esa-sceva/satcom-chunk-collection huggingface dataset:
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 5 Computational Complexity  
This section compares commonly used data detection schemes in doubly selective channels. Table 1 presents the computational complexity order required by linear and nonlinear detectors. Specifically, leveraging the receiver diversity introduced by virtual carriers during transmission enables the MRC detection algorithm to be adapted with a linear complexity of \(\mathcal{O}(N)\). This represents significantly lower complexity than linear/nonlinear detectors in an OFDM system with virtual carriers.
## 4 Representation Learning Methods for Traffic Accident Knowledge Graphs
### Translation-Based Embedding Methods
As illustrated in Figure 8, for each entity--relation pair, TransD introduces two vectors--one representing the semantics of the entity (or relation) and another for constructing the projection matrix--thereby dynamically computing the mapping matrix and performing the translation operation. This approach retains the ability to model relation-specific semantics while markedly reducing parameter count and computational complexity, thus enhancing training efficiency. The traffic accident knowledge graph constructed in this study encompasses heterogeneous entities--such as vehicles, drivers, accident locations, and environmental factors--and their intricate, diverse interrelations, resulting in frequent one-to-many, many-to-one, and many-to-many mapping scenarios. Although TransE is simple and efficient, it struggles  
Figure 8: Schematic illustration of the TransD model. Figure 6: Schematic illustration of the TransH model. Figure 7: Schematic illustration of the TransR model. to distinguish entities under such complex mappings, causing representational ambiguity. TransH mitigates this by introducing relation-specific hyperplanes, yet it incurs higher computational overhead and still lacks fine-grained semantic modeling. TransR further separates entity and relation spaces to enhance expressiveness, but it demands a distinct projection matrix per relation, significantly increasing parameter counts and computational complexity. In contrast, TransD dynamically generates mapping matrices for each entity-relation pair, preserving fine-grained semantic nuance while markedly reducing parameter redundancy and computational cost, thus flexibly and efficiently capturing the complex relationships inherent in traffic accident data. Therefore, this study adopts the TransD model for knowledge graph embedding to strike an optimal balance between expressive power and computational efficiency.
# Effect of Methane Adsorption on Mechanical Performance of Coal
## 4 Discussion
### Effects of Metamorphic Degree of Coal on Compressive Strength  
Figures 5 and 7 present the curve for the relationship between strength and elastic modulus reduction and adsorption equilibrium pressure in coal. Equations (1)-(4) were used to predict the compressive strength \(\sigma_{c}\) and elastic modulus E of coal samples under different equilibrium pressures for methane adsorption. As shown in Figure 7, in a triaxial stress environment and after methane adsorption, a more significant strength decrease was observed in high-rank coals as compared to low-rank coals. When equilibrium pressure for methane adsorption was 3.0 MPa, the compressive strength and elastic modulus of meager coal decreased by 14.91% and 19.67%, respectively. It was also observed that compressive strength and elastic modulus of gas coal decreased by 7.63% and 8.67%, correspondingly. When the equilibrium pressure for methane adsorption displayed a value of 6.0 MPa, the compressive strength and elastic modulus of meager coal decreased by 23.17% and 32.76%, respectively, while those of gas coal decreased by 10.9% and 11.29%, correspondingly. The natural coal cleat system that is formed during the process of coalification is much more developed in high-rank coals than in low-rank coals. Thus, gas coal and meager coal present low and high stiffness, respectively. For this reason, meager coal shows a larger expansion after methane adsorption, which in consequence can produce more structural changes in the coal matrix.  
Figure 7: Prediction of compressive strength and elastic modulus of coal samples under different methane adsorption equilibrium pressures. (**a**) Lean coal. (**b**) Gas coal.
