esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Summary of the Contributions
### Forecasting  
Stock performance prediction is one of the most challenging issues in time series data analysis. Paper [19] proposes to build an automated trading system by integrating AI and the proven method invented by human stock traders. The knowledge and experience of the successful stock traders are extracted from their related publications. After that, an LSTM-based deep NN is developed to use the human stock traders' knowledge in the automatic trading system. Experimental results indicate that the proposed ranking-based stock classification considering historical volatility strategy outperforms conventional methods.  
In [20], the authors study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. For the improvement of the forecasting accuracy of Bitcoin's volatility, they develop hybrid forecasting models combining the GARCH family models with the ML approach including NNs.  
Paper [21] is about forecasting the Key Performance Indicators (KPIs), usually in the form of time series data, related to the COVID-19 pandemic. Making reliable predictions of these indicators, particularly for emergency departments, can facilitate acute unit planning, enhance the quality of care and optimise resources. The authors compare the KPI forecasting models including classical ARIMA, Prophet and General Regression NN.  
A development of the intelligent transport systems has created conditions for solving the supply-demand imbalance of public transportation services. In [22], a method to forecast real-time online taxi-hailing demand is introduced. It is based on NNs and extreme gradient boosting. The proposed method can help to schedule online taxi-hailing resources in advance.  
Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. In [23], for weekly prediction of heat-related damages, a random forest model was developed using statistical, meteorological, and floating population data. The results show that the proposed model outperforms existing ones.  
One of the hottest topics in today's meteorological research is weather nowcasting, which is the weather forecast for a short time period such as one to six hours. With the main goal of helping meteorologists in analyzing radar data for issuing nowcasting warnings, in [24], a regression model based on an ensemble of deep NNs for predicting the values for radar products is proposed. The proposed model is intended to be a proof of concept for the effectiveness of learning from radar data relevant patterns that would be useful for predicting future values for radar products based on their historical values.
## 3 Experimental Test Results and Analysis
### Flexural Strength  
Figure 8 depicts the effect of OWA on the flexural strength of cement pastes for all partial replacement levels after 1, 7, 28, and 90 days. To further assess the evolution of the flexural strength in dependence on the control paste, the rates of variation in the strength are monitored in Figure 9. The trend in flexural strength aligns closely with the observed pattern of compressive strength. For instance, the 90-day flexural strength varies between 4 and 2.5 MPa for pastes regardless of OWA levels. The drop reaches its peak rate for the addition of 20%, having a rate of 37%. However, among all OWA mixtures, it is noticed that the paste containing 10% of OWA exhibits the highest value, showing the lowest decline  
Figure 7: Evolution of compressive strength parameters (IRCS and UCS).  
rates of 3% and 5% after 28 and 90 days, respectively. The loss of flexural strength can be related to the reduced cement content in the mixture, which is required for reinforcing the material [28; 29]. Another explanation for this decline is that this ash diminishes or weakens the bonding between OWA and the cement particles, which is accompanied by an increase in the number of voids in the paste [29].
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks  
Drones connected to cellular networks will be a vital infrastructure that offers a wide range of services in various environments. The necessity for stable communication during their movement is a major challenge that must be emphasized. Several challenges arise with the implementation of connected drones due to tier connectivity and movement characterizations. Handover issues would result in high handover rates [74; 75; 76], which would lead to a large ping-pong effect [77], or a high rate of Radio Link Failures (RLFs) [78] or Handover Failures (HOFs) [30; 79; 80; 81; 82; 83; 84; 85]. RLFs and HOFs are both significant key performance indicators in mobile networks. Both may increase due to the high speed of mobile users, the suboptimal settings of handover control parameters, inefficient handover decisions, and other related factors.
