esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Related Work
Furthermore, their approach incorporates explanation techniques such as Grad-CAM++ to interpret the model's predictions, thereby enabling the identification of critical variables and periods that influence the occurrence of overloads. The results of this paper showed improvements over previous methods in terms of sensitivity, specificity, and F1 score. Physics-informed neural networks (PINNs) represent a significant advancement in combining deep learning with physical laws [11]. By embedding differential equations into training, PINNs enhance the representation of spatial and temporal features. Current methods use inductive bias to integrate prior physics knowledge into the computational graph, preserving symmetries and improving generalization to nonlinear field evolution problems [14; 25]. Recent studies on classification problems using PINNs [26; 27] highlight a bias towards majority classes, limiting their accuracy in predicting minority classes. Traditional methods like sample weighting or resampling often fail to resolve this issue effectively. Our hybrid approach integrates physical knowledge into the loss function to address this,explicitly targeting overload classification as the minority class. Our method aims to improve sensitivity, specificity, and overall prediction robustness in imbalanced datasets by embedding operation-specific rules derived from metallurgical expertise. Physics-guided neural networks (PGNNs) [28; 29] and physics-embedded neural networks (PENNs) are advanced frameworks for integrating physical knowledge into neural network training, enhancing model accuracy and interpretability beyond purely data-driven methods. To guide training, PGNNs flexibly incorporate physical insights, such as simulated data, parameter constraints, or heuristic rules. Unlike PINNs, which embed differential equations into the loss function, PGNNs aim to enrich the learning process with additional information reflecting the problem's physical realities. For example, [PERSON] et al. [28] demonstrated using PGNNs to model lake temperature dynamics by combining physical simulations with observational data, creating a robust hybrid approach that balances data-driven models and physical principles. Physics-embedded neural networks (PENNs) embed physical laws directly into the neural network architecture, ensuring outputs inherently respect principles like conservation laws and symmetry. Naturally adhering to these constraints, this design promotes reliable and physically consistent predictions. For instance, [PERSON] et al. [29] introduced PENNs using E(n)-equivariant graph neural networks (GNNs) to solve partial differential equations (PDEs) with mixed boundary conditions. Their model integrates boundary conditions into the network structure, rigorously fulfilling Dirichlet and Neumann's conditions. Based on IsoGCN, the method employs boundary encoders, Dirichlet layers, and a pseudoinverse decoder, achieving superior generalization across shapes and outperforming traditional solvers and machine learning models in speed-accuracy trade-offs.
# A Scalable Bidimensional Randomization Scheme for TLC 3D NAND Flash Memories
## 5 Conclusions  
In this work, we proposed a randomization scheme for 3D NAND Flash memory technology that allows a good degree of randomization in both memory dimensions (i.e., wordline and bitline) without requiring a complex implementation methodology while relying only on a proper arrangement of LFSRs circuits. The simulation results show that our methodology imposes a guard band on the maximum number of consecutive ones and zeros along the bitline dimensions (no more than 25) to keep the read failure probability during the data readout phase under control.  
Further, we demonstrate by construction that our randomization scheme has better control of the number of zeros or ones along the bitline, proving a good balancing of the write data to the memory, thus representing an optimal case for reliability.  
Finally, we experimentally validated our proposed methodology on an off-the-shelf TLC 3D NAND Flash memory chip, showing that, under JEDEC-style endurance and data retention stress, we can achieve RBER for LSB and CSB pages that is always below the correction limit imposed by a 100 b/1 kB Error Correction Code. Our randomization methodology can therefore be exploited by storage system designers to keep the memory reliability under control.  
The individual contributions to this paper are the following: conceptualization, M.F., P.O. and R.M.; methodology, M.F., A.M. and P.O.; software, M.F.; validation, A.M. and R.M.; electrical measurements and resources, A.M.; investigation, C.Z., P.O. and M.F.; data curation, C.Z. and R.M.; writing-original draft preparation, M.F. and C.Z.; writing-review and editing, A.M., R.M. and P.O.; visualization, C.Z.; supervision, R.M. and P.O. All authors have read and agreed to the published version of the manuscript.  
