esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Hypotheses Testing
#### 4.2.1 Effect of Question Sensitivity on Information Disclosure (H1)  
To test whether question sensitivity negatively influences users' information disclosure to chatbots (H1), we first extracted the response rate per question sensitivity. The average response rate for low-sensitivity questions was 98.4% (\(\pm\)13.5), while the response rate for high-sensitivity questions was 91.0% (\(\pm\)28.6). The results of the logistic regression showed that a question is less likely to be answered if it is highly sensitive compared to when it is low in sensitivity (estimate = \(-\)2.20, \(p\)-value \(<\) 0.0001). Thus, H1 is supported.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 1 Introduction
The network of the model is relatively simple, and the results can be improved by adjusting the network and loss functions. In this paper, we fully take into account the loss of information of source images during fusion, and we proposed an innovative DL-based fusion method, combining GAN and residual network (ResNet) to preserve both thermal radiation information of infrared images and texture information of visible images. We can flexibly adjust the amounts of residual blocks and skip connections of the generator model, simultaneously increasing the non-linear capability and depth of the network. In addition, the adversarial loss and content loss of the generator are all considered in our loss functions. All these mentioned guarantees the fused images of our method not only highly correlated with the source images, but also performed better in human perception matching [27]. To summarize, the main contributions of our research can be shown as follows:  
1. In this article, a novel method for infrared and visible image fusion which is based on GAN and ResNet has been proposed. This is an unsupervised image fusion method without pre-training on other labeled datasets. 2. The generator is designed with convolution layers, residual blocks, skip connections, batch-normalization layers, and activation functions; all these guarantee the fused images containing more information from source images. 3. The loss function of the generator consists of the adversarial loss and content loss. The structural similarity (SSIM) loss is added to the content loss which can help to guide the fusion optimization and improve the metrics in perceptual image quality assessment. The rest of this paper is organized as follows. In Section 2, some related work about image fusion and the networks of GAN and ResNet are introduced. Section 3 presents the fusion framework and loss function of our method in detail. Experimental settings, results, fusion evaluations, and discussions are provided in Section 4. Finally, Section 5 concludes the paper.
## 4 System Overview--HOS-Re
### Feature Extraction  
SignFi dataset is acquired in a scenario where background noises, signal interferences, and reflections exist in the presence of other Wi-Fi signals. Signal pre-processing techniques were  
Figure 4: HOS-Re methodology representing (**a**) Feature extraction; (**b**) Feature selection; (**c**) Classification.  
Figure 3: System overview.  
Third Order Cumulants  
HOS third order cumulant features are extracted from the raw CSI traces of the dataset with the assumptions that the statistical estimates are unbiased. Figure 5 shows the process of cumulant feature extraction. CSI data extraction from Wi-Fi signals needs a number of transmitting channels (\(N_{t}\)) and receiving channel (\(N_{t}\)). The receiving channel collects CSI measurements for the chosen sub carriers (\(N_{s}\)), with a specific number of CSI samples per gesture.  
The present work extracts 11 coefficients of the third order cumulant feature (\(F_{1}\) to \(F_{11},\cdots,F_{n-11}\) to \(F_{n}\)) from \(N_{t}\) channels having \(N_{s}\) subcarriers per channel. Thus, a total of \(F_{n}\) cumulant features (= 11 \(\times\)\(N_{t}\)\(\times\)\(N_{s}\)) were extracted. Finally, the \(n\)th record of the cumulant feature removes the sample mean and stores only the unbiased sample estimates of cumulants, as discussed in Section 3.2. HOS-Re uses higher order spectral analysis (HOSA) toolbox integrated with MATLAB [56] for extracting the third order cumulant features.