# Fitness Landscape Analysis of Product Unit Neural Networks
## 7 Empirical Analysis of Loss Surface Characteristics
### Regularized Architectures  
For the purposes of this section, the loss surfaces produced by regularized PUNNs are referred to as regularized PUNN landscapes (RPLs). The only noticeable effect that regularization has on the fitness landscapes of a PUNN is changes in the gradient measures. RPLs have larger magnitudes of gradients, as indicated by larger \(G_{avg}\) values. Larger gradients are caused by the addition of the penalty term to the objective function, which increases the overall error and causes larger loss values and, thus, larger gradients. Additionally, larger \(G_{dev}\) values indicate that regularization creates sudden ravines and valleys in the landscape, possibly introducing more local minima. The regularization coefficient \(\lambda\) has a severe effect on the landscape [14; 21]. However, a value of \(\lambda<0.001\) (for SUNNs) is not likely to influence the error landscape significantly [21]. Referring to Table 2, the optimal value obtained from tuning the penalty coefficient was \(\lambda=0.0001\) for all problems. This was most likely due to the fact that a smaller value for \(\lambda\) made the contribution of the penalty term insignificant to the overall error. Therefore, as a result of the small optimal value used for \(\lambda\), no other significant changes to the fitness landscape were detected by the fitness landscape measures besides \(G_{avg}\) and \(G_{dev}\). Results for SGD are not provided because it failed to train PUNNs for all problems. SGD only succeeded when the weights were initialized very close to the optimal weights. The reasons behind the failure of SGD can now be understood using FLA: It was observed that the average gradients \(G_{avg}\) for PUNN loss surfaces were exceptionally large and were orders of magnitude larger than those of SUNN loss surfaces. The standard deviations \(G_{dev}\) for PUNN loss surfaces were also very large--indicative of sudden ravines or valleys in the PUNN loss surfaces. These characteristics trap or paralyze SGD. Larger values of \(G_{dev}\) suggest that not all the MRWs sampled such extreme gradients. Taking into consideration that the longest neutral areas of both SUNN and PUNN loss surfaces tend to be the same size, only certain parts of the PUNN loss surface have extreme gradients, whereas some areas are still relatively level. Such loss surfaces are impossible to search using gradient-based algorithms. \(G_{avg}\) and \(G_{dev}\) are the only measures that differ substantially between PUNN and SUNN loss surfaces. Therefore, the gradient measures are likely to be the most relevant fitness landscape measures that explain why SGD works for SUNNs and fails for PUNNs. Smaller \(\overline{\mathcal{E}}_{T}\) values were obtained for OPLs compared to CPLs for all classification problems. Note that the dimensionality difference between OPLs and CPLs is the most significant for the classification problems. Loss surfaces with larger bounds--hence, larger
# Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets
## 2 Materials and Methods  
### Calpuff  
CALPUFF is a non-steady-state Lagrangian Gaussian puff model [34] in which individual puffs of pollutants are released and allowed to grow horizontally and vertically, spreading outward from the plume's centerline, following a normal statistical distribution, according to the following equations [34]:  
\[C(x,y,z)=\frac{Q}{2\pi\sigma_{x}\sigma_{y}}g\left[exp\left(-\frac{1}{2}\left( \frac{d_{e}}{\sigma_{x}}\right)^{2}\right)\right]\left[exp\left(-\frac{1}{2} \left(\frac{d_{e}}{\sigma_{y}}\right)^{2}\right)\right] \tag{1}\]  
\[g=\frac{2}{\sigma_{z}\sqrt{2\pi}}\sum exp\left[\frac{-(H_{e}+2 nh)^{2}}{2\sigma _{z}^{2}}\right] \tag{2}\]  
where \(C\) [g/m\({}^{3}\)] is the concentration at (\(x\), \(y\), \(z\)) location; \(Q\) [g] is the mass of pollutant in the puff; \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{z}\) [m] are the Gaussian distribution standard deviations in the along-wind,cross-wind, and vertical directions; \(d_{a}\) and \(d_{c}\) [m] are the distances from the puff barycenter to the receptor in the along-wind and cross-wind directions; \(H_{e}\) [m] is the height of the puff barycenter with respect to the ground; and \(h\) [m] is the mixing-layer height.  
The Lagrangian puff formulation simulates the plume as a sequence of Gaussian puffs that move and disperse into the atmosphere in response to time and space-varying local conditions. The US-EPA [35] recommends CALPUFF as an alternative model for near-field transport (i.e., <50 km), as it contains modules of algorithms for complex topography, overwater dispersion, building downwash, wet and dry deposition, and chemical reactions. Moreover, CALPUFF, unlike traditional Gaussian plume models, is also able to simulate calm wind conditions, which is crucial for estimating odor impact: this condition is characterized by a low capacity of the atmosphere to disperse pollutants, making the occurrence of odor events likely.  
In fact, the US-EPA recognizes that CALPUFF [36], compared to the preferred model AERMOD or other Gaussian plume models, may be more appropriate in specific situations (e.g., in the case of complex topography).  
Furthermore, CALPUFF has been widely used and accepted by regulatory agencies for environmental impact assessments, permitting, and compliance purposes. It has a well-established user base and a history of regulatory acceptance in many jurisdictions.  
The model is coupled with the meteorological processor CALMET, which provides 3D diagnostic wind and temperature fields for a CALPUFF dispersion simulation.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Machine Learning Algorithms for Fault Detection
#### 3.2.4 K-Nearest Neighbors (KNN) Algorithm  
The classification performance is stable and the KNN approach is straightforward and simple to apply. However, when the sample is imbalanced and the sample dimension is too large, the algorithm performs poorly in terms of operational efficiency and classification impact. Numerous researchers have made improvements to their data or algorithms in this area [25; 26; 27; 28; 29; 30; 31; 32; 33].  
