esa-sceva/satcom-chunk-collection huggingface dataset:
### Related Work  
In the last decade, many efforts have been made to reduce the effects of multipath in the PVT solution. Actually, there are many techniques to do so, ranging from the classical narrow or strobe correlators [1] to more sophisticated techniques such as the MEDLL [2]. These techniques are either not designed to mitigate carrier phase effects or are usually too complex to be implemented in mass-market receivers. Other schemes include site-dependent techniques trying to model the multipath propagation with in-site multipath calibrations [3] or with the use of external information such as cameras [4]. Unfortunately, these techniques are only suited to static receivers with very specific multipath propagation or they need additional hardware/components that are not usually available in traditional GNSS receivers.  
Furthermore, none of the techniques listed above completely eliminate the effects of multipath or suit all GNSS applications. The most effective techniques are those based on antenna arrays [5], which can cover most multipath situations. Nevertheless, antenna arrays are often not suited in some kinematic applications due to the size of the array, which is usually bulky. In this regard, multipath mitigation techniques using dual polarization antennas may provide a good alternative due to the reduction of size, as they have the same size as a single antenna element. The first studies of dual polarization antennas for GNSS multipath mitigation can be found in Refs. [6; 7; 8], which consider in-lab experiments or simulation demonstrations. It was not until some years later, though, that real-world conditions were analyzed in Refs. [9; 10; 11]. These works demonstrated for the first time the capability of dual polarization antennas to detect Non-Line-of-Sight (NLOS) and then to improve the positioning accuracy under real working conditions.
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 5 Discussion
In this study, the contribution of each conditioning factor to landslide occurrence was assessed using the certainty factor (CF) method, which enables range-based interpretation of variable influence. This approach allowed for a more nuanced understanding of how specific categories, such as slope aspect or lithology type, affect landslide probability--offeringinterpretable, domain-relevant insights essential for hazard planning. While alternative techniques like partial dependence plots (PDPs) or SHAP values could further improve model transparency, especially from a machine learning interpretability perspective, future research may benefit from integrating such tools to complement CF-based geomorphological reasoning [87]. Moreover, while this study employed a structured grid search approach for model optimization due to its transparency and computational feasibility, especially in resource-limited contexts, we acknowledge the growing value of advanced hyperparameter tuning methods. Techniques such as Bayesian Optimization, Genetic Algorithms, and Particle Swarm Optimization have shown promise in recent studies for enhancing model performance in complex geohazard applications [87]. Although not adopted here due to practical constraints, integrating these metaheuristic algorithms could be a valuable direction for future research--particularly when working with larger datasets or in multi-objective scenarios demanding greater computational precision and efficiency. Another point that should be mentioned is the possibility of the risk of autocorrelation concerning the distribution and selection of training and validation landslides. To minimize data leakage, each landslide sample in this study was derived from the centroid of a unique landslide polygon, ensuring no spatial overlap between training and validation datasets. This approach reduces the risk of autocorrelation and maintains dataset independence. However, we recognize that applying spatial cross-validation methods, such as k-fold blocking or buffer-zone partitioning, could further improve model robustness and will be considered in future research.
## 5 Discussion
### Potential Improvements on NavACL-Q  
We have indeed verified the effectiveness of our automatic curriculum learning approach _NavACL-Q_ in Section 4.3.1. Nonetheless, we still spot some cases where _NavACL-Q_ may fail despite the improvements on the original _NavACL_ algorithm. This happens when the agent fails even in the initial optimistic regions quite often, ending up with much more negative samples than positive ones. As a result, _NavACL_ cannot distinguish _easy_ tasks from _frontier_ ones or _random_ tasks due to severe class imbalance problems. Under these circumstances, the initial poses with low success probability are interpreted as _easy_. Consequentially, the curriculum fails to propose beneficial intermediate tasks, and is behaving similarly to random starts.  
One potential solution to this issue is to start curriculum proposing when the agent performs sufficiently well on the favorable initial state. With guaranteed success on a favorable initial task, the _NavACL_ algorithm can distinguish _easy_ task from _frontier_ task. This idea will be investigated in our future work.  
