esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the silent operation of the receiver station in a multistatic radar system, it is difficult for the jammer to generate the cooperative active deception target for the multistatic radar system. Making use of the spatial diversity property, a data level fusion method is proposed to counter the active deception jamming in this paper. According to the spatial correlation difference in physical target and active false target motion states, the deception velocity of the physical target, which is obtained by the radial velocity of each receiver, obeys the Gaussian distribution with zero mean, and the one of the active false target obeys the Gaussian distribution with the mean being its true deception velocity. Based on this fact, the active false target and physical target are discriminated by the deception velocity testing. The proposed deception velocity-based (DVB) method can keep a constant misjudgment probability for physical targets and discriminate active false targets effectively, especially in large deception velocity cases. The simulation verifies the feasibility and validity of the proposed discrimination method. Moreover, the proposed method can be combined with the location information association method to enhance the ability to discriminate the range-velocity joint deception of false targets.  
active deception jamming; multistatic radar system; data level fusion; target discrimination; deception velocity
## 5 SSFXG Architecture and Basic Interactions
### NW Service Provisioning
#### 5.2.2 NW Subscriber Identity Verification  
After the NW subscriber generates and transmits the verification parameters, the NW provider undertakes a crucial step in securely handling the subscriber's identity. The NW provider sends the concealed \(DID_{SUB}\)--a secure and obfuscated version of the subscriber's decentralized identifier--to the SC platform where the subscriber's SC is hosted (Step 4 Figure 3). This ensures that the interaction with the subscriber's identity is secure and follows decentralized principles.  
The SC platform is designed to actively monitor and listen for incoming NW requests related to its associated subscribers. Once the SC identifies a concealed \(DID_{SUB}\) within the NW request, it initiates the de-concealment process (Step 5 Figure 3). This involves securely revealing the original \(DID_{SUB}\) from its obfuscated form. This step is critical for authenticating the subscriber without exposing sensitive information during transmission or storage.  
Upon successfully de-concealing \(DID_{SUB}\), the SC securely responds to the NW provider. The response includes the extracted \(DID_{SUB}\), allowing the NW provider to validate the subscriber's identity further and proceed with service provisioning. This interaction ensures that only verified subscribers are granted access to the NW resources while maintaining strict confidentiality and security of the subscriber's identity.  
This process highlights the role of SCs in securely mediating interactions between subscribers and the NW provider. The federated wireless communication NW balances efficient service delivery and privacy protection for its participants by leveraging concealed identifiers and secure de-concealment mechanisms.
## 2 Materials and Methods
### Statistical Analysis  
Correlation coefficients between each parameter are displayed in the heatmap. In this heatmap, the correlation coefficients are plotted in the range of \(-1.0\) to \(1.0\), with warm colors showing a positive correlation and cold colors showing a negative correlation. Pearson's correlation coefficient test was performed and items with correlation coefficients greater than \(0.4\) or less than \(-0.4\) were considered correlated. For statistical examination, a chi-square test and paired \(t\)-test were performed on the distribution of electrophysiological severity, CTSI-SS scores, and CTSI-FS scores. For electrophysiological severity, logistic regression analysis was performed after grouping Stages 4 and 5 into the severe group and Stages 1-3 into the mild and moderate group. Multivariate analysis was performed for statistically significant variables to determine changes in CTSI-SS and CTSI-FS. Statistical analysis was performed using the Statsmodels library (ver. 0.14.1) of Python (ver. 3.10.12), and \(p<0.05\) was set as statistically significant.  
For machine learning classification model evaluation, the accuracy, recall (sensitivity), precision, and f-measure, as well as the specificity for each model, were calculated from the confusion matrix obtained from the test data. The characteristics of each evaluation method are as follows. Accuracy: the proportion of correctly predicted instances among all instances. Recall: the proportion of actual positives correctly identified by the model. Precision: the proportion of positive predictions that are truly positive. F-Measure: the harmonic mean of precision and recall. Specificity: the proportion of actual negatives correctly identified by the model. The AUC (Area Under the Curve) was calculated from the Receiver Operating Characteristic (ROC) curve. The regression models were evaluated using the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (R\({}^{2}\)). Machine learning model results were presented in the form of a mean (95% confidence interval). As a method of explainable AI (XAI), the important features in the model were visualized using feature importance and SHAP (Shapley Additive Explanation) scores. Feature importance is a concept that shows how important each feature is in improving the accuracy of the model. The SHAP score is a score that quantifies how much each feature has contributed to the prediction result of the model based on game theory.
