esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 4 Result and Discussion
### Ablation Experiment  
Ablation experiments are used to compare several improved methods proposed in this paper. The results are listed in Table 2. Where CN is combined network, OP is overlap pooling, ICN is improved combined network we proposed, CC is the number of correct classifications, P is average accuracy, R is average recall, F1 is average F1-score, Acc is overall classification accuracy, and K (Kappa) is consistency index.  
Table 2 illustrates the performance of the model at various stages from the basic structure to the final structure. In the ICN model, accuracy of the MGCD dataset is increased by 2.03% and 2.88%, respectively, compared with the sub-networks ResNet50 and VGG16, while the accuracy of the NRELCD dataset is increased by 1.56% and 1.45%, respectively. Figure 10 is the accuracy curve of the model on different datasets as the epoch increases. As shown in Figure 9a, VGG16 produces overfitting in training on MGCD dataset. At this time, the parameters of the ResNet50 are still being further optimized. The overfitting phenomenon is suppressed effectively on improved VGG16(VGG16 + OP + ECA-WS), and the parameter optimization of the two sub-networks is close to synchronization, which provides a good prerequisite for decision fusion. As illustrated in Figure 10b, improved VGG16 is better than VGG16 in performance on the NRELCD dataset with a larger scale. The optimization process of the two sub-networks is also al  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**MGCD/4000 Test Samples**} & \multicolumn{6}{c}{**NRELCD/4650 Test Samples**} \\ \cline{2-13}  & **CC** & **P** & **R** & **F1** & **Acc** & **K** & **CC** & **P** & **R** & **F1** & **Acc** & **K** \\ \hline ResNet50 & 3522 & 85.67\% & 86.12\% & 85.69\% & 88.05\% & 0.8593 & 4373 & 93.33\% & 93.12\% & 93.12\% & 94.04\% & 0.9291 \\ VGG16 & 3488 & 84.77\% & 84.86\% & 84.76\% & 87.20\% & 0.8492 & 4378 & 93.40\% & 93.30\% & 93.27\% & 94.15\% & 0.9304 \\ CN & 3551 & 86.23\% & 87.02\% & 86.53\% & 88.78\% & 0.8679 & 4411 & 94.17\% & 94.23\% & 94.17\% & 94.86\% & 0.9389 \\ CN + OP & 3557 & 86.77\% & 86.87\% & 86.69\% & 88.93\% & 0.8696 & 4436 & 94.88\% & 94.67\% & 94.77\% & 95.40\% & 0.9453 \\ CN + OP + ECA & 3585 & 87.59\% & 87.91\% & 87.55\% & 89.63\% & 0.878 & 4440 & 94.91\% & 94.79\% & 94.84\% & 95.55\% & 0.9471 \\ ICN & 3603 & 88.09\% & 88.15\% & 87.85\% & 90.08\% & 0.8834 & 4445 & 95.11\% & 94.93\% & 95.01\% & 95.60\% & 0.9477 \\ \hline \end{tabular}
\end{table}
Table 2: Ablation experiment.
## 3 Results and Discussion
### Phenolic Profile of the Kiwi Peel Extract and Experimental Data for UAE Optimization  
Kiwi is rich in polyphenols with antioxidant activity and often described as a \"super-fruit\" due to its low caloric value and high amount of water, dietary fiber, and vitamin C, among other nutrients [21,22]. The HPLC-DAD-ESI/MS\({}^{n}\) (Table 1) analysis allowed the tentative identification of two quercetin glycosides, namely quercetin-3-O-glucoside (identified based on the pseudomolecular ion [M-H]+ at _m/z_ 463 and the fragment ion at _m/z_ 301 and by comparison with the available standard compound) and quercetin-3-O-ramnoside (pseudomolecular ion [M-H]+ at _m/z_ at 477 and fragment ion at _m/z_ 301), as also two flavan-3-ols, namely epicatechin (identified based on the pseudomolecular ion [M-H]+ at _m/z_ 289 and fragments at _m/z_ 245, 205, and 179), and a B-type (epi)catechin dimer (pseudomolecular ion [M-H]+ at _m/z_ 577 and fragments at _m/z_ 451, 425, 407, and 289). A representative HPLC phenolic profile of the kiwi peel extracts is present in Supplementary Materials Figure S1.  