This research no external funding.  
The authors declare no conflict of interest.  
Figure 11: Topological characterization of the RBER in a 3D NAND Flash memory for Endurance and Retention working corners when the input data come from an HC randomizer (**a,c**) or from our proposed method (**b,d**).
## 3 Description of the Method
### Definition of the Keel Curve
#### 3.1.2 Definition of the Bow Outward Section  
The outward section of the bow is located at the end of the keel curve, which starts from point \(\mathbf{P}_{k3}\) and ends at \(\mathbf{P}_{k4}\) (see Figure 7). At point \(\mathbf{P}_{k3}\), it satisfies first-order continuity with curve \(\mathbf{S}_{k2}(\mathbf{u})\). The tangent vector of endpoint \(\mathbf{P}_{k4}\) makes an angle \(\alpha_{k}\) with the \(x\)-axis, which is appropriately expressed via a quadratic Bezier curve, as given by Equation (13). Further, \(\mathbf{C}_{k3}(x_{Ck3},0,2x_{Ck3})\) denotes the control point that should be suitably determined.  
\[\mathbf{S}_{k3}(u)=B_{0,2}(u)\cdot\mathbf{P}_{k3}+B_{1,2}(u)\cdot\mathbf{C}_{ k3}+B_{2,2}(u)\cdot\mathbf{P}_{k4} \tag{13}\]  
Control point \(\mathbf{C}_{k3}\) can be evaluated by solving Equation (14), which is established based on the slope constraints at both endpoints, given that the end angle at the design parameter is \(\alpha_{k}\).  
\[\begin{bmatrix}-\eta_{3}&1\\ -\tan(\alpha_{k})&1\end{bmatrix}\begin{bmatrix}x_{Ck3}\\ z_{Ck3}\end{bmatrix}=\begin{bmatrix}H_{c}-\eta_{3}\cdot L_{c}\\ H_{f}-tan(\alpha_{k})\cdot L\end{bmatrix} \tag{14}\]  
where the slope of the tangent vector at \(\mathbf{P}_{k3}\) is denoted by \(\eta_{3}\), which is evaluated by control point \(\mathbf{C}_{k2}\) and point \(\mathbf{P}_{k3}\).
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 2 Related Work
### Mobile-Group-Based Location Service (MoGLS)  
The MoGLS [10] uses a hierarchical structure of location servers. The lower level is made up of dynamic groups of vehicles with similar trajectories. Each group has a cluster head that acts as the location server for its group. The upper level is made up of fixed servers. Cluster heads regularly send their location base to fixed servers. MoGLS offers the data aggregation and dynamic management of vehicle groups. This location service can only operate with a fixed infrastructure, and the management of the group of vehicles can quickly generate significant traffic overhead, particularly in the event of high density and mobility.
# Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites
## 3 Results and Discussion
### Results of Block Adjustment  
Data for Shanxi were collected in this experiment. The detailed information is shown in Table 2. The 221 scenes of the basic product image, image distribution position, and matching connection point distribution are shown in Figure 15.  
Using the high-precision matching algorithm [16], 13,086 connection points were obtained on the 221-scene image. Based on the image affine model of RPC expressed in Equation (5), DEM-assisted uncontrolled plane adjustment [20; 21; 22] was performed on the region 221-scene image. The auxiliary DEM is the DEM with a 25 m resolution and 5 m elevation accuracy of the Shanxi region. The results are shown in the table below.  
