esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
These include (1) physics-derived representations based on physical principles, (2) morphological representations reflecting structural priors, and (3) directional discrepancy representations characterizing wave propagation patterns. By translating these knowledge representations into loss functions to guide the wavefield separation process, the method aims to combine the representational power of deep learning with physical constraints for up/down-going wave separation, overcoming limitations of traditional approaches and purely data-driven supervised methods. The proposed KGCAE leverages its dual-autoencoder architecture and unsupervised, knowledge-driven learning paradigm to eliminate dependence on large, labeled datasets, which is typically required by supervised deep learning methods. Through the integration of diverse knowledge representations, this approach guides the network to generate physically plausible and structurally coherent separation results, particularly under challenging geological conditions with severe wavefield aliasing and low signal-to-noise ratios. This knowledge-guided mechanism effectively constrains the solution space, avoiding non-physical artifacts or stability issues common in purely data-driven or morphology-based unsupervised methods, thereby achieving more robust and stable separation performance. However, the method's performance depends on the accuracy of the input knowledge representations. The physical knowledge relies on the quality of the velocity model obtained through full-waveform inversion (FWI), while the morphological knowledge currently requires manual dip picking. This study aims to achieve VSP upgoing and downgoing wavefield separation by establishing a comprehensive multi-dimensional knowledge representation system for signal analysis and constructing a knowledge-driven separation framework based on a dual convolutional autoencoder, transforming the knowledge representation system into constraint loss functions applied to the autoencoder. An unsupervised separation of the upgoing and downgoing waves was achieved. Systematic validation experiments conducted on both the homogeneous layer and Marmousi models demonstrated that the proposedmethodology effectively processes the VSP upgoing and downgoing wavefield separations under complex geological conditions, effectively avoiding the reliance on large-scale labeled samples inherent in traditional data-driven methods.
## 3 Analyzing Challenges
### Data Issues  
A type of AI-based agent invests in data-driven methods to construct learning models. In these algorithms, data issues cause various problems. Some of these problems are explained below [28; 29; 30; 31; 32]:  
* Cost is one of the main issues of data. Major sources of cost are gathering, preparing, and cleaning the data [28].
* The size of collected data in a wide range of systems such as IoT (Internet of Things) is another data-related challenge. This huge amount of data leads to a new concept, called big data. Analyzing big data in an online fashion via machine learning algorithms is a very challenging task [28; 29; 30].
* Data incompleteness (or incomplete data) is another challenging problem in machine learning algorithms which leads to inappropriate learning of algorithms and uncertainties during data analysis. This issue should be handled during the pre-processing phase. Various approaches can be used for mitigating this problem. Filling missed (incomplete) data via most frequently observed values or developing learning algorithms to predict missed values are some examples of these approaches [32].
* Data heterogeneity, data insufficiency, imbalanced data, untrusted data, biased data, and data uncertainty are other data issues that may cause various difficulties in data-driven machine learning algorithms [28; 29].
* Bias is a human feature that may affect data gathering and labeling. Sometimes, bias is present in historical, cultural, or geographical data. Consequently, bias may lead to biased models which can provide inappropriate analysis. Despite being aware of the existence of bias, avoiding biased models is a challenging task. For more information, please refer to [28].  
Not only the mentioned problem, but also other problems such as imbalanced data, synthetic (fake) data, and noisy data can be classified in this part. Data issues become more critical challenges in designing HLI-based agents. Nowadays, finding datasets that reflect the correct human behaviors might not be possible in many of the domains that HLI-based agents can be utilized.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Advanced Malware Detection Techniques  
Advanced malware detection techniques surpass traditional methods by incorporating approaches such as machine learning, computer vision, federated learning, memory analysis, and adversarial learning. These methodologies effectively address emerging threats such as fileless malware, adversarial attacks, and distributed environments (IoT/IIoT), showing improvements in accuracy, robustness, and computational efficiency [49]. Strategies include active learning, which reduces the need for labeled data; federated learning, which preserves privacy; and computer vision, which converts binaries into images for analysis with convolutional networks [50]. Additionally, hybrid architectures and memory analysis are employed to improve detection and generalization against real-world attacks [51].
# Application of DMLF in Pulse Ranging LiDAR System
## 4 Conclusions  
In this paper, a novel pulsed-laser ranging system of the differential methods based on linear fitting is proposed, and a mathematical model is established and verified. Compared with the traditional peak method with a single APD receiver, DMLF obtains the echo signal through two APD receivers and acquires the differential signal through a subtracter. Therefore, the PPs are changed to PGZCPs. Simulation experiments show that PGZCPs has higher sensitivity. We can also obtain the highest sensitivity when the axial shift \(d=c\tau\). According to the above simulation and analysis, the proposed DMLF could improve the timing accuracy of laser ranging. Additionally, there are some obvious advantages to our approach:  
1. It can handle phenomena such as low SNR and high noise power, and has strong anti-interference ability and robustness;
2. Compared with the traditional peak method, the DMLF proposed in this paper can reduce the timing error and decrease the RMSE by almost 50%;
3. Using the differential method with optimal axial shift \(d\), the sensitivity and ranging accuracy of the pulse ranging LiDAR are significantly improved, and the RE of ranging can be less than 30 ppm.  
