other three models. The mean absolute error of the state-of-charge (SOC) prediction was less than that of the GRNN, XGBoost, and SVR models by 57.25%, 58.12%, and 63.97%, respectively. Moreover, the correlation coefficient of the predicted values was more robust compared to the other three models. Additionally, Figure 8 shows that the variation in the relative error of the model developed in this study was smaller compared to the GRNN and XGBoost models. The SVR model exhibited overfitting due to the limited amount of data used, leading to a significant increase in the error on the training set. The decision tree in the XGBoost model was susceptible to interference effects, resulting in a significant variation in the prediction error. The unoptimized GRNN model had the same neuron weight as the model layer, leading to a more significant influence of historical time data on the current time node, especially toward the end of the discharge process. The adaptive particle swarm optimization algorithm used in the model developed in this study adjusted the velocity weight of the particle swarm to converge the neuron weights to the corresponding optimal value. This allowed the model to prioritize the historical data that had a more significant impact on the current moment, thereby improving the prediction accuracy and anti-interference ability of the model, reducing the variation in the prediction error, and accelerating convergence.
## 3 Results and Discussion
Figure 7 depicts the results for pump pulses after 30 round trips. The SC spectrum showed obvious signs of improvement at the longer wavelengths with a much smoother profile. We sent the SC to be characterized in the SFG-XFROG, and the results are presented in Figure 8. The retrieved trace and spectrum from the SFG-XFROG in Figure 8b,d exhibited significant improvements and were devoid of the strong modulation observed in Figure 5. The components above 3.5 \(\upmu\)m of the SC spectrum were not present in the retrieved spectrum by the XFROG due to the phase-matching bandwidth limitations of the applied non-linear crystal. Such a clean spectral profile combined with the well-defined smooth phase would make the generated supercontinuum a viable candidate for application in MIR-OPA systems. Such systems pumped at the vicinity of 2 \(\upmu\)m by a regenerative amplifier and seeded by the SC from YAG hold the promise of achieving MIR pulses with a wavelength as long as 11 \(\upmu\)m in the form of the idler in a single stage. Figure 6: Comparison between the pump pulses after 45 and 30 round trips (RT). Inset: intensity representation in logarithmic scale. One drawback of reducing the round trips of the regenerative amplifier is a significant drop in the output energy. In the case of 30 RT, the amplifier was only capable of generating 0.07 mJ. That would hint toward the necessity of adding an additional fiber pre-amplifier to the regenerative amplifier system in order to provide a well-suited pump source for a scalable MIR-OPA design.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Crossband Filtering--Length Preservation
#### 4.5.1 Optimal FWSegSNR (\(L_{\text{bb}}=15\))  
The obtained results are illustrated in Figures 5a-c, providing a comprehensive assessment of the performance. The optimal gains and corresponding values of \(L_{\text{cb}}\) are summarized in Table 2. Surprisingly, the observed performance is highly competitive with the outcomes presented in Section 4.4.1, despite using fewer data for the dereverberation process. Notably, the introduced method even exhibits an improvement in terms of CD compared to the length extension approach. Optimal performance, in terms of FWSegSNR, is achieved when \(L_{\text{cb}}=2\), while, for CD and PESQ, the optimal values are obtained with \(L_{\text{cb}}=3\). These findings highlight the efficacy of incorporating early crossband samples, demonstrating their valuable contribution in improving dereverberation outcomes.
# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
## 5 Conclusions  
This paper presents a new end-to-end trainable framework where patchGAN is used with dual discriminators. The advantage of using the patchGAN is that it tries to signify if each \(N\times N\) patch in an image is real or fake rather than determining the entire image, allowing us to observe features that are otherwise hard to perceive. The fundamental characteristic of the proposed method is the weights derived from the infrared images. These weights are utilized in defining the new loss function which can be used in two ways; (1) Use the new weighted loss in infrared discriminator and as an adversarial loss  
Figure 4: Values of each metric for images from RoadScene Dataset with different \(\gamma\). Lambda is fixed to 0.5 for this analysis. Type of the loss used is shown in the legends.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Methods** & **CC** & \(MSSSIM\) & **SCD [33]** & \(FMI_{det}\) [34] & \(SSIM_{a}\) & **PSNR** \\ \hline RoadScene & Avg-Pooling & 1.2675 & 0.8655 & 1.6188 & 0.2691 & 0.7381 & 16.0222 \\ RoadScene & Max-Pooling & 1.2637 & 0.8617 & 1.5528 & 0.2671 & 0.7375 & 16.0355 \\ \hline Private & Avg-Pooling & 1.4715 & 0.9068 & 1.4614 & 0.2684 & 0.7594 & 19.0826 \\ Private & Max-Pooling & 1.4467 & 0.8988 & 1.3488 & 0.2594 & 0.7508 & 19.224 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average values of quality metrics for 20 fused images on two datasets with different pooling methods. The best values are indicated in blue.  
in generator, (2) Use the new loss as adversarial loss in generator only. These weight maps can help achieve our objective of accurately extracting the information from the highly informative regions of infrared as well as visible images. The experiments are conducted on RoadScene and our private dataset to evaluate the performance of our proposed method qualitatively as well as quantitatively. The experimental results indicate that the images fused by the proposed method contain more details and are more vivid. The proposed technique is simple yet effective and achieves better results than the state-of-the-art methods.  