The other improvement is the generalization of _NavACL-Q_ to be domain independent. The current input of the success prediction network \(f_{\pi}\) still considers the domain dependent properties such as distance to goal and initial rotation, which requires additional manual design. A more meaningful approach is to leave out domain-dependent handcrafted features and to retain only domain-independent ones, for instance, \(Q\)-value of the initial state-action pair, which will be used in most DRL algorithms. Such settings will easily generalize _NavACL-Q_ to other tasks, which is worth future investigation.
## 5 Performance Evaluation
### Real Dataset
#### 5.1.4 Node Contribution Rate  
In this evaluation, we try to examine the node contribution rate \(\alpha\) to structure fidelity in order to provide a way to determine the fidelity threshold in practice. Figure 6 shows the structure fidelity when all 52 nodes are added into the active nodes set one by one. It can be observed that the fidelity monotonically increases with the active node ratio (ANR), which is the ratio of active nodes to all nodes. Consequently, the fidelity is optimal (\(100\%\)) when the ANR is equal to 1. This is no surprise because more selected active nodes means smaller distortion. Based on Definition 1, we calculate the contribution rate for different number of nodes. The result is given in Table 1. The initial 22 active nodes with \(42.31\%\) ANR contribute \(90.56\%\) of fidelity, while the subsequent three active node sets only provide \(6.42\%\), \(2.46\%\), \(0.56\%\) of the whole fidelity, respectively. For the evaluated scenarios, therefore, \(61\%\) of nodes need to work when the target fidelity is set to a high value \(96\%\). That means it is possible to save \(39\%\) energy on nodes dissipation while only losing \(4\%\) fidelity.  
Based on above analysis, we can draw the conclusion that the spatial redundancy in the experimental scenario is high since last 30 nodes representing about \(58\%\) of all nodes account for less than \(10\%\) of fidelity. Thus, a way is introduced for evaluating the level of nodes redundancy. For the WSNs with high spatial redundancy, the satisfying energy saving can be achieved while the expected threshold is met.  
\begin{table}
\begin{tabular}{l c c c c} \hline Number of Nodes & 1–22 & 23–32 & 33–42 & 43–52 \\ \hline Nodes contribution rate & 0.9056 & 0.0642 & 0.0246 & 0.0056 \\ \hline ANR & 0.4231 & 0.1923 & 0.1923 & 0.1923 \\ \hline \end{tabular}
\end{table}
Table 1: Nodes contribution rate with different active node rate.  
Figure 6: Structure fidelity _vs_. ANR.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 2 System Model
### Performance Metrics
#### 2.4.2 Area Spectral Efficiency (\(ase\))  
\(ASE\) is a measure of spectral reuse efficiency in the network and is defined as the sum of the average data rates per unit bandwidth normalized with the total service area (bits/sec/Hz/Km\({}^{2}\)), given by the following,  
\[ASE=\frac{\sum_{k}L_{k}R_{k}}{A}E[log_{2}(1+SINR^{(nj)k})]. \tag{14}\]  
A higher value of \(ASE\) implies a higher achievable sum rate for the network, which allows a greater number of users to get service from the network.
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
## 2 Material and Methods
### Comb Filters
It has to be emphasized that the RMS signal recovery operates as a type of low-pass filtering depending on the length and the offset of the moving window. The longer length, the stronger the low-pass filtering. Choosing the appropriate values of length and offset is a compromise between the attenuation of EMG signal, the complexity of calculus, and the overall processing algorithm's execution time. This is crucial for real-time applications.
## 4 Results and Discussion
### Cost, Energy, and Throughput Analysis
#### 4.5.2 Build Times and Production Rate  
FDM builds averaged 45-55 min, while SLA prints required 90-100 min, including post-curing. Figure 4 illustrates the throughput advantage of FDM, making it ideal for rapid prototyping.
# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 6 Conclusions  
In this paper, we introduced a solution for underwater communications that adaptively modifies communication parameters to minimize disturbance to the natural environment while enhancing energy efficiency. A key characteristic of the proposed approach is that all core operations, including environmental sensing, classification, and communication adaptation, are executed in real-time onboard the software-defined radio platform using the GNU Radio framework. By enabling localized inference and adaptive transmission, the system reduces data volume and optimizes energy consumption. The approach was implemented and validated in a simulated environment, demonstrating its potential for sustainable and energy-aware underwater communications.  
As part of our future work, we plan to conduct real-world field trials to evaluate the framework under actual underwater acoustic conditions, including dynamic channel behavior, mobility, and environmental noise. These experiments will provide critical insights into the system's robustness and scalability and will help refine the adaptive transmission strategies for practical deployment.  