# Synthetic Text as Data: On Usefulness and Limitations
## 4 Experiments
### Evaluating GPT-Generated Text as a Data Augmentation Tool
Among various evaluation metrics, we report only the AUC, as it offers a threshold-independent and stable measure of classification performance--particularly suitable for comparing models in imbalanced data settings. Figure 3 compares the AUC scores of six top-performing models across four under-represented MBTI types (ESFP, ESTP, ESTJ, ESFJ). The blue bars represent performance using only the original Kaggle dataset, while the red bars reflect results from the Extended Dataset, which included GPT-augmented samples. Across most combinations of model and personality type, the inclusion of GPT-generated text led to performance improvements. For instance, models such as LightGBM and CatBoost showed notable gains for ESTP and  
Figure 2: The sample distribution for the Extended Dataset scenario. Solid bars represent training samples and hatched bars indicate test samples for each MBTI type. Additional training data generated by GPT are shown in red for the last four types (ESTP, ESFP, ESFJ, ESTJ). ESFP when augmented data were added. Although the magnitude of improvement varied by model, the overall trend remained consistent: synthetic data contributed positively to classification performance. In no case did performance significantly deteriorate due to augmentation, and in several cases, it helped close the gap caused by data scarcity. Taken together, the results suggest that although the degree of improvement varied across models and MBTI types, GPT-based augmentation consistently led to performance gains. While there were a few exceptions where augmentation did not yield clear improvements (as shown for XGBoost and some other models in the dashboard, possibly due to differences in how the model processes textual features or responds to synthetic inputs), the majority of cases showed that models trained on augmented datasets performed better than those trained on the original data alone. In fact, for every MBTI class examined, the highest AUC score was always achieved using the augmented dataset--a strong indication that synthetic data are not only helpful but at times critical in overcoming data scarcity. These results provide empirical support for our first research question--whether large language model outputs can be leveraged as an effective data augmentation strategy. Having established the utility of GPT-augmented data as a supplemental resource, we now turn to a deeper question: can synthetic text also function as a substitute for real-world data? In other words, if we had replaced the GPT-generated samples with actual human-written text, would the performance have improved even more? To address this, we designed a second experiment under a few-shot learning setup, which provided a more extreme and controlled scenario for testing such substitution. We describe this next.
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Temporal Normalization
#### 3.2.4 Simplified SZA Correction Model  
Given the instantaneous FAPAR measured at the time of satellite overpass FAPAR (\(\theta_{0}\)), the instantaneous FAPAR at any other time is FAPAR (\(\theta_{i}\)), then FAPAR (\(\theta_{i}\)) could be corrected to FAPAR (\(\theta_{0}\)) following  
\[\mathrm{FAPAR}\left(\theta_{0}\right)=\mathrm{k_{1}}-\left[\frac{\mathrm{k_{ 1}}-\mathrm{FAPAR}\left(\theta_{i}\right)}{\mathrm{k_{2}}}\right]^{\mathrm{ cos}\left(\theta_{i}\right)/\mathrm{cos}\left(\theta_{0}\right)}\times\mathrm{k_{2}} \tag{6}\]  
where the correction is related to \(k_{1}\) and \(k_{2}\), while the parameter \(k_{3}\) has been eliminated by comparing FAPAR measurements at two SZAs.  
To diminish the effects of random measurement errors, two or four instantaneous FAPAR values measured around 10:30 a.m. (\(\theta_{0}\)) symmetrically were selected and corrected, and the average corrected value was taken as the value of FAPAR (\(\theta_{0}\)).
# Bearing Capacity of Shallow Foundations on Unsaturated Silty Soils
## 4 Conclusions  
In this study, an equation was proposed to determine the UBC of shallow foundations on unsaturated silty soil layers. In an experimental model setup created for this purpose, the UBC values of different types and sizes of model footings on silty soil layers with a different SSD/MS and VR values were measured. In addition, the SWCCs and SS parameters of unsaturated silt were determined. The conclusions obtained as a result of these experimental studies and analyses can be listed as follows:  
1. Although the SS parameters of the unsaturated silty soil sample changes depending on the MS and the VR values, the failure behavior (punching failure) remains the same.
2. An increase in cohesion values in accordance with the literature was observed due to the increase in MS for all silt samples with different VRs. However, an increase was observed in the SS angle value due to the MS magnitude.
3. It was observed in the literature that the values calculated with the equations for the UBC of shallow foundations on unsaturated soils do not comply with the results of the model tests carried out on silty soils. Within the scope of this study, a new equation that gives suitable results for shallow foundations on unsaturated silty soils is proposed (Equation (8)).
