esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 4 A Novel Battery SoC Estimation Using a Data-Driven PHM Approach
### Handling Noisy Measurements  
EKF deals with process and measurement noise by modeling the vectors \(w_{k}\) and \(v_{k}\) as white Gaussians, as explained before. The UKF considers an even worse condition, where noise can also be nonlinear. Recent works have addressed the issue of dealing with uncertainties in the measurements due to unmodeled noise using the exponentially weighted moving average (EWMA) and the exponentially weighted moving standard deviation (EWMS) of past data [5; 12; 25].  
The EWMA and the EWMS of all input signals \(u_{k}\) at a discrete-time \(k\) is given by the following:  
\[\mu_{k}=\frac{\sum_{i=0}^{k}w_{i}u_{k-i}}{\sum_{i=0}^{k}w_{i}} \tag{30}\]  
\[\sigma=\frac{\sum_{i=0}^{k}w_{i}(u_{k}-\mu_{k})^{2}}{\sum_{i=0}^{k}w_{i}} \tag{31}\]  
where \(w_{i}=(1-\alpha)^{i}\) with \(\alpha=2/(s+1)\) and \(s\) is an arbitrary span.  
Multiple EWMA and EWMS calculations with different spans result in a range of smoothed versions for all time-series and their corresponding standard deviations. To  
Figure 3: Dataflow diagram for data-driven PHM approach.  
manage memory usage, we only consider four specific values for the span during the exploration process.
## 3 Application to Resistive Driven-Rod Method: Testing with Some Examples
### Synthetic Examples with Internal Resistance
As a preliminary conclusion, it can be stated that to get a good approximation to the soil resistivity profile, a fine sampling of the ground resistance of the rod is necessary. With the purpose of comparing with the Wenner method, starting from the parameters of the synthetic soil, the apparent resistivity of a test with separations between electrodes equal to the rod lengths used in Table 4 has been obtained. The process is then reversed, and the soil parameters are obtained from the apparent resistivity profile. The result of the reverse process is \(\rho_{1}\) = 99.99 \(\Omega\)m, \(\rho_{2}\) = 183.32 \(\Omega\)m, \(\rho_{3}\) = 39.94 \(\Omega\)m, h\({}_{1}\) = 2.95 m, and h\({}_{2}\) = 1.13 m, somewhat better than the one obtained with the present RDR method. Another detail to consider is that with a fine sampling a greater number of joints between pieces is introduced and therefore a greater overall internal resistance in the length of buried rod. As a consequence, as the total length of buried rod increases, the grounding resistance for a given length is somewhat higher in fine sampling than in coarse sampling. For this reason, a continuous graph of the grounding resistance calculated with the real values of the soil cannot be superimposed on Figure 6 for comparing purposes, as was done in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**L (m)** & 0.5 & 1.0 & 1.5 & 2.0 & 2.5 & 3.0 & 3.5 & 4.0 & 4.5 \\ \hline
**R (\(\Omega\))** & 137.09 & 79.20 & 56.86 & 44.76 & 37.15 & 32.17 & 29.94 & 27.36 & 19.60 \\ \hline
**L (m)** & 5.0 & 5.5 & 6.0 & 6.5 & 7.0 & 7.5 & 8.0 & 8.5 & 9.0 \\ \hline
**R (\(\Omega\))** & 15.92 & 13.52 & 11.80 & 10.50 & 9.47 & 8.64 & 7.95 & 7.37 & 6.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Buried rod length L (m) and the corresponding grounding resistance R (\(\Omega\)). Figure 7: Grounding resistance vs buried length of a rod composed by sections of 0.5 m length (**left** panel) and 1.5 m length (**right** panel) joined by resistive segments.
## 4 Discussion
### Application of BCG/SCG in Medical Treatment and Health Care  
#### 4.3.1 Specificity and Comprehensiveness  
The application of BCG/SCG in medical field is very specific. The researchers applied this technology to the measurement and diagnosis of cardiovascular parameters, as shown in Figure 9a. The measurement of a specific cardiovascular parameter was achieved by analyzing a characteristic value in the signal waveform, and the corresponding diseases  
Figure 8: A schematic of smart home construction.  