## 4 Results
### Identification of the Threshold  
Figure 6a shows that the 25-m pixel-level yield prediction accuracy fluctuates between 0.35 and 0.75 using MODIS and \(L_{LM}\) data in time and space, where time-series Landsat observations are incomplete. The \(R^{2}\) derived from MODIS is steady (between 0.4 and 0.5) where the fraction of missing Landsat data ranged between 0.05 and 0.42, which is lower than the \(R^{2}\) (0.62-0.5) derived from \(L_{LM}\) data. The \(RMSE\) derived from both data sources remains approximately 0.9 t/ha for the same fraction of missing Landsat data (Figure 6b). When more incomplete time-series Landsat data are observed (from 0.42 to 0.75 on both Figure 6a,b), the \(R^{2}\) based on MODIS increases to around 0.7 and the \(RMSE\) decreases to 0.4 t/ha (as MODIS is not as cloud-affected as Landsat due to imagery being acquired on more days), whereas the \(R^{2}\) derived from the \(L_{LM}\) fluctuates between 0.6 and 0.4 and the \(RMSE\) changes between 1.3 and 0.8 t/ha (Figure 6). Given this, up to 42% of missing Landsat data in the growing season is defined as the threshold for when L-M blending is optimal to implement. That is, the 25-m pixel-level yield prediction accuracy can be improved using \(L_{LM}\) when the fraction of missing Landsat data at the coordinates is below this threshold. For instance, the \(L_{LM}\)-based model increases \(R^{2}\) by up to 20% when the fraction of the missing Landsat data is below 42% (Figure 6a).  
Figure 5: Validation of C-Crop-predicted yield pooled for wheat, barley, and canola using MODIS and L-M blended data when the complete Landsat time series are not available (the fraction of missing Landsat series \(\geq\)0.083). From top to bottom, the first (**a,b**), second (**c,d**), and third (**e,f**) rows show the comparison between observed (\(x\)-axis) and model-predicted yields (\(y\)-axis) on the field scale (\(n=210\)), 250-m pixel level (\(n=3978\)), and 25-m pixel level (\(n=231\),667), respectively, where \(n\) is the sample size. From left to right, the first (**a,c,e**) and second (**b,d,f**) columns delineate the validation using MODIS and L-M blended data, respectively. The solid black line is the line of best fit, the purple and the yellow lines represent the upper and lower bounds of the prediction confidence intervals (i.e., \(p=0.01\) and \(p=0.05\)), and the black dashed line is the 1:1 line.
## 4 Results
### Validation of Landslide Susceptibility Maps  
Validating the landslide susceptibility map (LSM) and determining its accuracy is an important aspect of landslide susceptibility research. The verification of landslide susceptibility maps is very important. Without it, the rigor of the study is greatly weakened, and the results of the study have no scientific significance [97; 98; 99]. In consequence, three commonly used statistical parameters, the receiver operating characteristics (ROC) curve [100; 101; 102] and the area under the curve (AUC) [103; 104; 105], standard error, and 95% confidence interval are introduced to verify the landslide susceptibility maps. It can be seen that the FR_SysFor model acquired the highest AUC value (0.940) of all hybrid models in the training data, followed by the IoE_SysFor model (0.926), IoE_LR model (0.783) and FR_LR model (0.779) (Figure 4). With the exception of the AUC value, the other statistical parameters in Tables 4 and 5 show similar results; the FR_SysFor model obtained the smallest SE (0.0132) and 95% CI (0.906, 0.965), followed by the IoE_SysFor, IoE_LR and FR_LR models. The validating data showed similar results to the training data, and the FR_SysFor model achieved the highest AUC value (0.831) and the smallest SE (0.0388) and 95% CI (0.753, 0.893).  
highly susceptible area with the prediction rate curve recognized by the FR_SysFor model also includes more than 70% of landslides, which illustrates that the precision of the FR_SysFor model is the highest and that the FR_SysFor model is the best model in this study.
## 2 Materials and Methods
### Determination of LOD\({}_{95\%}\)  
To determine the lowest copy number, still detectable with a 95% confidence interval (LOD\({}_{95\%}\)) in the duplex v-ddPCR method, two distinct serial dilutions of the target DNA from C. _jejuni_ and C. _coli_ were prepared at 6 low copy number levels (4, 2, 1, 0.4, 0.2 and 0.02 copies/\(\mu\)L). Sonicated salmon sperm DNA solution (10 ng/\(\mu\)L) (Thermo Fisher Scientific Inc.) was used for dilution of both species to maintain the stability of genomic DNA. Each dilution level was measured using a duplex v-ddPCR in 12 independent technical replicates. The probability of detection (POD curve) and LOD\({}_{95\%}\) was computed via a web service provided by QuoData (QuoData Web Service [38]) according to BVL guidelines [39].