The results of the 20 experimental runs of the CCRD design applied to optimize the UAE of flavonoids from the kiwi peel are presented in Table 2. The extraction yield ranged from approximately 37% to 59% with runs 14, involving 100% ethanol and medium levels of the other two independent variables, and 12 and 4, which combined 500 W power with medium levels (0) of the other variables and medium-high levels of time and ultrasonic power with a medium-low ethanol concentration, respectively. In turn, the total content of flavonoids ranged from 1.00 to 1.75 mg/g of extract and the lowest values were obtained with runs 9 and 10, corresponding to the use of the lowest and highest levels (\(\alpha\) values) of the variable time, mainly due to the low levels of epicatechin achieved with these runs. In general, the recovery of the B-type (epi)catechin dimer appeared to have been promoted by the two axial points corresponding to the extraction solvent (runs 13 and 14), while both quercetin glycosides were better extracted with the run 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Peak** & **Rt (min)** & \(\lambda_{\text{max}}\) (nm)** & **[M-H]+ (m/z)** & **MS\({}^{2}\) (m/z)** & **Tentative Identification** \\ \hline
1 A & 5.85 & 281 & 577 & 451(27), 425(100), 407(32), 289(10) & B-type (epi)catechin dimer \\
2 A & 7.63 & 280 & 289 & 245(100), 205(41), 179(17) & Epicatechin \\
3 B & 18.71 & 352 & 463 & 301(100) & Quercetin-3-O-glucoside \\
4 B & 20.28 & 351 & 477 & 301(100) & Quercetin-3-O-rhammoside \\ \hline \end{tabular}
\end{table}
Table 1: Flavonoids composition of the kiwi peel extract. The retention time (Rt), wavelengths of maximum absorption in the visible region (\(\lambda_{\text{max}}\)), and mass spectral data are presented.
## 4 Results and Discussion
### Comparison of SOE Estimation of Different Models  
To verify the performance of the proposed model, we employed a BiLSTM model and LSTM model for comparison analysis. Additionally, the LSTM hyperparameters were configured according to reference [30], taking current, voltage, temperature, and mechanical force as input features [30]. The hyperparameters of BiLSTM are consistent with those of the proposed model. The test dataset was selected from the FUDS at a temperature of 35 \({}^{\circ}\)C and with a preload force of 500 N.  
The three evaluation criteria of SOE estimation results are shown in Table 4. For different models, the addition of the mechanical force achieves a significant improvement in all three performance metrics, which further demonstrates the potential of the force feature in SOE estimation of LFP batteries. Compared to the criteria of the LSTM network, the RMSE, MAXE, and MAE of BiLSTM with the bidirectional recurrent structure reduce from 2.34%, 9.33%, and 1.74% to 1.69%, 6.05%, and 1.27%, respectively. The integration of PSO-KF with BiLSTM further improves the accuracy of SOE estimation with an RMSE of 0.62%, MAXE of 2.66%, and MAE of 0.48%.  
Figure 9: SOE estimation results at different temperatures: (**a**) SOE curves at 15 \({}^{\circ}\)C; (**b**) SOE errors at 15 \({}^{\circ}\)C; (**c**) SOE curves at 35 \({}^{\circ}\)C; (**d**) SOE errors at 35 \({}^{\circ}\)C.  
Figure 10 illustrates the estimation results of various models. The BiLSTM-PSO-KF model demonstrates superior performance with estimation results that are the closest to the actual curves, and exhibits better fitting capabilities compared to other models. It should be noted that the estimation accuracy exhibits a slight decrease in the SOE range of 0.4-0.6. This phenomenon can be attributed to two primary factors. One factor is the nonlinear relationship between force and SOE, which features a distinct peak with an opposite trend within this range. Additionally, a comparison of the curves in Figure 3(a1-a3) reveals that the FUNDS exhibits a longer cycle duration compared to the DST and the BJDST, with larger fluctuations within each cycle.