Conceptualization, L.G. and G.M.; methodology, L.G. and A.U.R.; software, A.U.R.; validation, A.U.R. and L.G.; writing, A.U.R., L.G. and G.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE0000001 program \"RESTART\").  
Figure 10: End-to-end delay comparison for BPSK and QPSK.
## 3 Results
### Meteorological Forecasting and Assimilation Performances
#### 3.1.2 Extreme Event Forecasting: Convective Storms
Moreover, the CSI index, which measures the agreement between forecast and observed \"yes\" events, is very similar for both runs and thresholds, with a slight improvement in DA. Overall, DA improves spatial accuracy by refining feature localization and reducing overestimation compared to OL. While OL exhibits a higher Probability of Detection (PODY), it also has a higher False Alarm Ratio (FAR), meaning it tends to overpredict precipitation, leading to rainfall forecasts where none occurred. DA, although more conservative, reduces false alarms and produces a more realistic precipitation field, balancing detection capability with improved precision. Finally, the bottom panels of Figure 8 show the main MODE-derived spatial attributes, comparing WRF OL (blue bars) and WRF DA (green bars). It is observed that the intersection area, representing the overlapping region between observed and forecasted objects, is very similar in both OL and DA, with a slight deterioration for DA. However, looking at the area ratio, defined as the area of the predicted object divided by the area of the observed object and serving as an indicator of overestimation or underestimation, a clear improvement in DA is evident for both thresholds. Again, considering the attribute \"angle diff\", which indicates the angular difference between the axes of the forecasted and observed objects, DA performs better at the 15 mm threshold, while OL shows a smaller angle difference at 10 mm. This suggests that at 10 mm, the OL forecasted object is more aligned in orientation with the observed object, whereas at 15 mm, DA achieves better angular alignment. Finally, regarding centroid distance--which quantifies the spatial displacement between the centroids of forecasted and observed objects, results are comparable between OL and DA at the 10 mm threshold, while DA shows a significantly smaller displacement at 15 mm. This indicates that at higher intensities, DA better captures the correct location of precipitation features.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 2 Materials and Methods
### The Measuring Method of Modular WIM System Based on Transformer-GRU  
#### 2.3.1 Axle Recognition  
To calculate the number of axles, axle loads, and gross vehicle weight, it is necessary to obtain the signal of axle entering platforms from sensors in the front and the signal while exiting platforms from sensors in the rear as shown in Figure 4a. The entering input sequence shows that when an axle enters the platform, there is a sudden change in the entering signal, and as the axle travels further away from the sensor, the signal gradually decreases; the exiting input sequence shows that when an axle exiting the platform, the change is the opposite. With the help of the Transformer-GRU axle recognition model identifying the time points of each axle entering and exiting the weighing platforms as the label sequence, the number of consecutive segments of class 1 in the label sequence is the number of axles, as shown in Figure 4a, and it is a four-axle vehicle.
# A Parameter-Driven Methodology of Wheel Flat Modeling for Wheel-Rail Impact Dynamics
## 3 Complex 3D Wheel Flat Surface Reconstruction  
The simulation of vehicle-track dynamics based on the measured wheel roughness is more accurate when compared with the analytical form of the geometric modeling. When the wheel contains a wheel flat, the reconstruction of the complex surface of the flat through the measured roughness data of the wheel lays the foundation for accurately reproducing the vehicle-track dynamics response law under the flat condition.  
In recent years, the non-uniform rational B-splines (NURBS) method has gained popularity as a mathematical description of shapes in computer-aided geometric design (CAGD). This method combines the advantages of [PERSON]'s method and offers great flexibility in designing complex surfaces such as wheel flats. In the design of profiles of wheel and rail, and wear prediction, the cubic B-spline is primarily used [22; 23; 24; 25; 26; 27]. In addition, the B-spline technique plays an important role in the profile design of high-speed turnout switch panels [28]. This paper focuses on establishing the complex surface reconstruction algorithm of wheel flats based on the cubic B-spline.  
The reconstruction of the wheel flat surface consists of two stages including the computation of the flat control vertex matrix and the computation of the flat interpolation point matrix.  
Figure 5: The feasible domain of control parameters.