Conceptualization, J.-H.K. and Y.H.; methodology, S.M. and J.-H.K.; software, S.M.; validation, S.M. and J.-H.K.; writing--original draft preparation, S.M.; writing--review and editing, Y.H.; visualization, S.M.; supervision, J.-H.K. and Y.H.; project administration, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Grand Information Technology Research support program (IITP-2020-0-01462), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1077110).  
The authors declare no conflict of interest.
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 4 Performance Evaluation
### Observation  
The primary observations from the experiments above are multi-fold, which are listed below:  
1. Hazard-type detection achieves statistically high performance over hazard-type (very high accuracy on tornado-wind scenes, high on the earthquake scene, and weak on the Tsunami-wave scene), and the best model architecture is \(M_{HL}(FRC,RN)\).
2. Damage-level prediction retains moderate yet _explainable_ performance; nonetheless, the model \(M_{DL}(FRC,RN)\) secures acceptable performance on minor- and detecting major-damage level.
3. The bounding-box-based detection is overall satisfactory and sufficiently captures the attention zones in disaster-scene images.
4. Regarding the three-model architecture for the two disaster-scene understanding tasks, it is observed too that Faster R-CNN as a general object-detection architecture with outputs of both bounding boxes and class labels has a superior performance.  
More explanations and limitations in the proposed methodology framework are discussed in the following.
## 2 Literature Review
### Spatial Analysis Methods  
The previous studies on travel behavior commonly employed Ordinary Least Square (OLS) regression to investigate influencing factors [60]. Other methods, such as discrete choice models, spatial clustering, and machine learning algorithms, have also been used under various research contexts [77; 78; 79]. However, these approaches often assume that all the explanatory variables remain spatially stationary across the study area [80; 81]. In reality, travel distribution varies across urban spaces and is influenced by built-environment characteristics, resulting in substantial spatial heterogeneity. To address this issue, empirical research has introduced local models that account for spatial non-stationarity, most notably the geographically weighted regression (GWR) model [60; 72; 82; 83; 84]. GWR uses a spatial weighting matrix that locally calibrates model parameters for each observation, capturing the spatial variations in the estimated coefficients. Recognizing that spatial heterogeneity can manifest at different scales, some scholars have proposed the semi-parametric geographically weighted regression (SGWR) model, which categorizes influencing scales into global or local [85; 86; 81]. Nevertheless, SGWR has certain limitations in refining spatial de tail. More recently, the multi-scale geographically weighted regression (MGWR) model has gained traction [71; 73; 87]. Unlike traditional GWR, MGWR allocates a distinct bandwidth to each variable as an indicator of its spatial scale, enabling a better fit for scenarios where the underlying processes operate at multiple scales. Despite these advances, the MGWR model also has limitations, such as increased computational complexity and potential multicollinearity, which require careful parameter tuning and model diagnostics, which are often underreported in empirical applications.
# Intelligent Communication in Wireless Sensor Networks
## 1 Introduction
In other words, an agent has been integrated in each sensor node, in such a way that each node of the network is seen as an autonomous agent having its own characteristics and behaviors towards the various events they receive. A mobile agent is used to reduce the cost of communication dramatically, in particular, the elimination of redundancies between source nodes, by moving information processed by an agent introduced into each sensor instead of bringing them to the sink in the raw state. In addition, the SN cooperates with these neighbors to ensure the movement of the MA. Therefore, these agents (introduced in the nodes) eliminate the redundant information contained in the MA and the sensor by itself. Iteratively, the network was partitioned into sectors [3] where the SNs in each sector are included in an itinerary. This technique makes it possible to find an optimal itinerary for an MA to collect data from several distributed sensor nodes. The SN within each sector can be obtained by choosing the angle in an adaptive itinerary. The main purpose of this technique is to group data by a mobile agent and send them at a time to minimize transmission, instead of each sensor sending their data. To collect data in each area, we have created an itinerary algorithm that allows the mobile agent to:  
* Reduce the response time;
* Travel the route from the furthest sensor to the sink;
* Start aggregating data from the SN as much as possible until they reach the sink;
* Consume less energy on the route back to the base station;
* Avoid exhausting energy when visiting the SN;
* Decrease the chances of being lost due to energy shortage from the SN; and
* Use neighboring sensors to help SNs that have less energy. The rest of the article is organized as follows: Section 2 provides an overview of the literature in which algorithms for a number of studies have been performed regarding routing in the WSN. Inthe first place, we describe the classification of proposed routing solutions, and then the solutions proposed in the integration of mobile agents in the directed diffusion protocol, before presenting the statement of the problem. In Section 3, we present the description of our communication strategy. The packet structure of each mobile agent for itinerary planning and cooperation in information gathering are described in Sections 4 and 5, respectively. Next, Section 6 sets out the purpose of our application, which is to establish a system to simulate the communication between a set of sensors and a base station. Next, Section 7 is devoted to the evaluation of our proposal through simulation, while Section 8 analyzes the results of our approaches compared to other approaches, according to several criteria. Finally, Section 9 summarizes and concludes this article.