4. It was determined that the foundation's UBC value for unsaturated silty soils can be estimated by using the unconfined compression test result and the fitting parameter depending on the SSD.
5. To evaluate foundations on unsaturated silty soils, the UBC can be estimated by considering the 0.1B method (ASSHTO method) by using the proposed equation in the current paper and by taking the fitting parameter (\(\Psi\)) as \(-2.89\). If the graphical method is taken into account, the estimation can be made by taking the fitting parameter as \(-1.33\) due to the generated relationship.
6. As the foundation size becomes smaller, the values of the foundation's UBC values measured in the model tests and calculated from the proposed equation become closer to each other. In addition, it was observed that the model foundation dimensions, and the variation in the foundation type causes changes to the measured UBC value.
7. In future studies, UBC tests can be performed on silt soils with different plasticity indexes and a relationship can be derived between the plasticity index and the fitting parameter (\(\Psi\)).  
Writing--original draft, M.U.Y.; conceptualization, A.O.; supervision, A.O.; investigation, M.U.Y.; methodology, M.U.Y. and A.O.; data curation, M.U.Y. and A.O.; writing--review and editing, A.O. and M.U.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We thank [PERSON], [PERSON], and [PERSON] (Sakarya University) for comments and suggestions.  
The authors declare no conflict of interest.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 8 Performance Evaluation of the Proposed FL-IDS  
The simulation scenario used toi evaluate the proposed FL-IDS's performance is composed of five smart buildings collaborating to develop a multi-layer ML-IDS. The system is composed of five clients, which are coordinated by an OSS. This component can initiate the FT process, which is designed to enhance the overall model quality. Additionally, the OSS retains a limited set of validation data, which is employed to assess the efficacy of the FT. Moreover, the OSS maintains a record of the current state of the global models.  
Each client is linked exclusively to the OSS and possesses its own data pre-processing and engineering system, operating within its designated cyber space. The client utilizes the central model provided by the OSS to perform inference on their proprietary data. It stores the inferred data to be later used in FT and participates in the FT.  
The considered scenario can be classified as a \"cross-silo\" and \"horizontal\" federated setting. In this setting, clients are distinct organizations that share a common feature space by leveraging a unified data pre-processing and engineering system, yet possess disparate data samples. Figure 13 provides an overview of the scenario under consideration.  
Figure 13: Overview of the simulation scenario.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**CNN** & **Accuracy** & **Precision** & **Recall** & **F1** & **FPR** & **FNR** \\ \hline Network & 99.23 & 99.28 & 99.84 & 99.56 & 4.76 & 0.16 \\ \hline GPS & 100 & 100 & 100 & 100 & 0 & 0 \\ \hline Fridge & 93.25 & 90.95 & 100 & 95.26 & 20.94 & 0 \\ \hline Garage door & 100 & 100 & 100 & 100 & 0 & 0 \\ \hline Weather & 93.98 & 93.60 & 100 & 96.69 & 50.0 & 0 \\ \hline Thermostat & 91.23 & 100 & 83.17 & 90.81 & 0 & 16.83 \\ \hline Motion Light & 100 & 100 & 100 & 100 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 8: CNN performance metrics (%).
## 2 Materials and Methods
### WRPM: Weather-Robust Preprocessing Module
#### 2.4.2 LS-UNet  
For regions with light haze in the image, we designed an attention-driven dehazing network based on UNet [41]. Building upon the classic UNet encoder-decoder structure, we innovatively integrated the channel attention mechanism (\(CA_{\text{ffa}}\)) and the pixel attention mechanism (\(PA_{\text{ffa}}\)), forming an attention-enhanced deep architecture. Figure 4 illustrates the processing flow of input features within this module.  
During the downsampling phase of LS-UNet, low-dimensional features are extracted through consecutive convolutional operations, gradually reducing the spatial dimensions. In the middle part of LS-UNet (i.e., the bottleneck layer), we enhanced its design by introducing a dual-attention architecture that combines the channel attention mechanism and the pixel attention mechanism. The channel attention mechanism dynamically adjusts the weights of feature channels, focusing on more important feature channels to better preserve key information in light haze regions. The pixel attention mechanism enhances the ability to capture detailed features through local feature aggregation, providing richer feature representations for the subsequent decoding phase. During the upsampling phase, deconvolution operations are combined with features from corresponding layers to help restore image details and edges. This design enables the network to excel in detail recovery and edge preservation in light haze regions.  