# An ADMM Based Parallel Approach for Fund of Fund Construction
## 3 Algorithm for FOF Optimization Model
### Solving the Reduced KKT System  
We describe how \(x^{k}\) is updated with (11). Minimizing the augmented Lagrangian (11) involves solving the following equality-constrained least-squares problem:  
\[\arg\min_{\bar{x}\in\mathcal{C}_{1}} \frac{1}{2}\bar{x}^{T}\bar{P}\bar{x}+\bar{q}^{T}\bar{x}+\frac{ \tau_{\bar{k}}}{2}||x-z^{k}+\frac{u^{k}}{\tau_{\bar{k}}}||^{2}, \tag{15}\] \[\bar{A}\bar{x}=\bar{b}. \tag{16}\]  
Let \(v^{k}=z^{k}-\frac{u^{k}}{\tau_{k}}\), the minimizing \(\bar{x}\) can be found by solving the following linear equation:  
\[\begin{bmatrix}\bar{P}+\tau_{k}I&\bar{A}^{T}\\ \bar{A}&0\end{bmatrix}\begin{bmatrix}x^{k+1}\\ \
u\end{bmatrix}=\begin{bmatrix}\tau_{k}v^{k}-\bar{q}\\ \bar{b}\end{bmatrix}. \tag{17}\]  
\(\bar{P}\) is a \((m+n)\times(m+n)\) matrix and \(\bar{A}\) is a \(m\times(m+n)\) matrix, we need to solve the \(2m+n\) linear equation. As \(\tau_{k}\) goes to change, we have to solve the linear system at each iteration, which will prevent us from solving the target problem effectively. Since \(\tau_{k}>0\), the matrix \(\bar{P}+\tau_{k}I\) is positive defined. There exist orthogonal matrix \(\bar{Q}\) such that  
\[\bar{P}=\bar{Q}^{T}\bar{D}\bar{Q} \tag{18}\]  
We have  
\[\begin{bmatrix}I&\mathbf{0}\\ -\bar{A}\bar{Q}^{T}(\bar{D}+\tau_{k}I)^{-1}\bar{Q}&I\end{bmatrix}\begin{bmatrix} \bar{P}+\tau_{k}I&\bar{A}^{T}\\ \bar{A}&\mathbf{0}\end{bmatrix}=\begin{bmatrix}\bar{P}+\tau_{k}I&\bar{A}^{T} \\ \mathbf{0}&-\bar{A}\bar{Q}^{T}(\bar{D}+\tau_{k}I)^{-1}\bar{Q}\bar{A}^{T} \end{bmatrix}. \tag{19}\]  
Then, \(x^{k+1}\) could be updated by  
\[x^{k+1}=\bar{Q}^{T}(\bar{D}+\tau_{k}I)^{-1}\bar{Q}(\tau_{k}v^{k}-\bar{q})-\bar {A}^{T}\
u \tag{20}\]where  
\[(\tilde{D}+\tau_{k}I)^{-1}=\begin{bmatrix}(d_{1}+\tau_{k})^{-1}&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&(d_{n}+\tau_{k})^{-1}\\ &&&\tau_{k}^{-1}I_{m\times m}\end{bmatrix} \tag{21}\]
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 5 Conclusions  
In this work, we presented a novel fingerprint-based indoor positioning algorithm that enhances positioning accuracy in multi-building, multi-floor environments. Unlike traditional machine learning approaches that train models on the entire dataset, the tree-based binary classification method introduced here enables the selection of a subset of training samples for localization. It significantly reduces the complexity of the relationships that the algorithm must learn in its final stage. Conventional DNN-based positioning algorithms typically overlook the statistical characteristic of RSSI samples at each RP and rely on two output nodes to directly estimate the user's location. In contrast, the improved DNN model achieves higher accuracy by incorporating one output node per RP, allowing it to capture the variations in RSSI patterns during the optimization process. Additionally, the designed WKNN algorithm was employed to address the limitations of DNN models when training data is inadequate, further enhancing localization accuracy. Using the publicly available UJIndoorLoc dataset, we demonstrated that the proposed ISDL method significantly improves localization performance.  