The adjustment accuracy in Table 5 depends on the elevation influence and the accuracy within the image. As can be seen from Table 2, the maximum intersection condition of the 221 scene data is \(9.82^{\circ}\) and \(-8.69^{\circ}\), and the elevation effect in plane adjustment is 0.2 pixels, which is basically negligible. Therefore, the adjustment accuracy of 1.15 pixels in this area mainly reflects the accuracy of the image, which is consistent with the above theoretical concept of \"not exceeding 2 pixels.\" Further, an orthophoto image of the region was generated, and the result is shown in Figure 16. The result satisfies the accuracy requirement of 1 pixel of the image mosaic edge and verifies that the OHS basic product image has a large area mapping capability. A detailed image of the local edge joint is shown in Figures 17 and 18.  
Figure 14: Single scene directional residual diagram.  
Figure 16: Diagram of orthographic image overlay.  
Figure 15: Image and connection point distribution.
## 2 Material and Methods  
### Overview of the Site Context and Equipment  
The experimental site is located at Catenoy (Oise), about 50 km north of Paris [6]. It is situated in the unconfined chalk aquifer of the Paris Basin. The geology corresponds to a few meters of Quaternary deposits and Tertiary formations, lying over a hundred meters of Senonian chalk that is only visible in the thalwegs (see also Figure 1). Under the site, the underground geology of the first 25 m is: 3 m of colluvium, 4 m of Thanetian sands, and 18 m of chalk. The chalk encloses an aquifer with a static level at a depth of 13 m, which flows in the WSW-ENE direction. The storage coefficient is from 1.1 \(\times\) 10\({}^{-2}\) to 6.5 \(\times\) 10\({}^{-2}\) and the hydraulic conductivity is from 6.4 \(\times\) 10\({}^{-4}\) to 1.4 \(\times\) 10\({}^{-3}\) m\(\cdot\)s\({}^{-1}\).  
The experimental site has eight piezometers which are 25 m deep and aligned in the direction of the aquifer flow (Figure 2), over a distance of 80 m. The PZ1 piezometer, which functions as a control, is located 20 m upstream of the PZ2 injection well. The PZ3, PZ4, PZ5, and PZ6 piezometers are, respectively, located 10, 20, 30, and 60 m downstream of the injection well. There are also two close monitoring piezometers: the PZ2 BIS located at 5 m, which is the main monitoring piezometer for this study, and the PZ2 TER located at 7.5 m, which contains Infrared and Raman spectrometers (the specific results from these are the subject of another article in preparation). The site is also equipped with a technical  
Figure 1: Location and geological context of the experimental site.  
room and a meteorological station. The data are transmitted in real time to the dedicated e.cenaris cloud monitoring system [25,26].  
The hydrodynamic characteristics of the aquifer were determined during a pumping test carried out previously [10]. Depending on the piezometer considered, the porosity varies from 1.1 \(\times\) 10\({}^{-2}\) to 6.5 \(\times\) 10\({}^{-2}\) and the hydraulic conductivity from 6.4 \(\times\) 10\({}^{-4}\) to 1.4 \(\times\) 10\({}^{-3}\) m.s\({}^{-1}\). The groundwater flow velocity, measured during a tracing test, varies from 3 m.day\({}^{-1}\) at PZ3 and PZ5 to 10 m.day\({}^{-1}\) at PZ4, which is situated in a preferential flow path (probably a fissured zone). This highlights the dual porosity of the aquifer studied, which may be reflected hydrogeologically by contrasts in flow velocity and variations in transit times from one piezometer to another.
# Fall Recognition Based on Time-Level Decision Fusion Classification
## 3 Fall Detection System
### Classification  
In the proposed system, the classifier uses the key points obtained from the pose estimation module as inputs to classify the actions of the detected persons in the image as either falling or as normal behavior. Using these key points as inputs instead of all RGB pixels in the video prevents overfitting, thereby improving generalization performance. Falls occur because of a series of movements in which balance is lost and the body makes  
Figure 3: Skeleton data output of ViTPose and AlphaPose: (**a**) result of ViTPose, and (**b**) result of AlphaPose.  