## 2 The FOWT-SFFC Concept
### Wind Turbine and Tower  
For subsequent stability and seakeeping analyses, the famous National Renewable Energy Laboratory (NREL) 5-MW wind turbine [33] is tentatively adopted for integration with the octagonal fishing cage. The cut-in, rated and cut-out wind speed are respectively 3 m/s, 11.4 m/s, 25 m/s. For the FOWT-SFFC concept herein, the geometries of wind tower are identical to those in OC3 Hywind [32] (for instance, height of tower is still 77.6 m) except that the tower base is 15 m above SWL as mentioned in Section 2.1 and the hub height is 95 m above SWL. More details can be found in the work by [PERSON] et al. [32; 33]. It should be mentioned that nowadays, the offshore wind industry is developing so rapidly that upscale wind turbines of rated power beyond 10 MW would be employed in the near future [34; 35; 36; 37]. In this study as our focus is on the feasibility and performance of the FOWT-SFFC concept, the NREL 5 MW wind turbine suffices to meet the requirement of analyses. For an upscale wind turbine, dimensions of key structural members need to be adjusted to accommodate a larger displacement and variations in buoyancy and gravitational centers and so on.
## 2 Database Establishment
### Channel Model  
The mmWave channel is generated by combining the ray-tracing and geometric-channel models [8]. The paths in mmWave-channel modeling are equivalent to the rays in ray tracing. From this, the strongest L-path information can be obtained: \(\{\phi_{\ell}^{A},\phi_{\ell}^{A},\phi_{\ell}^{D},\phi_{\ell}^{D},\tau_{\ell},\mathbf{a}_{\ell}\}\),\(\ell=\{1,2,\cdots,L\}\), where \((\phi_{\ell}^{A},\phi_{\ell}^{A})\) stands for the azimuth and the elevation of arrival, and \((\phi_{\ell}^{D},\phi_{\ell}^{D})\) stands for the azimuth and the elevation of departure. Moreover, a stands for the path gain for the i-th ray, and \(\tau\) stands for the time of arrival. Uniform planar arrays (\(4\times 2\)) (\(N_{l}=N_{r}=8\)) are deployed at the transmitter and receiver. We approximately named the channel matrix of the geometric channel model as \(\mathbf{H}[n],\;n=0,1,\cdots L_{c}-1\).  
\[\mathbf{H}[n]=\sqrt{N_{l}N_{r}}\sum\
olimits_{\ell=1}^{L}g(nT-\tau_{\ell})\mathbf{a}_ {r}\Big{(}\phi_{\ell}^{A},\phi_{\ell}^{A}\Big{)}\mathbf{a}_{r}^{*}\Big{(}\phi_{ \ell}^{D},\phi_{\ell}^{D}\Big{)}\mathbf{a}_{\ell}, \tag{1}\]  
where \(\mathrm{g}\left(\cdot\right)\) is the pulse-shaping filter; \(\mathbf{a}_{r}(\phi_{\ell}^{A},\phi_{\ell}^{A})\) is the steering vector on the arrival side of the uniform planar array; \(\mathbf{a}_{i}^{*}(\phi_{\ell}^{D},\phi_{\ell}^{D})\) is the steering vector leaving the measurement; and \(T\) is the symbol period. The code groups used in the predictive encoder and combiner are DFT codes. The number of pairs in the dataset is \(N_{B}=N_{l}N_{r}=64\). The encoded information corresponding to the \(i\)-th pair of beams is \((\mathbf{w}_{i},\mathbf{f}_{i})\), \(i\in\{1,2,\cdots N_{B}\}\). The acquired power \((\mathbf{y}_{i})\) of the corresponding \(i\)-th pair of beams is:  
\[\mathbf{y}_{i}=\sum\
olimits_{n=0}^{L_{c}-1}\lvert\mathbf{w}_{i}^{*}H[n]\mathbf{f}_{i} \rvert^{2}, \tag{2}\]  
The beam-power regression-training label is \(\mathbf{y}=[y_{1},y_{2},\cdots y_{N_{B}}]\), and the optimal corresponding beam power is \(s=\mathrm{argmax}_{i}\in\{1,\cdots N_{B}\}\mathbf{y}_{i}\).  
Figure 1: Ray-tracing simulation in Urban CanyonChannel model.