Figure 2 illustrates the KNN-based Transformer failure diagnosis procedure. In this process, mapping Transformer fault samples using the NCA model's output findings (metric matrix) is referred to as NCA transformation. In the process, the enhanced NCA method suggested in this research is applied.  
Figure 2 depicts the KNN-based diagnostic process for transformer fault detection. FRA samples are mapped using the NCA model, followed by feature transformation and distance-based classification. The enhanced NCA method ensures accurate mapping and robust fault detection. Annotations highlight the role of each stage in optimizing the diagnostic process.
## 4 Experiments and Results Discussion  
An initial comparison between YOLO v3 and different techniques of object detection were evaluated. They included methods based on feature extraction such as SIFT [27], SURF [28] or ORB [29]. The matchers used with SIFT, SURF and ORB were respectively FLANN, BF, and Hamming KNN [49]. YOLO was chosen because the results were better when faced with new images (70.06% vs. 61.16% SIFT, 17.85% SURF and 56.70% ORB) and, in addition, its processing time was much faster (0.15 s vs. 16.75 s SIFT, 1.24 s SURF and 1.61 s ORB). Moreover, when the training set grows, SIFT, SURF or ORB processing times also increase because these methods must match the input image descriptors with all the image descriptors in the database to get the closest match. YOLO has no such problem, since once the model is trained, its runtime remains stable regardless of the size of the dataset. However, YOLO v3 needs a heavy initial training. One problem with the shabtis is that there are not many known shabtis for each owner. In some cases, it is possible to find more than 50 examples, such as Seti I or Pinudjem I, but for most of them, less than 10 examples are usually found in museums or private collections. In some cases, such as Pa-khonsu, only one example is known. To avoid overfitting of the network because of this particular situation, data augmentation is carried out during the training, creating random images obtained by changing the parameters of the original images, such as the saturation, exposure or hue. The list of shabti owners used for the training of the FN is shown in Table 3. Some people had the same name, although it could be written in a different manner, as [PERSON] and [PERSON] (Queen), who were two different persons. The reason is because there were different hieroglyphs with the same phonetic sound and even transliteration in our language. Other owners had completely different shabtis produced in several materials. For these cases, the category has been decoupled to make the network split the objects more accurately. Some examples are the famous [PERSON] (Faience and Wood), [PERSON] (Limestone and Wood), or [PERSON] (Alabaster, Stone and Wood). In total, there are 158 categories for 151 different shabti owners. 64 out of these 158 categories were used to train the HN, since the names of hieroglyphs could only be read for images of 64 classes. 815 images have been used, respectively, for the FN and HN trainings. 354 images have been used for the tests.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 6 Conclusions  
This paper proposes an improved hybrid A* algorithm with lemming optimization for autonomous vehicle path planning, incorporating the LOA to enhance the safety, smoothness, and search efficiency in trajectory generation. By integrating a penalty-based graph search with Reeds-Shepp curves, the proposed algorithm obtains collision-free paths with continuous curvature while maintaining a safe distance from obstacles. Furthermore, it effectively balances the global optimization capabilities of the LOA with the motion-aware local search of the improved hybrid A* algorithm to avoid local optima traps. The simulation results show that, in simple and complex scenarios, the improved hybrid A* algorithm can shorten the time by 33.2% and 23.5% and the path length by 11.1% and 13.6%, respectively, compared with the traditional hybrid A* algorithm. These findings confirm that the proposed approach excels in solving target path planning problems, combining rapid convergence with high-quality path generation, and exhibits strong potential for real-world applications.  
Although the proposed lemming-optimized hybrid A* algorithm demonstrates significant improvements in safety, smoothness, and search efficiency, there remains room for enhancement, with several promising research directions.  
(1) Although this method can reduce unnecessary searches on the path and shorten the search time, the proposed algorithm does not significantly improve the path length. Therefore, in future work, we will adjust the evaluation criteria to improve their suitability and introduce stricter penalty mechanisms to prevent the generation of excessively long paths.  
(2) The current study assumes a static environment, which does not align with the real world. Therefore, in future work, the framework could be extended through online parameter adjustments to handle real-time dynamic obstacles and uncertain road conditions, enabling dynamic obstacle avoidance.  
Conceptualization, Y.C. and Y.L.; methodology, Y.L. and Y.L.; software, Y.L.; validation, Y.C. and Y.L.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.C.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.C. and W.X.; visualization, Y.L.; supervision, Y.C. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Project for capacity construction of science and technology innovation service, Beijing laboratory construction, Beijing Laboratory for New Energy Vehicles (PXM2021_014224_000065).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We would like to thank all participants and collaborators who contributed to this study.  