Notably, the introduction of the attention mechanism allows the network to adaptively aggregate features and selectively focus on multiscale features, emphasizing critical aspects such as image structure and texture information while suppressing irrelevant noise. This results in finer image restoration in light haze scenarios, providing clearer image input boundaries for subsequent UGSAM urban vegetation segmentation and effectively improving segmentation accuracy.  
Figure 4: Detailed structure diagram of LS-UNet. LS-UNet is an encoder–decoder structure. It implements downsampling inside the encoder to extract low-dimensional features from the image through consecutive convolution operations, and implements upsampling inside the decoder to enhance the ability to restore image details.
## 3 The Proposed Method
### Computational Complexity Analysis  
The computational complexity of four existing methods is compared to show that the proposed method has low computational complexity. \(N_{r}\) and \(N_{a}\) represent the number of range cells and the number of azimuth pulses, respectively. The searching times of \(a_{1}\), \(a_{2}\) and \(a_{3}\) are the same, denoted by M, which is usually be larger than \(N_{r}\) and \(N_{a}\)[8; 10; 25].  
The GRFT method [29] is considered a statistically optimal method. Its implementation mainly includes the 3D grid searching of target motion parameters [14; 25; 29], whose computational complexity is about \(O\left(N_{r}N_{a}M^{3}\right)\). The 2D frequency matched filtering method [30] includes the searching of the second-order motion parameter with the computational complexity being about \(O\left(M\left(N_{r}N_{a}\log_{2}^{N_{r}}+N_{r}N_{a}\log_{2}^{N_{a}}\right)\right)\). The Hough-GHAF [25] algorithm mainly includes a SOKT, a Hough transform to detect the line in range-time and azimuth-time domains, and a 1D search of \(a_{2}\), whose total computational complexity is \(O\left(N_{r}N_{a}^{2}+M(M-1)N\right)+O\left(N_{r}N_{a}^{2}\right)+O\left(N_{a }^{3}\right)+O\left(MN_{a}\log_{2}^{N_{a}}\right)\)[8]. Concerning the KDCT-FSFT method [10], its major implementation procedures include the pre-filtering, KDCT for the RCMC, FSFT for the third-order Doppler parameter estimation and 2D-FFT, whose total computation is \(O\left((N_{a}+M+2)N_{r}N_{a}+(M+1)N_{r}N_{a}\log_{2}^{N_{a}}+N_{r}N_{a}\log_{ 2}^{N_{r}}\right)\).  
The major implementation procedures of the proposed method include Hough transform with computational complexity of \(O\left(N_{r}M^{2}\right)\), the pre-filtering with computational complexity of \(O(N_{r}N_{a})\), the SOKT for the range curvature correction with computational complexity [14] of \(O\left((N_{a}+1)N_{r}N_{a}+N_{r}N_{a}\log_{2}^{N_{r}}\right)\), SAC for the third-order Doppler parameter estimation with computational complexity of \(O\left(N_{a}+N_{a}\log_{2}^{N_{a}}\right)\) and 2D-FFT with computational complexity of \(O\left(N_{r}N_{a}\log_{2}^{N_{r}}+N_{r}N_{a}\log_{2}^{N_{a}}\right)\). So, its total computational cost is  
\[O\left(N_{r}M^{2}+(N_{a}+2)N_{r}N_{a}+N_{a}+N_{a}\log_{2}^{N_{a}}+N_{r}N_{a} \log_{2}^{N_{r}}+N_{r}N_{a}\log_{2}^{N_{a}}\right) \tag{38}\]  
Let \(M=N_{r}=N_{a}=N\); the computational complexity of the five methods is listed in Table 1. It can be seen from Table 1 that the proposed method has the lowest computational complexity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Computational Complexity** \\ \hline GRFT method [29] & \(O\left(N^{5}\right)\) \\
2-D frequency matched filtering [30] & \(O\left(N^{3}\log_{2}^{N}\right)\) \\ Hough-GHAF method [25] & \(O\left(4N^{3}+N^{2}\log_{2}^{N}\right)\) \\ KDCT-FSFT method [10] & \(O\left(2N^{3}+N^{3}\log_{2}^{N}\right)\) \\ Proposed method & \(O\left(2N^{3}+N^{2}\log_{2}^{N}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of computational complexity.