contact with the ground. To recognize such temporal changes in movements, it is crucial for the classifier to understand not only individual images but also how changes unfold in a video. In each video frame, the classifier should learn the configuration of the person's body and understand how the body arrangement changes across consecutive frames. Therefore, we employed ST-GCN [35] as the classifier. This choice was motivated by its ability to grasp the formation of the body in each video frame and capture how the body's structure evolves over successive frames. In ST-GCN, the skeleton coordinates are organized into graph-formatted data for input. This model recognizes the spatial patterns between the key points in the skeleton graph data obtained from each frame, and detects the temporal patterns between skeleton graph data acquired from multiple frames. By utilizing both spatial and temporal information, ST-GCN can detect falls effectively. The operating process of the ST-GCN is shown in Figure 4.  
In one layer of the ST-GCN, the spatial kernel size, which represents the distance considering the connections between adjacent joints, is set to \(1\). This means that only the most adjacent nodes are included in the spatial operation range. The spatial receptive field of one ST-GCN operation is considered to involve three nodes. As shown in Figure 4, three nodes get involved in one ST-GCN operation. For instance, for the elbow node, the spatial receptive field of the ST-GCN operation includes the wrist, elbow, and shoulder nodes. Next, the parameter for the temporal kernel size, which determines how many frames participate in the temporal operation, is set to 9. In other words, the corresponding frame and the surrounding four frames are included in the temporal receptive field. It is not the length of the skeleton sequence but rather the size of the temporal kernel that is 9. Therefore, conducting ST-GCN operations layer by layer enables the recognition of patterns in spatial-temporal information. By utilizing the ST-GCN, we can overcome the limitations of traditional classifiers in handling unseen data and enhance generalization performance.
## 3 Results
### Comparative Study of Modulation in Fibers with and Without APPJ Treatment  
According to the results presented in Table 3, and Figure 5 the DMF exhibited significant differences. Regarding TMED, the LM0, LB, and LP5 groups demonstrated a higher percentage of total medulla compared to the LP10 and LP15 groups, which exhibited lower percentages. The samples subjected to prolonged plasma discharge (LP10 and LP15) displayed a lower percentage of total medulla in the fibers. Generally, medulllation is considered an undesirable characteristic as it devalues the wool. For NMED, the LM0 group samples had the lowest percentage of non-medullated wool, while the LP15 group had the highest percentage of non-medullated wool. The percentage of non-medullated wool increased with the plasma discharge treatment time. No significant differences were observed between the LB and LP5 groups. In FMED, the LP15 group presented the highest percentage of fragmented medulla, while the LM0 group had the lowest percentage. The percentage of wool with fragmented medulla was directly proportional to the increase in plasma discharge time. No significant differences were observed between the LB and LP5 groups. For DMED, significant differences were observed among the five sample groups, indicating that the treatment does not influence the variation in the percentage of fibers with discontinuous medulla. In CMED, the LM0 group had the lowest percentage of continuous medulla, while the LP15 group presented the highest percentage. The continuous medulla in sheep wool also increased with the plasma discharge treatment time. No significant differences were observed between the LB and LP5 groups. Finally, in SMED, the variations  
Figure 4: Heatmap correlating textile characteristics. shown by the data were less clear compared to the previous columns. The LP5 group had the lowest percentage, and the LP10 group had the highest percentage. No significant differences were observed between LB and LP15 groups. Statistical analysis of the textile characteristics of the samples (LM0, LB, LP5, LP10, and LP15) was conducted utilizing analysis of variance (ANOVA) for each textile characteristic (TMED, NMED, FMED, DMED, CMED, and SMED). The analysis revealed no statistically significant differences between the parameters (\(p>0.05\)); however, significant differences were observed in DMF. The results are shown in Figure 6, in which a heatmap correlates the various variables measured in the wool fiber yarns. The mean fiber diameter (DMF) had a strong positive correlation with the percentage of fibers with a fragmented medulla (FMED) (0.87), fibers with a continuous medulla (CMED) (0.87), and strongly medullinated fibers (SMED) (0.4). This suggests that as DMF increases, the proportions of the mentioned characteristics also increase. However, DMF shows a strong negative correlation with the total medullation  
Figure 5: Bar chart of medullation properties of samples treated with and without plasma.