The authors declare no conflicts of interest.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Verification of the Model  
The location and movement of the nodes has been checked using the network animator (NAM) tool that comes with ns-2. By overlaying NAM screenshots on a map of the area (Figure 11), the positioning of the four fixed nodes (labelled Elsam Traffic, Snaefell, South Stack, and Crosby) and the route taken by the ships are reaffirmed. This is further verified by matching with the displays from the ShipAIS website for the simulated period.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 4 System Design
### Virtual Antenna Array Direction of Arrival Estimation
Since the beam width of the spatial spectrum gradually decreases with the increase of the number of array elements, the resolution of the DOA algorithm is enhanced. However, increasing the number of elements will have a relatively small effect, as the number of elements in an array is very large. Therefore, under the premise of ensuring the estimation performance of the algorithm, the selection of a suitable number of sampling points for DOA estimation should be considered comprehensively.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 3 Conventional Two-Stage Carrier Tracking Strategy  
Carrier estimation consists of carrier acquisition and carrier tracking. Carrier acquisition is a search process, and the frequency search step is typically set to 666.67 Hz. Therefore, the maximum frequency error of the acquisition results is 333.34 Hz. Due to the large frequency error, it takes a long time to track the signal. To decrease the tracking time and achieve fast convergence, it is necessary to design a two-stage carrier tracking control strategy, which was presented in [12].  
Figure 1 shows the block diagram of the conventional two-stage carrier tracking strategy. This architecture contains both coarse tracking and fine tracking. Hereafter, each part of the architecture is presented in detail.
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 2 Compression and Decompression Algorithms Based on Hybrid Strategies
### Strategies
#### 2.3.1 Predictive Coding  
This paragraph elucidates a predictive coding strategy that employs gradient quantization and Golomb-Rice coding to efficiently encode and decode pixel component, enhancing image compression by dynamically adjusting based on local component characteristics. As detailed in Figure 3, the predictive coding strategy incorporates a sequential workflow that begins with the computation of gradient values G1, G2, and G3, which are then quantized into Q1, Q2, and Q3 using predefined quantization rules, as shown in Formula (1). The algorithm initializes two significant arrays: A, with a length of 730 elements set to zero, representing the accumulated error values; and N, also 730 elements long but initialized to one, signifying the count of occurrences. These arrays play an integral role in updating the predictive model and are meticulously adjusted with each new Golomb-Rice-coded value that is computed. The update mechanism dictates that for each quantized gradient mQ, the N array at the index mQ is incremented by one, and the A array at the same index is increased by the magnitude of the prediction error mErrval.  
Figure 2: Process of the decompression algorithm.  
\[Q_{i}=\begin{cases}-4&G_{i}\leq-21\\ -3&-21<G_{i}\leq-7\\ -2&-7<G_{i}\leq-3\\ -1&-3<G_{i}<0\\ 0&0\leq G_{i}<0\\ 1&0\leq G_{i}<3\\ 2&3\leq G_{i}<7\\ 3&7\leq G_{i}<21\\ 4&21\leq G_{i}\end{cases} \tag{1}\]  
During the decompression phase, when the decompressor encounters the flag bit '1B', it activates the inverse Golomb-Rice coding process. This involves the recalibration of the prediction error mErrval, which is then applied alongside the calculated gradient values to reconstruct the original pixel component. By incorporating gradient quantization and Golomb-Rice coding, this predictive coding strategy achieves a dynamic and adaptive compression method that responds to the localized component characteristics within the image, enhancing compression efficiency.
# Microstructure-Based Prediction of the Elastic Behaviour of Hydrating Cement Pastes
###### Abstract  
The development of the elastic properties of a hardening cement paste results from the microstructural evolution due to cement hydration. The elastic behaviour of cement paste can be predicted by a combination of the hydration model and micromechanical analysis, which originates from a microstructural representative volume where the elastic behaviour of different hydrating cement products can be recognised. In this paper, the formation of the microstructural volume is simulated with the computational code HYMOSTRUC3D for cement hydration. The obtained microstructure is an input for a micromechanical modelling. A 3D regular lattice model is proposed to predict the elastic modulus of the microstructure, considering a water-to-cement (_w/c_) ratio within the range [0.30-0.50]. In addition, the Finite Element Method (FEM) is used to compare and validate the results from the lattice model. Predictions from these two modelling approaches are then compared to the experimental results provided by the EMM-ARM (Elasticity Modulus Measurement through Ambient Response Method) testing technique, the latter allowing measurement of the elastic modulus of hydrating cement pastes. Finally, the above-referred numerical models are used to evaluate the influence of the following features: the particle size distribution of the cement grains, the microstructure discretisation refinement and the elastic modulus of the C-S-H cement hydration product.  
paste microstructure; HYMOSTRUC3D; lattice; FEM; elastic behaviour Article