## 5 Results and Discussion
### Experiment 1: Variation Across \(K\) (Constant \(L=3T_{0}f_{s}\))  
We ran each algorithm varying \(K\) (the frame size was kept at \(L=3T_{0}f_{s}\)) and recorded the resulting local and global SRER values. We started from \(K=2\) and increased \(K\) by two partials up to \(K_{\max}\). Figure 5 shows the local and global SRER (averaged across sounds) as a function of \(K\) for the SM, EDS, and eaQHM. Sounds with different \(f_{0}\) values have different \(K_{\max}\). Figure 5 shows that the addition of partials for the SM does not result in an increase in SRER after a certain \(K\). EDS tends to continuously increase the SRER with more partials that capture more spectral energy. Finally, eaQHM increases the SRER up to \(K_{\max}\).  
Figure 4: Plot of the signal-to-reconstruction-error ratio (SRER) as a function of number of adaptations to illustrate how adaptation increases the SRER in eaQHM. Iteration 0 corresponds to QHM initialized with the full-band harmonic spectral template.  
Figure 5: \(Cont.\)The SM, EDS, and eaQHM use different analysis and different synthesis methods, which partially explains the different behavior under variation of \(K\). More importantly, the addition of partials for each algorithm uses different criteria. Both the SM and EDS use spectral energy as a criterion, while eaQHM uses the frequencies of the sinusoids assuming quasi-harmonicity. In the SM, a new sinusoid is selected as the next spectral peak (increasing frequency) with spectral energy above a selected threshold regardless of the frequency of the peak. In fact, the frequency is estimated from the peak afterwards. For EDS, \(K\) determines the number of sinusoids used upon resynthesis. However, ESPRIT ranks the sinusoids by decreasing eigenvalue rather than the frequency, adding partials with high spectral energy that will increase the fit of the reconstruction. The frequencies of the new partials are not constrained by harmonicity. Finally, eaQHM uses the spectral template to search for nearby spectral peaks with LS and frequency correction. The sinusoids will converge to spectral peaks in the neighborhood of the harmonic template with \(K\) harmonically related partials starting from \(f_{0}\). Therefore, \(K_{\text{max}}\) in eaQHM corresponds to full-band analysis and synthesis but not necessarily for the SM or EDS.
## 4 Conclusions  
Previous research has referred to characteristics about the technology, user and staff resistance [18], difficulties when using the technology [19], or more general barriers for adoption [20] as obstacles for using VC. However, from our findings, we can say that the VC technology is ready for this kind of use. It is clearly easy to use and works without any problems, and the users seem satisfied. A positive effect of such consultations is the possibility of knowledge-sharing from more experienced specialists at the specialist hospital to the physicians at the referring hospital.  
Here, we argue for the importance of contradictions at the level of the _institutional_ activity system for the understanding of difficulties and low frequency use in medical cognition. By using CHAT as a framework for understanding low frequency use of VC as a tool for discussing individual stroke treatment, we illustrate how traditional ways of performing stroke treatment is a challenge for future development. In this article, we illustrate inner contradictions in a medical activity system. The treatment activity is shaped by institutional rules and division of labor, which affects the way the treatment activity is performed. To understand innovation in the future, it is important to understand the historic way of performing an activity, _i.e._, the organizing of health care and how knowledge is traditionally accessed in health care.  
The results show that VC for collaborating between the activity systems was used only four times. Since the equipment, the network, and the routines works fine, we can only believe that it will take more time to have VC fully integrated in stroke services within health care. However, as a discreet conclusion for future e-health; there is clearly a need for future internet to ensure a secure and reliable connection for VC services between health care institutions, as we believe it takes a long time for a large organization to adjust to new routines, technology, and services.  
We will continue our research within the area, which will make it possible to analyze what happens in the boundary zone between the two activity systems: the zone where the collaborative activity expands the stroke treatment and might create a new collective practice. Discussing medical problems may improve knowledge among health care professionals and prepare them for medical conditions that are constantly emerging. It may also prepare the health care system for technological changes in society in general, expanding from using traditional telephones to more modern forms of communication. As the patients use modern technology and access an increasing amount of medical information by using these technological tools, the expansion from collaboration by telephone to VC may prevent the health care system from falling behind.