## 2 Materials and Methods
### Mass Spectrometry  
NSC34 cells were transfected with PEGFP-hSMN-HH227, 273 RR and differentiated for 72 h. Upon lysis, as described above, SMN-EGFP was pulled down with a mouse \(\alpha\)-GFP antibody (Roche 11814460001, LOT 14442000) bound to Dynabeads protein G, washed three times with IP buffer and eluted in Laemmli buffer. Proteins were separated by SDS-PAGE and the gel was stained with Coomassieie Brilliant Blue (BioRad #161-0436). The expected band in the range of 60-65 kDa was excised and subjected to tryptic in-gel digestion as described previously [32]. To identify the protein composition of the gel band, 10% of the sample were desalted and analyzed by LC-MSMS. For sensitive detection of phospho-peptides, 90% of the extracted tryptic peptides from the gel band were enriched with the High-Select(tm) TiO\({}_{2}\) Phosphopeptide Enrichment Kit (Thermo Fisher Scientific) following the manufacturer's instructions. LC-MSMS analysis of peptides was performed on an Ultimate 3000 RSLC nano system online coupled to an Orbitrap Q Excative Plus mass spectrometer (both Thermo Scientific, Bremen, Germany). The system comprised a 75 um i.d. \(\times\) 250 mm nano LC column (Acclaim PepMap C18, 2 um; 100 A; Thermo Fisher Scientific). Data were acquired in a data-dependent acquisition mode using a Top10 method with the following parameters: full MS spectra, 300-1800 \(m\)/z, resolution of 70.000, AGC target 1 \(\times\) 10\({}^{6}\), max IT 50 ms, MSMS spectra, 200-2000 \(m\)/z, resolution 17.500; AGC target 5 \(\times\) 10\({}^{5}\), max IT 120 ms, 1\({}^{+}\) charge state excluded, isolation window of 1.6 \(m\)/z, normalized collision energy of 27%). For phospho-peptides measurements, parameters were changed to AGC target 1 \(\times\) 10\({}^{6}\) and max IT 120 ms (MS), AGC target 5 \(\times\) 10\({}^{4}\) and max IT 500 ms (MSMS). Data were analyzed with Mascot software version 2.6.1 (Matrix Science Ltd., London, UK) and databases SwissProt 2018_07 (_Mus musculus_ 17,043 sequences), a contaminant database (247 sequences) and an in-house-database (395 sequences including the entry of hSMN-HH227,273 RR-EGFP). The following parameters were selected: trypsin/P, three missed cleavages, variable modifications, carbamidomethylation (C), propionamide, oxidation (M), phosphorylation (S, T, Y), Acetyl (Protein N-term), pyro-glu (Q), mass tolerances for MS and MSMS: 5 ppm and 0.02 Da. Proteins were accepted as identified if at least two unique peptides provided a Mascot MSMS score for identity (\(p<0.01\)). In addition, MSMS spectra of phospho-peptides were validated by manual inspection.
# A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
## 4 Discussion
For instance, it would be useful to define the parameters \(q\) and \(m\) for the main common types of weed community [21], even if this requires experimental activities on site. Regarding the influence of flight height, the reduction in the ground resolution of the processed images passing from 10 to 20 m led to a further loss in accuracy for the M1 and a slight improvement for the M2-WGC\({}_{\rm WP+CP}\) and a limited worsening for the M2-MGC\({}_{\rm WP+CP}\). This means that the degradation of images made in the joint estimation of AM and AW are even less accurate, whereas the increase in the Ca-TGC\({}_{\rm PWP}\) and consequently in the M2-MGC\({}_{\rm WP+CP}\) estimation at 20 m had the effect of bringing M2-WGC\({}_{\rm WP+CP}\) (underestimated at 10 m) closer to the observed data. The improvement in the M2-WGC\({}_{\rm WP+CP}\) estimation at 20 m did not lead to an improvement of RYL fitting, probably because of too high a value attributed to \(q\) (the damage coefficient of the weed population). Finally, the determination of the economic threshold using the three curves (C1, C2 and C3) did not lead to great differences. The deviation between ET-C2 and ET-C1 was equal to \(-16\%\) and the deviation between ET-C3 and ET-C1 was equal to \(-33\%\), but the values attributed to the \(q\) and \(m\) parameters in C2 and C3 seem to hardly be justified. From a technical point of view, the solution of using M2 at 20 m may be acceptable in usual farming practices in relation to a remarkable reduction (about one-half) of the flight times.
## 3 Proposed Charging Station Modeling
### The Charging Converters Model  
The proposed system charging converters are called the ESS converter and the EV charge converter. Both are DC/DC converters. However, the EV charge converter is of a step-down type. On the other hand, the ESS converter is an up/down-type converter. It is capable of two-directional operations. The two converters may have the same topology, as shown in Figure 3. However, the operation of the topology may be adapted to act as a one-quadrant or two-quadrant converter. It is assumed that the converters operate in the continuous conduction mode. The converter consists of two transistors (Q\({}_{1}\), Q\({}_{2}\)) and a filter. Usually, each transistor has a built-in parallel diode. The input to the converter is the DC link voltage and the output is the ESS. There are two modes for the converter, called the step-down/up modes. It can operate in the step-down mode when the transistor Q\({}_{1}\) is on and the transistor Q\({}_{2}\) is off, and this occurs in the charging process of the ESS. On the other hand, it operates in the step-up mode when switch Q\({}_{2}\) is on and the switch Q\({}_{1}\) is off, and this occurs in the discharging process of the ESS.  