The application of BCG/SCG in the field of health management is comprehensive. By macroscopically detecting various physiological parameters of the human body, as shown in Figure 9b, the physiological changes of the human body in a certain period of time can be calculated, and the body functions can be evaluated. Based on these functions, BCG/SCG, for its features of easy design and operation, low measurement threshold, comprehensive data analysis, etc., will have a great potential for development in the field of health care.
## 2 Materials and Methods
### Validation with NWP Data Reanalysis
#### 2.3.1 Mera-2 Reanalysis Data  
MERRA-2 is a long-term reanalysis dataset implemented by the Global Modeling and Assimilation Office (GMAO) of NASA that provides data beginning in 1980. It is an evolution of the previous MERRA dataset that includes an assimilation module for satellite hyperspectral, microwave and GNSS radio occultation observations in addition to ground weather stations, radar and balloon data, already assimilated by the previous version. The Earth atmospheric domain is organised in a regular lat-lon horizontal grid with 576 points in the longitudinal direction and 361 points in the latitudinal one, corresponding to a resolution of 0.625\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). Quantities are provided in the vertical direction on either the native vertical grid (at 72 model layers or the 73 edges), or interpolated to 42 reference pressure levels. For this work, we selected the latter option, using the product namedM2I6 NPANA, as downloaded from [42], consisting of 6-hourly instantaneous 2D fields of sea level pressure (SLP) and surface pressure (SP), 3D meteorological fields, geopotential height (HGT), ozone mixing ratio (OMR), specific humidity (QV), air temperature (T), eastward wind component (U), and northward wind component (V) at 42 pressure levels (P) from 1000 hPa to 0.1 hPa. As extensively demonstrated in the scientific literature, ZPD is directly provided by the refractivity integrated along the vertical direction [43; 44]. Therefore, for our purposes, i.e., to derive reference ZPD values from MERRA-2 data, the relevant data were SLP, SP, P, HGT, QV and T, at all the available vertical levels.
## 4 Experiments
### DL vs. ML on BUZZ1 Validation Dataset  
We evaluated all raw audio ConvNets and ML models on the BUZZ1 validation dataset of 1150 audio samples. The receptive field size for all ConvNet was \(n~{}=~{}80\). Among the ConvNets, the top performer was RawConvNet (see Table 11). Among the ML models, the top performer was M1 (logistic regression) (see Table 12). Appendix B contains the confusion matrices for the other models on the BUZZ1 validation set. The total accuracy of RawConvNet (95.21%) was greater than the total accuracy of logistic regression (94.60%) and outperformed logistic regression in the bee and noise categories. Logistic regression outperformed RawConvNet in the cricket category.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Testing Type** & **Scaling Type** & **Testing Accuracy** \\ \hline
1 & train/test & none & 99.97\% \\
2 & k-fold, k = 10 & none & 99.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Classification accuracy for M4 (Random Forests) on BUZZ1 train/test dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Testing Type** & **Scaling Type** & **Testing Accuracy** \\ \hline
1 & train/test & none (S1) & 99.86\% \\
2 & train/test & standard (S2) & 99.87\% \\
3 & train/test & min max (S3) & 99.91\% \\
4 & train/test & L1 (S4) & 99.84\% \\
5 & train/test & L2 (S5) & 99.89\% \\
6 & k-fold, k = 10 & none (S1) & 99.93\% \\
7 & k-fold, k = 10 & standard (S2) & 99.94\% \\
8 & k-fold, k = 10 & min max (S3) & 99.96\% \\
9 & k-fold, k = 10 & L1 (S4) & 99.95\% \\
10 & k-fold, k = 10 & L2 (S5) & 99.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classification accuracy for M2 KNN on BUZZ1 train/test dataset. The accuracy is the mean accuracy computed over \(n\in[1,25]\), where \(n\) is the number of nearest neighbors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Testing Type** & **Scaling Type** & **Testing Accuracy** \\ \hline
1 & train/test & none (S1) & 99.86\% \\
2 & train/test & standard (S2) & 99.87\% \\
3 & train/test & min max (S3) & 99.91\% \\
4 & train/test & L1 (S4) & 99.84\% \\
5 & train/test & L2 (S5) & 99.89\% \\
6 & k-fold, k = 10 & none (S1) & 99.93\% \\
7 & k-fold, k = 10 & standard (S2) & 99.94\% \\
8 & k-fold, k = 10 & min max (S3) & 99.96\% \\
9 & k-fold, k = 10 & L1 (S4) & 99.95\% \\
10 & k-fold, k = 10 & L2 (S5) & 99.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification accuracy for M3 SVM OVR on BUZZ1 train/test dataset.  