# Analysis and Design of Rateless Two-way Relay Networks Based on a Multiply-and-Forward Scheme
###### Abstract  
In this paper, we propose a rateless, three-stage, two-way multiply-and-forward (MF) relaying system over the Rayleigh flat fading channel, where two source nodes communicate with each other through a relay node and all nodes work on half-duplex and time-division mode. We thoroughly analyze the signals during all three stages in the proposed MF system and derive the closed-form symbol error rate (SER) expressions for an uncoded MF-two-way relay network (MF-TWRN). Furthermore, we provide the equivalent point-to-point fading channel model, which is employed to carry out the asymptotic performance analysis. We finally put forth an optimization model for the MF-TWRN with fountain codes. Simulation results show that our optimized degree distribution can provide outstanding performance for the MF-TWRN compared to those in the literature.  
two-way relaying; multiply-and-forward; fountain codes; optimization +
Footnote †: journal: Applied  
_Article_
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 3 Results and Discussion
### Spectral Curve Analysis  
The wavelength range of the hyperspectral data collected in the experiment is 881-1715 nm with 256 wavebands. Since the head and end of the spectral data are obviously subject to noise during acquisition, this part of the data should be removed during the study. Therefore, 228 bands from band 13 to band 240 (that is, the spectrum with a wavelength range of 920-1661 nm) are used for analysis.  
Spectral data were pretreated with MSC to eliminate or minimize the effects of acquisition environment and instrument noise as much as possible. The mean spectral curves of the five kinds of corn seeds are shown in Figure 3. As 2490, 2780, GD5, and XX8 seeds use the same female parent in the hybridization process, they are highly similar in genes and traits, and XY335 is relatively different. However, the main components of corn seeds are basically the same, so the average spectral curves of the five kinds of maize seeds are similar and cannot be distinguished from the original curves.
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 2 Target Identification
### Charging Technologies
#### 2.3.3 Inductive Charging  
The operational principle of inductive power transfer resembles the operation of conventional transformers. The general scheme includes a transmitting coil that generates the electromagnetic field, usually integrated in the street, and a coupled receiving pick-up mounted on the vehicle, which is finally followed by a rectifier. Due to the large air gap, a capacitor bank is usually included as well [91]. A pilot project in Germany demonstrated the operation of a 200 kW inductive power transfer for a HDV [92]. Very few authors consider wireless solutions for AS. The authors of [42] take into account both inductive (200 kW) and conductive charging (300 kW), and an upper limit of 5 min for charging time is imposed on the resolution process. Due to higher upfront costs of inductive charging, only conductive charging stations are implemented in the cost optimized solution. In [43], the authors make use of an automatic CI through an inductive overhead system but do not specify the characteristics of the charger. Wireless solutions present several characteristics that can increase user comfort compared to static charging and battery swapping, such as the possibility to charge the vehicle on the move (dynamic charging) and avoiding the use of connectors that must be plugged by the users. On the other side, researchers investigated whether the electric and magnetic field produced during the coupling could result in safety concerns for human health. In [93], the authors studied the coupling of a double coil and a unipolar coil to achieve the maximum coupling coefficient; researchers analyzed the operational risks for humans, concluding that worst case scenarios are well below InternationalCommission on Non-Ionizing Radiation Protection (ICNIRP) regulations. In [94], authors carried out a test in a laboratory facility (by a renown automotive house) and verified an efficiency (DC power transmission) range between 86% and 90%. Recently, authors in [95] reviewed the technical characteristics of dynamic solutions implemented in pilots around the world. Researchers simulated the mobility demand of a highway in Norway (both for light and heavy duty vehicles) to ultimately design an electric infrastructure for both static and inductive charging infrastructures. In their results, the authors assessed the size of the conductors and the substations rating, comparing the load on the charging stations and on the substations.