## 1 Introduction
Some previous studies evaluated RT-induced xerostomia with different MRI techniques, such as DWI [21; 30; 31; 32] and DCE-PWI [33]. However, neither used both techniques on the same cohort of patients in pre- and post-RT MRI examinations. Furthermore, the texture analysis was mainly performed with CT imaging [34; 35; 36], and only one paper can be found on ultrasound [37] and conventional T1 MRI sequences [38]. The current study represents the first attempt to evaluate RT-induced xerostomia by using multiparametric MRI techniques, including DWI, DCE-PWI, and texture analysis,carried out in both pre- and post-RT imaging. The aim of this retrospective study was to correlate such MRI techniques with the severity of RT-induced xerostomia clinically confirmed with the Common Terminology Criteria for Adverse Events (CTCAE).
## 3 Methods
### Broadband Dielectric Spectroscopy  
Broadband Dielectric Spectroscopy (BDS) is a modern diagnostic method which allows interconnecting several measurement techniques to obtain a comprehensive view of the material behavior under an electric field with a frequency in a very wide range. For this investigation, the main diagnostic unit of the Alpha-A measuring device (Novocontrol Technologies) has been used. It contains a frequency response analyzer with a sinusoidal signal generator. The presented analysis was performed in the temperature range from \(-50\)\({}^{\circ}\)C to \(60\)\({}^{\circ}\)C and in the frequency range from 0.5 Hz to 1 MHz.
## 5 Experimental Results and Evaluation
### Performance Evaluation
#### 5.2.1 End-to-End Latency and Gas Usage by Data Authorization  
We scaled up \(N_{m}\) in the data authorization scenarios to evaluate how the size of the ordered list of digests (Swarm hash) impacts the performance. As a transaction's committed time is greatly influenced by the Blockchain confirmation time, we observed that all data authorization operations with different \(N_{m}\) demonstrated almost a similar end  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Device** & **Cloud Server** & **Desktop** & **Raspberry Pi 4 Model B** \\ \hline \multirow{3}{*}{CPU} & Intel(R) & Xeon(R) & \multirow{3}{*}{Intel Core TM i5-3470} & \multirow{3}{*}{Broadcom ARM Cortex} \\  & Gold 5220R CPU @ & & (4 cores), 3.2 GHz & A72 (ARMv8), 1.5 GHz \\ \hline \multirow{3}{*}{Memory} & 512 GB DDR4 & 16 GB DDR3 & 4 GB SDRAM \\ \hline \multirow{3}{*}{Storage} & 4 TB HHD & 500 GB HHD & 64 GB (microSD) \\ \hline \multirow{2}{*}{OS} & Ubuntu 20.04 & Ubuntu 20.04 & Raspbian (Jessie) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of experimental nodes.  
Figure 3: The experimental setup and network configuration.  
to-end latency (about 4 s) in our private Ethereum network. Regarding the computational complexity and processed data required by the SC, the gas used by the transactions may vary. Figure 4 shows the gas usage by data authorization transactions as \(N_{m}\) increases. The longer the ordered list of digests, the more gas is used per each transaction that stores the data on the Blockchain. Hence, recording the Swarm hash, rather than the meta-data or even the raw data on the distributed ledger, can greatly reduce the gas consumption of the Blockchain transaction.