## 5 Conclusions and Outlook  
### Conclusions  
This paper pre-selects grassland fire influencing factors from 2000 to 2020 and analyzes the primary factors affecting grassland fires in Qinghai Province based on historical fire data records. It further discusses grassland fire susceptibility by employing the MaxEnt model alongside historical data to examine the underlying causes of these fires. The results indicate the following:  
1. The model simulated natural grassland fire susceptibility with an AUC of 0.947 (\(\pm\)0.024) and anthropogenic grassland fire susceptibility with an AUC of 0.978 (\(\pm\)0.002). The high-susceptibility zones had G values of 0.87 and 0.92, respectively. The model demonstrated high precision, reliability, and accuracy, making it suitable for evaluating grassland fire susceptibility in Qinghai Province.
2. Grassland fire susceptibility in Qinghai Province exhibits clear regional and geographical characteristics. High-susceptibility areas for natural grassland fires are primarily located in the southern region, encompassing most of Guoluo Tibetan Autonomous Prefecture, Zeku County in Hainan Tibetan Autonomous Prefecture, Henan Mongolian Autonomous County, and Yushu City in Yushu Tibetan Autonomous Prefecture. Conversely, high-susceptibility areas for anthropogenic grassland fires are found in the eastern parts of Huangnan Tibetan Autonomous Prefecture, Haidong City, and eastern Hainan and Haibei Tibetan Autonomous Prefectures, where the intensity of human activities is high and settlements are prevalent.
3. The influencing factors for natural grassland fire susceptibility include distance to rivers, vegetation type, multi-year average precipitation, and average wind speed. For anthropogenic grassland fire susceptibility, the key influencing factors are NDVI, relative humidity, population density, distance to settlements, and the human footprint index. Additionally, water sources and combustible loads act as accelerators for natural grassland fire occurrences, whereas human activities serve as an accelerating factor for anthropogenic grassland fires.
4. Among the grassland fires with identified causes, natural fires were predominantly caused by spontaneous combustion, accounting for 6.67%. Fires attributed to human activities accounted for 93.33%, with high-voltage-line contact, heating during grazing, and firecrackers being the primary causes.
## 3 Service Requirements Analysis of the Data Center Management System
Because this type of service involves real-time transmission and feedback of electricity prices and data information, communication network latency requirements are high to ensure fast response and real-time control of the service. To prevent the theft, tampering, or destruction of electricity market transaction data and information, the communication technology carrying this service should have a high degree of security protection capability. In addition, to support power market data transmission and information sharing between different regions and regions, this type of service requires high communication network transmission distance. The adaptability evaluation system of service and communication technology is shown in the Figure 3.  
Figure 3: Adaptability evaluation system of service and communication technology.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 5 Challenges and Future Research Directions
### Ethical and Privacy Considerations  
As Generative AI becomes integral to securing IoV ecosystems, several ethical and privacy challenges arise that must be explicitly addressed to ensure responsible deployment. A primary ethical concern is the potential misuse of synthetic attack data. While Generative Adversarial Networks (GANs) and other generative models can produce valuable synthetic datasets to train or evaluate IDSs, such data could be exploited by malicious actors if leaked or improperly shared. The availability of realistic synthetic attack patterns may inadvertently lower the barrier to crafting new, evasive cyberattacks targeting vehicular networks.  
Furthermore, data privacy concerns are particularly pronounced in federated learning scenarios, where generative models are trained across distributed vehicle nodes without centralized data collection. Although federated learning is privacy-preserving by design, recent studies have shown that generative models can memorize and leak sensitive user data through side channels or model updates. Attacks like membership inference or gradient inversion can extract identifiable information from model parameters, undermining the foundational privacy guarantees of such systems. To mitigate these risks, techniques such as differential privacy, secure multi-party computation, and encrypted model aggregation should be incorporated into federated generative IDS designs.  
Equally important is the issue of transparency and explainability, especially given the safety-critical nature of vehicular systems. Many Generative AI models--including Transformers and VAEs--operate as black boxes, providing limited insight into why a particular decision was made. In high-stakes contexts such as autonomous driving or real-time traffic coordination, explainability is essential to enable human oversight, legal accountability, and system debugging. Incorporating interpretable AI techniques--such as attention visualization, saliency maps, or post hoc explanation frameworks like SHAP or LIME--can help make generative IDSs more transparent and trustworthy to stakeholders.