Therefore, based on the above summary, our proposed method has a few advantages in improving the accuracy of the laser pulse-ranging system.  
In this paper, our works aim to establish a mathematical model of the proposed method and perform a simulation to verify the feasibility, and experimental verification will be carried out in our future works.  
Conceptualization: D.G.; data curation: [PERSON], B.Q.; formal analysis: [PERSON], B.Q.; funding acquisition: C.W.; investigation: D.G.; methodology: D.G. and B.Q.; resources: C.W.; software: D.G.; supervision: C.W.; validation: D.G. and B.Q.; visualization: D.G.; writing--original draft: D.G. writing--review and editing: D.G. and B.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shenzhen Fundamental Research Program (Grant No.JCYJ2020109150808037); the National Key Scientific Instrument and Equipment Development Projects of China (Grant No.62027823); the National Natural Science Foundation of China (Grant No.61775048).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Site Potential and Expected Ecosystem Performance  
Site potential was estimated using temporally static inputs that accounted for 49% of the variation in site potential (Figure 1, R\({}^{2}\) = 0.49, \(n=2529\), \(p<0.0001\)). Coarse resolution inputs (permafrost, long-term climate, surface geology) prevented the model from explaining higher amounts of 250 m GSN variability since GSN captures a significant amount of local variability. Fire perimeters also had larger errors for older fires and some fires were mapped before they were completely extinguished so outlier pixels could include some burned pixels.  
Site potential tends to coincide with the east to west moisture gradient in the Yukon River Basin (R\({}^{2}\) = 0.26, \(p\) \(<\) 0.01) due to the isolation of eastern areas from maritime moisture [1] (Figure 1). Low precipitation impacts are especially apparent in the Yukon Flats region in the north-central part of the Yukon Basin and in east-central Yukon, Canada, which has some of the lowest site potentials in the basin attributable to their continentality and rain shadows that reduce precipitation. Elevation was negatively related to site potential (R\({}^{2}\) = 0.21, \(p\) \(<\) 0.01). In contrast to longitude, site potential was not significantly related to latitude, due to the confounding effects of elevation and the proportionately small latitudinal range of the study area. Site potential tends to be highest on floodplains and mid-successful deciduous forest. Our results commonly demonstrated low site potential in wet coniferous forests, while deciduous stands of aspen and birch were considerably more productive than white and black spruce [29], and had the highest site potentials, similar to [5].
## 3 Operation and Models of the VANET Testbed
### Network Models
#### 3.2.1 VANET Model  
The VANET model consists of the VANET mobility model for the movement of a vehicle, and a communication model for information exchange with neighboring MS and BS.
## 3 Results and Discussion
### FTIR Analysis  
The results of the FTIR spectroscopy are shown in Figure 4 for MIP(DEGDMA) and Figure 5 for MIP(DVB). The description of the peaks of the functional groups was analyzed according to the handbook in [63] and is shown in Table 3. As shown in Table 3, both monomers are present in structures on the obtained MIPs. The presence of methacrylic acid is shown by signals characterizing a carboxylic group: intra- and intermolecular hydrogen bonds between -OH groups at 3418.59 cm\({}^{-1}\) for MIP(DEGDMA) and 3420 cm\({}^{-1}\) for MIP(DVB); a -COOH group at 1629.57 cm\({}^{-1}\) for MIP(DEGDMA) and 1628.91 cm\({}^{-1}\) for MIP(DVB); an -OH in the carboxylic group at 887.15 cm\({}^{-1}\) for MIP(DEGDMA) and 892,49 cm\({}^{-1}\) for MIP(DVB). The presence of 4-vynilpyridine is characterized by signals of a pyridine ring at 1069.89 cm\({}^{-1}\) for MIP(DEGDMA) and 1030.52 cm\({}^{-1}\) for MIP(DVB). Also, both structures are characterized by a peak of -(CH\({}_{2}\))\({}_{\text{x}}\)- at 708.02 cm\({}^{-1}\) for MIP(DEGDMA) and 706.38 cm\({}^{-1}\) for MIP(DVB), which shows that both monomers were polymerized during synthesis. The difference in the obtained MIPs is due to the crosslinkers used during synthesis, which have different structures. This was reflected in the signals observed in the FTIR spectra. The presence of DEGDMA is characterized by the following peaks: a -CH\({}_{2}\)-CO- group at 2920.06 and 1441.52 cm\({}^{-1}\); a carbonyl group -C=O at 1285.25 cm\({}^{-1}\); and a -C-O-C- group at 1176.37 cm\({}^{-1}\). The structure of DVB in the obtained MIPs is shown by bands of variable intensity of the pyridine group at 1508.65 and 1483.93 cm\({}^{-1}\) and by out-of-plane deformation vibrations of the 1,4-substitution at 794.67 cm\({}^{-1}\). Thus, the results of the FTIR spectrometry show that the synthesis was successful.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Properties** & **MIP(DEGDMA)** & **MIP(DVB)** \\ \hline Color & Dark brown & Dark brown \\ Density & Loose & Dense \\ Odor & Strong smell of monomer & Gasoline-like smell \\ \hline \hline \end{tabular}
\end{table}
Table 2: Organoleptic analysis of obtained MIPs.  