The model of the converter can be presented as:  
The step-down mode of operation:  
\[\mathbf{X}=\left[\begin{array}{c}i_{l}\\ v_{b}\end{array}\right] \tag{1}\]  
\[\dot{\mathbf{X}}=\left[\begin{array}{cc}0&\frac{-1}{L}\\ \frac{1}{C}&\frac{-1}{r_{b}C}\end{array}\right]\mathbf{X}+\left[\begin{array}{c} \frac{V_{dc}}{L}\\ 0\end{array}\right]S_{1}+\left[\begin{array}{c}0\\ \frac{E_{b}}{r_{b}C}\end{array}\right] \tag{2}\]  
where (\(v_{b}\), \(i_{l}\)) are the ESS terminal voltage and the inductor current, (\(r_{b}\), \(E_{b}\)) are the ESS internal resistance and voltage, \(S_{1}\) is a binary number that represents the switch S\({}_{1}\) state, \(V_{dc}\) is the DC link voltage, and (\(C\), \(L\)) are the filter capacitance and the inductance.  
The step-up mode of operation:  
\[\dot{\mathbf{X}}=\left[\begin{array}{cc}0&\frac{-1}{L}\\ \frac{1}{C}&\frac{-1}{C_{b}}\end{array}\right]\mathbf{X}+\left[\begin{array}{c} \frac{-V_{dc}}{L}\\ 0\end{array}\right]S_{2}+\left[\begin{array}{c}\frac{V_{dc}}{L}\\ \frac{E_{b}}{r_{b}}\end{array}\right] \tag{3}\]  
where \(S_{2}\) is a binary number that represents the switch \(S_{2}\) state.
## 1 Introduction  
Deepfakes represent synthetic media generated through artificial intelligence and machine learning techniques [1]. In recent years, the technologies within AI and machine learning have grown into potent tools that can be used for malicious activities [2]. This could be impersonation or disinformation. Early discussions focused mainly on their potential misuse in politics or celebrity culture. Nowadays, however, there is growing awareness that corporate environments are also at high risk. Deepfake-driven social engineering poses a significant challenge by creating hyper-realistic forgeries such as audio or video impersonations of executives or stakeholders that can bypass traditional security measures [3].  
A _critical gap_ remains in both the academic literature and industry practice: there is insufficient attention devoted to deepfake-driven social engineering at scale within corporate environments [4]. Although several studies have addressed deepfake generation and detection in general [5, 6], only a few have investigated how these synthetic media threats exploit structures and communication channels that are of great significance to modern enterprises. As illustrated in Table 1, this comparative overview reveals key gaps in the current cybersecurity practices that highlight the need for additional research to improve organizational preparedness for deepfake-driven social engineering threats. The existing corporate defense protocols, largely oriented toward classic phishing or generic malware, often fail to anticipate the sophistication and persuasive realism of deepfake attacks [7].  
To address these concerns, the aim of this paper is to bridge the research gap between theoretical deepfake capabilities and the limited, often reactive countermeasures employed by modern organizations. This work examines the effectiveness of the current detection methods and aligns them with practical defensive strategies. In this way, it extends the conversation beyond the abstract dangers of deepfakes with the goal of providing actionable insights for corporate stakeholders.
# CAPDepth: 360 Monocular Depth Estimation by Content-Aware Projection
## 2 Related Work  
Compared with traditional perspective images, panoramic images offer a wider FoV and a richer geometric context. The number of studies on depth estimation for omnidirectional images has increased. For example, some studies have explored monocular depth estimation of 360\({}^{\circ}\) images from various angles. ACDNet [9] replaces conventional convolutions with adaptive dilated convolutions, enabling more effective feature extraction on panoramic images due to corrected receptive fields. Furthermore, OmniDepth [3] adjusts filter sizes for different latitudes to handle varying degrees of distortion. HoHoNet [18] and SliceNet [19] consider the gravity direction, directly applying standard convolution layers vertically to predict ERP depth maps. M3 PT [14] reconstructs and completes the panoramic depth in masked areas of panoramic images and sparse depths using multimodal inputs and shared random masks.  