# A Transformer-Based Channel Estimation Method for OTFS Systems
###### Abstract
In [18], a two-dimensional CNN was proposed to accomplish signal detection in OTFS systems. The 2D-CNN can incorporate the imaginary part of the signal during training and the online phase to acquire a three-dimensional array channel. This method has a significant performance improvement over current methods in high-Doppler channels. In [19], deep RNN was used for channel prediction for the MIMO system, the proposed channel predictor contained a long short-term neural network (LSTM) and gated recurrent unit (GRU) to predict the channel state information (CSI). The authors in [20] proposed a 2D-convolutional long short-term memory network (2D-ConvLSTM) to estimate the channel coefficients. In this context, the channel is considered a two-dimensional convolution in the DD domain, which allows for the utilization of the 2D-ConvLSTM network to predict these coefficients. In [21], a deep residual learning network (ResNet) was used to enhance the performance of traditional channel estimation algorithms. The LSTM and implied temporal correlation between channels were used in the channel prediction in [22; 23]. In this work, we employ a predictive approach to enhance the performance of channel estimation by considering the implied time correlation. By capturing and utilizing this time correlation information, we aim to make predictions about the channel state and improve the accuracy of the estimation process. First, we obtain preliminary channel estimation results by using the algorithm in the literature [6]. The preliminary results are considered as time series due to the temporal correlation between channels, and then the series are put into the neural network to obtain more accurate channel estimation results. The remainder of this paper is outlined as follows: Section 2 describes the system model and threshold channel estimation algorithm. Then, the proposed channel estimation algorithm based on a transformer neural network is presented in Section 3. Simulation results are provided in Section 4, and the paper concludes in Section 5.
## 4 Results and Discussion
### Data Analysis
#### 4.2.8. Drainage EC  
As shown in Figure 19, the average values of drainage EC data were observed to be 3.5 dS/m, 3.2 dS/m, 3.0 dS/m, and 3.8 dS/m, respectively. Similar to the soil EC, the average drainage EC showed a decreasing trend from 3.5 dS/m in June to 3.0 dS/m in August but experienced a sharp increase to 3.8 dS/m in September. The minimum drainage EC levels were observed to be 2.96 dS/m, 2.44 dS/m, 2.38 dS/m, and 2.59 dS/m, respectively, with August recording the lowest minimum drainage EC. The maximum drainage EC levels were observed to be 4.78 dS/m, 4.66 dS/m, 5.43 dS/m, and 6.92 dS/m, respectively, with September recording the highest maximum drainage EC.  
Figure 17: Insolation data.  
Figure 18: Soil EC data.
## 3 Results
### Phone Call Scenario  
Since the very low distance between the antenna and the head (2 mm), the maximum value of local SAR was significant in all the human models (Table 2), but only in the superficial tissue (limited to the ear skin), as shown in Figure 3. The maximum local SAR value was in alignment with the ear apex for Duke, Ella, and Billie models and was also in alignment of the tragus for Thelionious.  
All of the obtained Sab values were well below the safety limit imposed by the most recent ICNIRP guidelines, that is 20 W/m\({}^{2}\).  
Billie had the highest value for the local SAR and Sab. Moreover, Ella had the lowest value of the local SAR, which was comparable to the maximum local SAR of Duke. The SAR distribution in the head shows that only a very small portion of the ear skin presented a high value of SAR for all the models. From the axial view, the power absorption was limited to the superficial tissue in the head for all the human models. The local SAR fell out very quickly for all models. For example, the SAR value fell out of 42 dB with respect to the maximum value in the ear cartilage for the Duke model. As reported in Table 3, the SAR depth of penetration was lower than 1 mm for Ella, Billie, and Thelionious models and was equal to 1.77 mm for Duke. Interestingly, Duke had the highest value of SAR depth of penetration to indicate a deeper power absorption in the male adult head with respect to the child's head, although this was limited to the superficial tissue. In effect, at 1 mm depth, the SAR value was about 2% with respect to its peak value for Ella, Billie, and Thelionious models, and 32.33% for the Duke model. Figure 4 shows the local SAR values (dB) along with the profile of maximum SAR value for the whole head, from the right ear to the left ear. In Figure 5, the local SAR value (normalized to the maximum value at the surface of the skin) is shown in a zoom area relative to the first 2 mm depth from the skin surface, for each model.