Figure 3: Synthesized MIPs: MIP(DEGDMA) (**a**) and MIP(DVB) (**b**).  
Figure 4: FTIR spectra of MIP(DEGDMA).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Functional Group** & **Wavenumber, cm\({}^{-1}\)** & **Wavenumber, cm\({}^{-1}\)** \\  & **for MIP(DEGDMA)** & **for MIP(DVB)** \\ \hline -OH (inter- and intramolecular & \\ -H bonds in polymers) & 3418.59 & 3420 \\ -CH\({}_{2}\)-CO- & 2920.06; 1441.52 & - \\ -COOH & 1629.57 & 1628.91 \\ Aromatic ring & - & 1508.65; 1483.93 \\ -C-O- & 1285.25 & - \\ -C-O-C- & 1176.37 & - \\ Pyridine ring & 1069.89 & 1030.52 \\ -OH in -COOH & 887.15 & 892.49 \\ 1,4-substitution in & - & 794.67 \\ aromatic ring & -(CH\({}_{2}\))\({}_{\rm X}\)- & 708.02 & 706.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of FTIR spectra results for MIP(DEGDMA) and for MIP(DVB).
# Interplanetary Magnetic Field Bx Effect on Field-Aligned Currents in Different Local Times
###### Abstract  
This study explores the impact of the radial interplanetary magnetic field (IMF) on the strength and latitude of peak field-aligned currents (FACs). FACs are derived through vector magnetic field observations of the Swarm satellite mission. The analysis examines how the responses of FACs to radial IMF vary according to local time, season, and hemisphere. In the dawn and noon-midnight sectors, which are primarily influenced by westward auroral electrojets, the Northern Hemisphere (NH) exhibits stronger poleward FACs (FACp) when the IMF cone angle is \(\geq\)135\({}^{\circ}\) and weaker FACp when the cone angle is \(\leq\)45\({}^{\circ}\). In contrast, the Southern Hemisphere (SH) shows the opposite response to the IMF Bx polarity. The effect of IMF Bx is more pronounced during summer than winter, especially in the noon-to-midnight sector, while its influence on FACs is more significant during the dawn period in winter. The latitude of FACs is most strongly affected by IMF Bx around noon and midnight. A relationship is observed between FAC density and latitude in response to IMF Bx, with stronger FACp occurring at lower latitudes.  
field-aligned currents; interplanetary magnetic field Bx; interplanetary magnetic field cone angle 2025171007
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 3 Proposed Method
### Localization Comparison
#### 3.3.1 Generation of Walking Data  
We generate walking data (stride length and heading direction per step) by connecting intersections in the map.  
Firstly, as shown in Figure 5, a walking path is generated using an information board. We manually extract the passage region from the board image, resulting in a binary image (one if the pixels are in a passage region, zero otherwise). Intersections in the passage are defined by hand, and paths are created by connecting the intersections. We can dynamically create a walking path by finding a route connecting two randomly selected intersections.  
Secondly, we generate walking data using a created walking path as shown in Figure 5c. Positions on the walking path are determined with the interval of 0.6 m, which is an approximate human stride length. Seven scales of the map \(mpp=0.01\times 1.5^{a}\mid\alpha=0,1,\cdots,6\) are used to project the interval to the map image. In addition, random noise is generated in the positions since map distortion and PDR errors cause a discrepancy between the actual and generated walking paths.  
Finally, sets of stride lengths and heading directions are obtained using the determined positions. For example, the stride length \(l_{i}\) at time \(i\) is defined as \(l_{i}=\|p_{i+1}-p_{i}\|_{2}\times mpp\), where \(p_{i}=(x_{i},y_{i})\) represents the position in the map image. Likewise, the heading direction is \(\theta_{i}=\arctan\left(y_{i+1}-y_{i},x_{i+1}-x_{i}\right)\).