Conceptualization, H.L. and X.L.; Methodology, H.L. and D.M.; Software, H.L. and D.M.; Validation, H.L. and D.M.; Investigation, H.L.; Writing--original draft, H.L. and D.M.; Writing--review and editing, H.L., D.M. and X.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline
**Building** & **Floor** & **Mean Positioning Error (m)** \\ \hline  & 0 & 5.62 \\
0 & 1 & 5.81 \\
0 & 2 & 5.67 \\  & 3 & 7.41 \\ \hline  & 0 & 9.64 \\
1 & 1 & 10.23 \\  & 2 & 10.39 \\  & 3 & 8.27 \\ \hline  & 0 & 13.45 \\
2 & 1 & 6.11 \\  & 2 & 9.84 \\  & 3 & 9.88 \\  & 4 & 12.34 \\ \hline \end{tabular}
\end{table}
Table 4: Building-floor mean positioning error.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Building Success** & **Floor Success** & **Mean Positioning** \\  & **Rate** & **Rate** & **Error (m)** \\ \hline ISDL & 100\% & 95.05\% & 7.82 \\ SCNN & 100\% & 95.52\% & 9.68 \\ RTLSUM & 100\% & 93.74\% & 6.20 \\ ICSL & 100\% & 86.93\% & 7.67 \\ HFTS & 100\% & 96.25\% & 8.49 \\ MOSAIC & 98.65\% & 93.86\% & 11.64 \\ KNN & 99.73\% & 89.92\% & 7.9 \\ \hline \end{tabular}
\end{table}
Table 5: Localization results on \(U|IndoorLoc\) validation dataset vs. six best methods in [36, 37].  
* This research received no external funding.
* The _UJ1 IndoorLoc_ dataset is available at [[https://geotec.uji.es/2014/10/03/ujindoorloc-database/](https://geotec.uji.es/2014/10/03/ujindoorloc-database/)]([https://geotec.uji.es/2014/10/03/ujindoorloc-database/](https://geotec.uji.es/2014/10/03/ujindoorloc-database/)), accessed on 2 January 2025.
* Author [PERSON] was employed by the company Wuxi Realid Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Results and Discussion
### Information-Theoretic Measures of Information: How Do They Work? An Example from Chemistry  
Let us exemplify the information-theoretic measure information introduced with an example taken from chemistry. Consider the benzene combustion reaction, represented by Equation (8):  
\[2\text{C}_{6}\text{H}_{6}+1\text{SO}_{2}\rightarrow\ 1\text{2 CO}_{2}+6\text{H}_{2}\text{O} \tag{8}\]  
Figure 9 depicts the reaction schematically and demonstrates the elements of symmetry for the molecules involved. All of these molecules possess planar molecular geometry, which allows us to consider a reaction that takes place in R2 space. The molecule of benzene in the R2 space is characterized by twelve elements of symmetry, namely: \(\frac{\pi}{3}\), \(\frac{2\pi}{3}\), \(\pi\), \(\frac{4\pi}{3}\), \(\frac{5\pi}{3}\), \(2\pi\) rotations, and six mirror axes. The molecules of oxygen and CO\({}_{2}\) are linear ones and possess four elements of symmetry, namely \(\pi\) and \(2\pi\) rotations, and two mirror axes. The molecule of water in R2 space possesses two elements of symmetry: \(2\pi\)-rotation and the mirror axis, as shown inFigure 9.  
Let us calculate how the chemical reaction changes the symmetry of the system. Our calculation conducted according Equations (2) and (5) demonstrates that, before the reaction, we obtain \(H_{sym}=2.01\); \(\Omega_{sym}=0.36\). \(H_{sym}=2.01\), which is due to the high symmetric benzene molecule, is as relatively high value. On the other hand, the value of \(\Omega_{sym}=0.36\) is relatively low. This means that a certain group of symmetry (namely group with the symmetry involving a linear molecule of oxygen) dominates in the system addressed before the reaction. This is easily understood; indeed, 15 molecules of oxygen react with two molecules of benzene. After the reaction, the value of \(H_{sym}\) decreases (\(H_{sym}=1.37\)); this decrease is due to the diminished number of elements of symmetry inherent for the reaction products, namely water and CO\({}_{2}\) molecules. The value of \(\Omega_{sym}\) is contrastingly increased, namely we established \(\Omega_{sym}=0.64\), which points to the more homogeneous distribution of the symmetry elements among products of the reaction. Thus, we conclude that the suggested measures of symmetry may be applied for quantification of the symmetry change occurring under chemical reactions.