In addition to directly addressing ERP distortion issues, using other representations with less distortion has also demonstrated superior results. This is because less distortion can lead to more effective use of pretrained CNN models developed for perspective imaging. Examples include using cubemap [6; 20], the fusion of ERP and cubemap [12; 13; 21], and using tangential images to represent 360\({}^{\circ}\) images [7]. Moreover, 360 MonoDepth [15] and OmniFusion [16] extract depth features using CNNs based on tangential images and predict the final depth map after fusion. In contrast, PanoFormer [22] samples the eight most relevant markers for each center marker on ERP to form tangential patches instead of segmenting the ERP image. However, due to inference-speed limitations, PanoFormer overlooks the local 2D structure and spatial information within each patch [23]. HRD-Fuse [17] builds upon OmniFusion by further learning the overall contextual information of ERP images and the regional structural information of patches. The method enhances the extraction and fusion of geometric features, but the distortion in the tangential images still reduces the depth estimation accuracy.  
In this work, we follow the paradigm of using tangential images as proposed in [16], but this study focuses on the distortion in tangential image projections. Using a superior projection method and an adaptive geometric information fusion strategy, this study achieves higher accuracy of depth estimation.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## Theory  
### [PERSON] et al. [22] reported that any signal is made up of several intrinsic mode functions (IMFs). The instantaneous frequency of any point of each IMF is meaningful. A signal is formed by the superposition of many IMFs. [PERSON] proposed the EEMD algorithm in 2009, that is, the white Gaussian noise is introduced in the decomposition process. Although white Gaussian noise is added to each IMF, as the noise added at each time of decomposition is random, it can be eliminated by the decomposition of enough times and the ensemble averaging. The procedure of EEMD is as follows:  
Step 1: White Gaussian noise with the mean value of zero and constant amplitude standard deviation is added to the decomposed signal \(x(t)\), and the signal after adding noise is normalized.  
Step 2: EMD is used to decompose the normalized signal to obtain each IMF.  
Step 3: Step 1 and Step 2 are repeated \(n\) times, ensuring the noise added at every time obeys a normal distribution.  
Step 4: Ensemble averaging of the \(n\) sets of IMFs obtained from EMD. According to the rule in statistics, the statistical mean of an uncorrelated random sequence is zero, and the influence of white Gaussian noise added to IMFs is removed. Finally, the decomposition of \(x(t)\) can be obtained by EEMD, that is,  
\[x(t)=\sum_{i=1}^{n}\sum_{j=1}^{n}c_{ij}(t)+\sum_{j=1}^{n}r_{j}(t), \tag{1}\]  
where \(c_{i}(t)\) is the \(i\)-th IMF, and \(r(t)\) is redundancy.  
The size of the white Gaussian noise added in the iterative process of EMD can directly affect the result of EEMD. The purpose of adding noise in EEMD is to avoid modal aliasing. However, if the noise is too large, it will affect the distribution characteristics of the extreme point intervals of effective high-frequency components. When the noise is too small, it will affect the selection of low-frequency extreme points of signal and lose the function of scale supplement. The criterion for adding white noise to EEMD is as follows:  
\[\delta=\frac{\eta_{h}}{\eta_{f}};\beta=\frac{\eta_{g}}{\eta_{f}};0<\beta< \frac{\delta}{2}, \tag{2}\]  
where \(\eta_{h}\) is the amplitude standard deviation of effective high-frequency component of \(x(t)\), \(\eta_{g}\) is the amplitude standard deviation of the added white Gaussian noise, and \(\eta_{f}\) is the amplitude standard deviation of \(x(t)\). Under normal circumstances, when \(\beta=\delta/4\), the modal aliasing problem can be effectively avoided. When \(\eta_{g}=0.4\) and the number of the ensemble average is 100, a better processing result can be obtained.  
EEMD can not only effectively avoid modal aliasing, reduce signal noise and get more meaningful IMFs, it can also decompose a one-dimensional observation signal into a multi-dimensional one, and create a condition for blind source separation.