Table 13 summarizes the classification performance of the raw audio ConvNets and the four ML methods on the BUZZ1 validation dataset. The results indicate that on this dataset where the validation samples were separated from the training and testing samples by beehive and location, RawConvNet generalized better than the three deeper ConvNets and performed on par with logistic regression and random forests. While KNN and SVM OVR performed considerably lower than RawConvNet, logistic regression, and random forests, both models (KNN and SVM OVR) outperformed the three deeper ConvNets.
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
###### Abstract  
In recent years, deep learning-based multi-focus image fusion (MFF) methods have demonstrated remarkable performance. However, their reliance on complex network architectures often demands substantial computational resources, limiting practical applications. To address this, we propose LightMFF, an ultra-lightweight fusion network that achieves superior performance with minimal computational overhead. Our core insight is to reformulate the multi-focus fusion problem from a classification perspective to a refinement perspective, where coarse initial decision maps and explicit edge information are leveraged to guide the final decision map generation. This novel formulation enables a significantly simplified architecture, requiring only 0.02 M parameters while maintaining state-of-the-art fusion quality. Extensive experiments demonstrate that LightMFF achieves real-time performance at 0.02 s per image pair with merely 0.06 G FLOPs, representing a 98.05% reduction in computational cost compared to prior approaches. Crucially, LightMFF consistently surpasses existing methods across standard fusion quality metrics.  
deep learning; multi-focus image fusion; focus property detection; lightweight network +
Footnote †: journal: Applied sciences
# Gravity Field Recovery Using High-Precision, High-Low Inter-Satellite Links
###### Abstract  
Past temporal gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE), as well as current solutions from GRACE Follow-On, suffer from temporal aliasing errors due to undersampling of the signal to be recovered (e.g., hydrology), which arise in terms of stripes caused by the north-south observation direction. In this paper, we investigate the potential of the proposed mass variation observing system by high-low inter-satellite links (MOBILE) mission. We quantify the impact of instrument errors of the main sensors (inter-satellite link and accelerometer) and high-frequency tidal and non-tidal gravity signals on achievable performance of the temporal gravity field retrieval. The multi-directional observation geometry of the MOBILE concept with a strong dominance of the radial component result in a close-to-isotropic error behavior, and the retrieved gravity field solutions show reduced temporal aliasing errors of at least 30% for non-tidal, as well as tidal, mass variation signals compared to a low-low satellite pair configuration. The quality of the MOBILE range observations enables the application of extended alternative processing methods leading to further reduction of temporal aliasing errors. The results demonstrate that such a mission can help to get an improved understanding of different components of the Earth system.  
Keywords:mass transport in the Earth system; GRACE and GRACE follow-on mission; current and future observation concepts and instruments +
Footnote †: journal: Remote Sensing, 11, 537
## 3 Security and Privacy-Preserving Solutions for IoE
### Quantum-Resilient Security Approaches
#### 3.3.3 Security Optimization in IoE with Quantum AI  
Quantum AI offers significantly greater computational capabilities compared to traditional AI methods, enabling more precise security analyses [17; 62; 66]. In IoE systems,  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Strengths** & **Weaknesses** & **Application Areas** & **References** \\ \hline Lattice-Based Encryption & High security level, post-quantum resistance, versatile usage & Large key sizes, high computational load & Blockchain data encryption, IoT security, digital signatures & [33; 34; 35; 35] \\ \hline Hash-Based Signatures & Low computational cost, long-term security, and quantum resistance & Limited use per key, large signature sizes & Blockchain protocols, digital signatures, and authentication & [43; 49; 54; 55] \\ \hline Code-Based Cryptography & Long-term security, low computational cost, and quantum resistance & Large key sizes, impractical in some scenarios & Authentication, IoT data security, email encryption & [119; 120] \\ \hline Multivariate Polynomial Cryptography & Small key sizes, fast processing time, and quantum resistance & Limited research and applications, potential weaknesses & Mobile device security, digital signatures, IoT devices & [56; 121] \\ \hline Quantum Key Distribution & Theoretically, absolute security, full protection against quantum attacks & High cost, requires special hardware, scalability issues & High-security data transmission, finance and healthcare, military apps & [18; 51] \\ \hline \hline \end{tabular}
\end{table}
Table 12: Feature and application analysis of prominent PQC methods.  