# PID Control Assessment Using L-Moment Ratio Diagrams
## 2 Applied methods
### Moment Ratio Diagrams  
Moment ratio diagrams graphically present empirical or theoretical statistical properties of given data in a two-dimensional plane. Practically, we use two versions of MRD diagrams [41]. A MRD(\(\gamma_{3}\), \(\gamma_{4}\)) presents the relationship between the third \(\gamma_{3}\) moment (or its square \(\gamma_{3}^{2}\) in some realizations) plotted as the abscissa and the fourth one, \(\gamma_{4}\) drawn as the ordinate. The kurtosis is often drawn upside down. One should remember that there is a theoretical constraint on the accessible area on a diagram, due to the following relationship:  
\[\gamma_{4}-\gamma_{3}^{2}-1\geq 0. \tag{7}\]  
Each distribution function can be represented by a point, curve, or region. This representation depends on the number of PDF shape parameters. Distribution functionsthat do not have shape factors (like Gauss, Laplace, or Cauchy) are reflected by a single point. The functions that have one shape factor, like the generalized extreme value (GEV), are drawn as a curve. Regions reflect distributions parameterized by two shape factors, like the \(\alpha\)-stable distribution of four parameter kappa (K4P). The second type of diagram, i.e., the MRD(\(\gamma_{2}\), \(\gamma_{3}\)), relates the variance \(\gamma_{2}\) drawn as the abscissa with the skewness \(\gamma_{3}\) at the ordinate. However, this diagram is location- and scale-dependent, which makes it less useful.
# UAV Formation Trajectory Planning Algorithms: A Review
## 3 Traditional Algorithm
### Dubins Curve  
The Dubins curve is the shortest locus connecting two two-dimensional planes (i.e., the X-Y plane) under the condition that the curvature constraint is satisfied and the tangent directions of the specified beginning and end are satisfied. In 1957, [PERSON] proved that any locus can consist of a maximum curvature arc or straight segment (the locus between two points must exist). In other words, the shortest path connecting two points will be constituted by the circular arc of the maximum curvature and the straight line segment. The Dubins of any starting point to the end point are composed of not more  
Figure 5: A cost diagram of a Dijkstra algorithm (A–G: nodes; lines: trajectories; numbers: the distance between vertices).  
than three original motions, and the sequence constituted by the three original motions is called a kind of trajectory. As two continuous and identical primordial motions can be combined into one primordial motion, [PERSON] proved that the optimal trajectory can only be one of the following six combinations: namely, RSR, LSL, RSL, LSR, RLR, LRL. The first four are collectively called a CSC trajectory, and the last two are collectively called a CCC trajectory, where the primordial motion R represents right turn, S stands for straight and L stands for left. Figure 6 is the trajectory diagram of one Dubins curve LRL.  
[PERSON], [PERSON], and [PERSON] [17] modeled the UAV as a Dubins vehicle, using a method based on the Reduced Visibility Graph (RVG), connecting selected nodes by arcs and segments, and adding the Rendez-Vous Waypoints (RVWs). It was based on the leader-follower Stackelberg model's two-layer game theory method to optimize the location of the trajectory point and the trajectory of the UAV as much as possible in order to find the optimal trajectory while maintaining the shape of the formation in many places.
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 5 Performance Evaluation
### Evaluation on the GTOT Dataset  
We first compared the performance of RMFNet with 12 RGB trackers to verify the necessity of incorporating thermal infrared modality information. The trackers include KCF [28], ACT [29], ECO [30], ACFN [31], CFnet [32], MDNet [12], RT-MDNet [33], SiameseFC [16], SiamDW [34], BACF [35], SRDCF [36], and DAT [37]. The results are shown in Figure 6(a), and it is clear that RMFNet outperforms the above RGB tracking algorithm, which fully demonstrates the complementary nature of thermal infrared modality to the visual modality. In particular, RMFNet outperforms MDNet and DAT in PR/SR by 9.9%/13.6% and 14%/15.1%, respectively.  
Figure 7: PR (%) and SR (%) for different trackers in the GTOT dataset.  
In addition, we conducted a comparison experiment between RMFNet and 14 state-of-the-art RGBT trackers, including SiamDW+RGBT [34], RT-MDNet+RGBT [33], MDNet+RGBT [12], DAT+RGBT [37], SGT [38], CMR [39], JMMAC [40], MANet [13], DAFNet [20], DAPNet [1], MACNet [41], APFNet [4], and HIDNet [42]. The first four trackers are RGB trackers extended to RGBT. SGT and CMR are traditional RGBT trackers, while the latter six are RGBT trackers based on the MDNet framework. We present the evaluation results in Figure 7b. According to the curve, it can be observed that RMFNet achieves the best performance compared to these RGBT trackers, reaching 91.1%/76.9% in PR/SR. In particular, RMFNet's PR/SR is 1.7%/4.5% and 0.6%/3.2% higher than MANet and APFNet, respectively.  
Finally, the GTOT dataset can be divided into 7 challenge attributes, namely thermal crossover (TC), fast motion (FM), low illumination (LI), deformation (DEF), small object (SO), and large scale variation (LSV). We combined RMFNet with SiamDW+RGBT [34], RT-MDNet+RGBT [33], MDNet+RGBT [12], SRDCF [36], SGT [38], RT-MDNet [33], CMR [39], ECO [30], MANet [13], DAPNet [1], MACNet [41], and APFNet [4], 12 trackers were compared for their evaluation results in these 7 challenge attributes. As shown in Table 1, our proposed algorithm performs well under various challenging attributes.