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
###### Abstract  
An accurate positioning and attitude computation of vehicles, robots, or even persons is of the utmost importance and critical for the success of many operations in multiple commercial, industrial, and research areas. However, most of these positioning and attitude systems rely on inertial measurement units that must be periodically recalibrated and have a high cost. In the present work, the design of a real-time positioning and attitude system using three positioning sensors based on the GNSS-RTK technology is presented. This kind of system does not need recalibration, and it allows one to define the attitude of a solid by only computing the position of the system in the global reference system and the three angles that the relative positions of the GNSS antennas define with respect to the principal axes of the solid. The position and attitude can be computed in real time for both static and dynamic scenarios. The only limitation of the system is that the antennas need to be in open air to work at full performance and accuracy. All the design phases are covered in the prototype construction: requirement definition, hardware selection, software design, assembly, and validation. The feasibility and performance of the system were tested in both static and dynamic real scenarios.  
AHRS; attitude; localization; real-time; GNSS-RTK +
Footnote †: journal:
## 1 Introduction
LDMOS without a wide drain structure has a high electric field peak at the Ndrift/\(N^{+}\) junction and a low breakdown voltage. As the wide drain region extends under the right edge of the field oxide, the breakdown voltage increases because of the suppressed electric field peak at the Ndrift/\(N^{+}\) junction, resulting in a gradual field distribution and a high breakdown voltage. However, further extending this drain region causes a high electric field peak near the right edge of the gate field plate and a low breakdown voltage. Although breakdown characteristics have been investigated in our previous studies, the high OFF-state current of FinFET with a narrow wire due to overlapping of the conduction band in the drain region with the valence band of the drift region has not. Therefore, the effect of the wide drain structure of a FinTFT on the GIDL induced by L-BTBT and ON-state current is discussed in this paper. In this study, an FP was formed to improve the breakdown characteristics of a FinTET without sacrificing the ON-state current. A tri-gate fin-like structure was adopted to suppress the short channel effects, and a wide drain design was used to reduce the electric field, thereby reducing GIDL and increasing the breakdown characteristics. The first part of this study focused on the effect of a wide drain on breakdown voltage. The electric field distribution is shown for comparison. For tri-gate FinTFTs with a wide drain, increasing the wide drain length is expected to suppress the electric field peak and increase the breakdown voltage. The second part of this study examined the effect of a wide drain length on the OFF-state GIDL current. GIDL induced by the L-BTBT of tri-gate FinTFTs with different wide drain lengths was investigated. A band diagram and the band-to-band generation rate distribution of devices with different wide drain lengths are shown. In addition, the ON-state current change with wide drain length is also important. The final part of this study analyzed the influence of wide drain length on the ON-state current of tri-gate FinTFTs with a gate field plate (GFP) or FP that has been subjected to extra-high voltage.
## 4 Proposed System Design Overview
### Optimised FTL-Assisted Edge IDS Learning
#### 4.3.1 Distributed Local Model Training with Parameter Transfer  
The FT-CID model first builds the local learning model at the edges of the heterogeneous IIoT network to train the IIoT data samples with the optimal feature set. In the FT-CID model, the IDS located at the edge of the IIoT network exploits the CNN model to perform the local learning process on the data samples of the IIoT network. With the advantage of a learning model with minimal parameters in the CNN, the proposed FT-CID adopts the CNN deep learning model for intrusion detection. The network traffic of the optimal features of the HIIoT is provided as input to the CNN. In the FT-CID, the CNN structure comprises the following layers: input, convolution, pooling, flattening, dropout, full connection, softmax, and output. According to the concept behind the TL model, layersare frozen during learning, whereas the flattening, dropout, and full connection layers are used to transfer parameters, along with the logits and weights. To retain the optimal set of features, the FT-CID model freezes the convolution and pooling layers in the CNN model. Furthermore, the softmax layer is used to optimise the local learning process.  
In the FT-CID model, the local learning model results in output in the form of logits. The FT-CID builds CNN local learning models (CNN1) using the datasets (D1). Furthermore, the updated local learning outputs of the fully connected layers with weights are fed into the global learning process as inputs through TL. The FT-CID model optimises the parameters of the local learning model with the optimal number of iterations based on the gradient of the learning results. The optimisation of the local learning model relies on weighted aggregation-based globally shared parameters and attention mechanisms. By iteratively monitoring the gradient of each local and global learning model, the FT-CID model customises the local learning model with the optimal number of iterations. Moreover, it transfers the parameters of the local learning model according to the degree of importance of each model, in addition to transferring the logits and weights to generate the global model.