Quantum AI facilitates anomaly detection, attack vector analysis, and dynamic optimization of security policies [26; 59; 64; 110]. Figure 9 presents the general structure of a Quantum AI-supported security framework. Data collected from IoE devices is encrypted using quantum security measures, such as PQC and QKD, and transmitted to the Quantum AI Engine.  
This engine operates through a three-stage process chain: Threat Prediction, Anomaly Clustering, and Decision Recommendation Development. This structure enables end-to-end security enhancement and increases the effectiveness of real-time defense mechanisms in IoE systems [129; 130; 131].
## 2 Materials and Methods
### Data Collection
#### 2.5.2 Defining the Inspection Strategy  
When the base alignment is established, the features of interest may be extracted from the CAD-model to define tolerance characteristics and determine inspection strategies. The entire inspection is conducted with a single stylus of 3 mm diameter, effectively filtering out surface roughness and other minor surface imperfections. The inspection makes use of scanning strategies where the stylus tip is dragged along the surface while taking points at a high-frequency without compromising the accuracy. This allows a high number of points to be registered, which increases the resolution of the measurements and, therefore, also the reliability of the results. Furthermore, the path of the stylus is designed to cross the build layers of the specimen regardless of part build orientation, which enables detection of defects arising from the layered nature of LB-PBF and is of particular importance for the current experiment. Among all the points collected from a scanned surface, the first points are disregarded to safeguard against inertia and any lingering vibrations. Consequently, it is preferred to inspect each feature with a single undisrupted path covering the largest possible surface area. Naturally, a tradeoff must be made between execution time and the accuracy/resolution of the inspection. For continuity, all features belonging to the same group are treated equally with regards to the stylus path, even though the number of points or the measurement speed must be corrected to make up for the dimensional differences.  
The base plate is inspected by a single path starting in the bottom left corner, moving along the edge in a counterclockwise manner, as displayed in Figure 16. When returning to the start point, the path trails off towards the interior of the specimen to make sure any warping towards the middle is included in the base alignment for, which this feature is crucial. Next, the line on HX2_Plane2 is repeated as a scanned line before the point on HX1_Plane4 concludes the CMM's confirmation of the base alignment.  
The cylinders are inspected with a helical scanning path starting at the base of convex cylinders (top of concave cylinders) at the extreme point in the x-direction, moving counterclockwise in 3 revolutions before reaching the other end, as shown in Figure 17. The spheres are inspected by three lateral paths at 15, 45, and 75 degrees from the horizontal, as illustrated in Figure 17, and the cones are inspected by helical paths in the same way as cylinders (Figure 17). Finally, the planes are inspected by a continuous path, as illustrated in Figure 17, where three square paths are connected and inspected as one.  
Figure 15: Manual probing points for base alignment are indicated with red areas. **(a)** Top view of points on the base plate; **(b)** a perspective view showing points on HX1 and HX2.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 3 Research Data and Methods
### Research Methods  
#### 3.3.1 Network Construction  
Cross-regional investment flows between enterprises and cargo flows between ports within the Greater Bay Area are abstracted as network expressions, with cities as nodes and intercity capital and shipping links as edges. Taking into account the directionality and inequality of capital and transportation flows between cities, weighted directed capital flow networks \(G_{F}=(V,E,W)\) and logistics networks \(G_{T}=(V,E,W)\) are constructed and represented by adjacency matrices \(F_{ij}\) and \(G_{ij}\), respectively:  
\[F_{ij}=\begin{cases}f_{ij}&if\ i\ and\ j\ are\ connected\\ 0&otherwise\end{cases},\quad G_{ij}=\begin{cases}g_{ij}&if\ i\ and\ j\ are\ connected\\ 0&otherwise\end{cases} \tag{1}\]
# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
## 3 DAO-YOLO Model
### OCIoU Bounding Box Regression Loss  
While the CIoU loss function in YOLOv8 enhances bounding box regression by optimizing aspect ratios, this approach may cause differences in the width-height ratios of the predicted and actual boxes. To resolve this problem, we propose OCIoU, a novel bounding box regression loss that independently optimizes width and height parameters through decoupled computations.  