# Mapping with Pleiades--End-to-End Workflow
## 3 Test Sites and Data Sets
### Test Site Tian Shui (China)  
The study area is located in central China close to the city of Tian Shui. Due to its location, it shows a geomorphological position in the landscape since it belongs to the Tibetan Plateau and also to the southern edge of Long-Zhong loess hilly area [110]. The ellipsoidal heights are ranging from 1200 m to 2100 m and the scene covers 395 km\({}^{2}\). Since for this site no reference data is available it more or less serves for visual interpretation purposes only. To allow accurate relative orientation tie-points are manually measured and applied within the sensor model adjustment. The test site is shown in Figure 7.
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 4 Some Integrated Solutions for Short-Reach Transmission Systems
### Transceiver Integration Scheme Based on Coherent Detection  
There is another alternative transceiver integration solution based on coherent detection [27], which had been designed as depicted in Figure 14. The PIC integrated two full coherent transmitter channels including two tunable lasers, four SOA amplifiers, two PDM-IQ modulators, four Mach-Zehnder modulator drivers, two full coherent receiver channels containing two tunable lasers, two 90\({}^{\circ}\) hybrids, eight BPDs, and four TIAs in a single InP-based chip. It is noticed that the transmitter and receiver on the PIC integrated two lasers respectively. The main purpose was to be able to tune the wavelength and output power of the laser on the transmitter and receiver independently, as such it was more suitable for a reconfigurable optical network rather than a shared laser solution between transmitter and receiver. In addition, the two channel transceivers integration scheme had been further demonstrated in a two channels 800-Gbit/s multi-subcarrier transmission system [27]. As shown in Figure 15a, the transmitter part driven by a 100-GSa/s digital to analog converter (DAC) generated a 800-Gbit/s/ wavelength 8 subcarriers (SC), and each subcarrier supported 100-Gbit/s probabilistic shaping (PS) -64-QAM signal so that a total capacity of 800-Gbit/s per wavelength can be obtained. The optical spectra of transmitter in Figure 15b showed a single -95-GHz wide channel with eight digital subcarriers, where SC1 represented the first subcarrier. Following the optical amplifier, the output optical signal was coupled into the receiver part of the transceiver to realize coherent detection operation, and the detected signal after TIA was connected to a set of real-time scopes operating at 200-GSa/s. Then, the channel impairments were equalized and compensated by offline DSP module. Overall \(Q_{2}\) factor of the received signal was measured to be about 7.5-dB under back-to-back conditions, which indirectly proved that there was enough margin for fiber transmission. All subcarrier PS-64 QAM constellations after impairments compensation were inserted in Figure 15c.  
Figure 14: Schematic of transceiver integration scheme based on coherent detection; MZMD: Mach Zehnder modulator driver; TE: transverse-electric; TM: transverse-magnetic; TIA: transimpedance amplifier; RF: radio-frequency; DC: direct current; InP: Indium phosphide; PIC: photonic integrated circuit; SOA: semiconductor optical amplifier; Ch_1: the first channel; (From [27] © 2021 IEEE).
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
###### Abstract  
Various information systems adopt different information category standards and description methods even for the same battlefield space. This leads to the heterogeneity of information distributed in different systems, which hinders the information sharing among different systems. In this paper, we adopt the idea of schema mapping and design a framework of realizing heterogeneous information sharing based on content. We design a concept-logic tree model to organize the battlefield situation information and realize the mapping from local concept models in different systems to a global unified concept model. By constructing a unified information space, we realize the centralized organization, storage, and management of entity description information. Then, the information broadcasting mechanism and content based information query mechanism are designed to realize information sharing. Theoretical analysis and experiment results verify the effectiveness of the proposed framework.  
content; heterogeneous; information; semantics; sharing Article