## 2 Data and Methods
### U-Net-Based Models  
The U-Net and UNet3+ architectures, provided by [PERSON] et al. (2015) and [PERSON] et al. (2020), respectively, form the basis of most of the neural network models described in this study (see Figure 2) [27; 43]. The U-Net design comprises an encoder and a decoder. The encoder uses double convolution and max pooling operations to extract features, while the decoder uses the same layers for upsampling [29]. U-Net employs a pixel-to-pixel mapping technique between the input and output images. UNet3+ is an improved version of the U-Net model that utilizes full-scale jump connections to merge low-level information with high-level semantics from feature maps of varying sizes, thereby optimizing the usage of full-scale feature maps [35; 36; 37].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable Name** & **Physical Meaning** \\ \hline gfs\_precipitation & GFS cumulative precipitation prediction \\ Gust & Gust intensity \\ Sp & Sea pressure \\ Cape & Convective available potential energy \\ Cin & Convective inhibition \\ Pwat & Precipitable water \\  
Lsm & Land and sea mask (1 means land, 0 means sea) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variable names and their corresponding physical meanings.  
SE-UNet and SE-UNet3+ are U-Net and UNet3+ models that incorporate attention modulation. This modulation directs the network towards task-relevant aspects by suppressing feature responses in irrelevant settings [32; 33]. Various attention strategies have been proposed to improve neural network performance [44; 45]. In this study, we aim to improve precipitation prediction accuracy by incorporating Squeeze-and-Excitation (SE) modules into the U-Net family of networks. Figure 2 illustrates the incorporation of an SE module behind each encoder layer, resulting in the SE-UNet and SE-UNet3+ models. For comparison, the U-Net and UNet3+ models, lacking an SE module, are also used in this study. Additionally, we develop a neural network using the VGG16 architecture to compare the generalizability of neural networks with varying architectures. The structure of VGG is illustrated in Figure S1 (see Supplementary Materials for further details).  
The neural network generates an 80 \(\times\) 80 single-channel image, representing the cumulative precipitation forecast for various lead times. To assess the model's performance against GPM precipitation observation, we use the mean square error between GPM observation and model precipitation forecast as the neural network's loss function, as demonstrated in Formula (1).  
\[\text{Loss}=\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2} \tag{1}\]  
Figure 2: Structures of (**a**) U-Net and SE-UNet and (**b**) UNet3+ and SE-UNet3+.  
where \(y_{i}\) is the GPM precipitation observation and \(\dot{y}_{i}\) is the model precipitation forecast. The adaptive moment estimation (Adam) optimizer is used to optimize the network parameters [46].
## 2 Experimental Programs  
### Specimen Design  
In order to reveal the similarities and differences between the beams with cracks before the formal operation and the complete beam in terms of service performance and failure mechanism, pre-cracked RC test beams were manufactured with a T-type cross section, based on scale ratio similarity theory [38], due to the constraints of test sites and the maximum range of the equipment. The concrete strength grade was C50 with P.O 42.5 cement. The water-cement ratio was 0.31 and the sand rate was 0.39. The amount of concrete material per cubic meter consisted of 160 kg water, 460 kg cement, 1062 kg coarse aggregate, 678 kg fine aggregate, 10.2 kg admixture, and 50 kg fly ash. The density of the material was 2410 kg/m\({}^{3}\). The length of the test beam was 2 m, and the calculated span was 1.9 m. HRB400 steel bar was used. The yield and ultimate strength of the HRB400 steel bar was 489 MPa and 618 MPa, respectively. The elongation was 14.6% and the Young's modulus was 210 GPa. The diameter of the bottom tension bar was 12 mm. The diameter of the other longitudinal steel bars was 10 mm. The diameter of the stirrup was 8 mm. Figure 1 shows the cross-section size and reinforcement of the test beam.  
The prefabricated cracks were arranged at the mid-span and \(1/4\) position on the left side of the test beams. The prefabricated crack was performed by inserting thin sections during concrete placement. The crack width and depth are the two main parameters to describe the characteristics of the crack, and only the influence of the crack depth was considered in this study. The crack depth was set at 20 mm and 60 mm, and the prefabricated crack width of the test beams was 1 mm. In addition, the mechanical method was adopted to polish the diameter of the steel bar at the mid-span prefabricated crack from 12 mm to 10 mm, to study the influence of the steel bar diameter at the crack on the bearing capacity of the test beam. A total of seven test T-beams were made. The test-condition settings are listed in Table 1.