The OCIoU loss is defined by the following expression:  
\[L_{\text{OCIoU}}=1-IoU+\frac{\rho^{2}(b,b_{gt})}{c^{2}}+\frac{\rho^{2}(w,w_{ gt})}{w_{c}^{2}}+\frac{\rho^{2}(h,h_{gt})}{h_{c}^{2}} \tag{6}\]  
As shown in Figure 7, \(IoU\) denotes the Intersection over Union between the predicted and ground truth boxes, \(b\) and \(b_{gt}\) respectively denote the center points of the predicted and ground truth boxes, \(w\) and \(h\), respectively, denote the width and height of the predicted box, \(w_{gt}\) and \(h_{gt}\) respectively denote the width and height of the ground truth box, \(w_{c}\), \(h_{c}\), and \(c\), respectively, denote the width, height, and diagonal length of the minimum enclosing rectangle, \(\rho(b,b_{gt})\) denotes the Euclidean distance between \(b\) and \(b_{gt}\), \(\rho(w,w_{gt})\) denotes the difference between \(w\) and \(w_{gt}\), and \(\rho(h,h_{gt})\) follows the same principle.
## 3 Results and Discussion
### Interlaboratory Assessment  
The 30 overlapping APIs between the CoEMS and USGS methods led to 120 chemical determinations for comparison, with chemical results being confirmed in 98% of determinations (117 of 120, Table S4). Only three determinations for codeine were confirmed at low concentrations (i.e., 15 to 26 ng/L) via the USGS method while not detected by the CoEMS method (Table S4). Of the 120 total determinations, 48 were nondetects in both methods, 14 were not detected in one method but confirmed between the LOD and LOQ of the other (e.g., mainly due to differences between LODs), 55 determinations were confirmed and quantified in both methods, while three were confirmed and quantified in one method but not in the other (Table S4).  
Figure 1: Mean (\(n\) = 3) interior and exterior temperatures of a sample shipping package containing two ice packs (as standardised for all sampling kits) over 7 days.  
Figure 2: Stability of the target chemicals in liquid chromatography-mass spectrometry (LCMS)-grade water held at 4 \({}^{\circ}\)C, 20 \({}^{\circ}\)C and 35 \({}^{\circ}\)C for 2 and 7 days respectively.  
The 55 determinations which were confirmed and quantified by both methods covered 19 APIs (Figure 3). Concentrations determined between the methods were in agreement with an overall mean deviation of 19.5% \(\pm\) 12.3% (Figure 3). Within this deviation, 76.4% of the determinations made at CoEMS were lower than those determined by the USGS (mean 19.6% \(\pm\) 9.6% lower). No substantial difference was observed between the interlaboratory deviations in WWTP effluent and surface water with median discrepancies of 19.7% (31.5 ng/L) and 19.4% (37.9 ng/L) respectively. The absolute differences in respective matrices ranged from 2.4% (1 ng/L, trimethoprim) to 38.7% (31.5 ng/L, sitagliptin) in effluent and 0.8% (2.6 ng/L, tramadol) to 36.4% (104 ng/L, lidocaine) in surface water (Figure 3). Generally, deviation between the two values (Table S4) was within the intra-day repeatability of the CoEMS method (Table S5).  
The top five APIs prioritised by concentration in both WWTP effluent and surface water were identical for both the USGS and CoEMS assessments: Fexofenadine, gabapentin, metformin, desvenlafaxine and venlafaxine (Figure 4).  
Figure 3: Relative percent difference (RPD) between concentrations of 30 medicinal chemicals (present in both respective methods) quantified by the USGS (using USGS method No. 5-B10 [7]) and CoEMS (using the method presented in this work) in both WWTP effluent and surface water with median RPD represented by the vertical bar.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 3 A Festival Mobility Model  
In this section, we analyze a festival dataset that will be utilized for our solution, and then we discuss which protocols would be suitable as a means of communication for ONs and their limitations. We also discuss a type of application that might benefit from the use of ONs and what communication patterns it implies. We then analyze existing mobility models and their drawbacks, and propose a novel mobility model better suited for approximating festival contacts.