## 4 Finite Element Implementations
### Solution of Plastic Strain by Euler Backward Method  
Given all constitutive variables at \(t=t_{n}\), the convergent solutions contain \(\sigma_{n},\boldsymbol{\alpha}_{n},\varepsilon_{n}^{p},\varepsilon_{n}^{bs},\)\(\xi_{n},\) and \(R_{n}\). Figure 12: Sketch of the Newton-Raphson (N-R) iteration. Figure 13: Sketch of the radial return method. The elastic tentative stress:  
\[\mathbf{\sigma}_{n+1}^{T}=\mathbf{\mathrm{D}}^{\varepsilon}:\varepsilon_{n+1}^{T}. \tag{29}\]  
The tentative strain:  
\[\varepsilon_{n+1}^{T}=\varepsilon_{n+1}-\varepsilon_{n}^{p}-\varepsilon_{n}^{bs }. \tag{30}\]  
Due to the elastic isotropy and plastic incompressibility, taking the deviatoric part of tentative stress \(\mathbf{\sigma}_{n+1}^{T}\)  
\[\mathbf{\mathrm{S}}_{n+1}^{T}=\sigma_{n+1}^{T}-\frac{1}{3}\textit{tr}\Big{(}\mathbf{ \sigma}_{n+1}^{T}\Big{)}\mathbf{\mathrm{I}}_{\mathrm{s}}. \tag{31}\]  
According to the radial return algorithm [38], the deviatoric stress on the yield surface corresponds to the tentative deviatoric stress  
\[\mathbf{\mathrm{S}}_{n+1}=S_{n+1}^{T}-2G[M_{2}]\Big{(}\Delta\varepsilon_{n+1}^{p} +\Delta\varepsilon_{n+1}^{bs}\Big{)}. \tag{32}\]  
Bain strain increment \(\Delta\varepsilon_{n+1}^{bs}\)  
\[\Delta\varepsilon_{n+1}^{bs}=\frac{1}{3}\Delta\upsilon\cdot\Delta\varepsilon_ {n+1}\mathbf{\mathrm{I}}_{\mathrm{s}}. \tag{33}\]  
Martensite content \(\Delta\varepsilon_{n+1}\)  
\[\Delta\varepsilon_{n+1}=\omega_{m}(\xi_{\infty}-\xi_{n})\Delta p_{n+1}. \tag{34}\]  
Isotropic hardening variable \(\Delta R_{n+1}\)  
\[\Delta R_{n+1}=\frac{2}{3}(1-\Delta\xi_{n+1})b(Q\Delta\xi_{n+1}-R_{n})\Delta p _{n+1}. \tag{35}\]  
Normal direction of the yield surface  
\[\mathbf{\mathrm{n}}_{n+1}=\frac{\mathbf{\mathrm{S}}_{n+1}-\mathbf{\mathrm{n}}_{n+1}}{\|\bm {\mathrm{S}}_{n+1}-\mathbf{\mathrm{n}}_{n+1}\|}=\sqrt{\frac{3}{2}}\frac{\mathbf{ \mathrm{S}}_{n+1}-\mathbf{\mathrm{n}}_{n+1}}{\sigma_{y(n+1)}}\Delta p_{n+1}. \tag{36}\]  
If the yield surface function is \(f_{n+1}=0\), the plastic strain increment can be calculated by the following formula  
Figure 13:0  
Plastic multiplier in the martensitic transformation model can be derived as follows:  
Figure 13:1  
Figure 13:2  
Figure 13:3  
Figure 13:4Figure 13:5  
The yield function is:  
Figure 13:6  
If \(f_{y}^{trial}\leq 0\), the material does not yield at this strain increment, \(\boldsymbol{\sigma}_{n+1}=\boldsymbol{\sigma}_{n+1}^{T}\). If \(f_{y}^{trial}>0\), the material yields and enters into the plastic state.
## 3 UAV Applications in Climate Change Research
### Urban Challenges and UAVs Opportunities  
As previously mentioned, climate change places a lot of pressure on the built environment with escalating impacts, ranging from climate hazards, such as floods, severe storms, and heatwaves, to infrastructure vulnerabilities. UAVs have emerged as a transformative technology to meet cities' sustainability challenges. With flexible deployment and data capture at neighborhood scales, UAVs enable cities to monitor their built environments and climate risks in real time, while helping diagnose and evaluate the solutions. The rich spatial and temporal perspectives offered by UAVs have become integral across diverse urban climate research domains, unlocking actionable intelligence needed by cities on the frontlines of climate disruption. UAVs are indispensable tools for developing resilient and sustainable cities. They provide critical data that enable informed decision making, efficient resource management, and proactive measures to address climate change challenges. As urbanization continues to occur, leveraging drone technology is essential in creating cities that are not only better prepared for climate-related threats, but also more sustainable and livable for their residents. Their capacity to furnish critical insights through drone-based climate change research is pivotal for enhancing urban planning, infrastructure design, and policy making. The precision and real-time monitoring capabilities of drones, equipped with advanced sensors, facilitate the acquisition of high-quality data on climate parameters, pollution levels, and environmental factors. This high-resolution data accessibility is critical to empower decision makers with the necessary information to navigate climate challenges. In a world marked by increasing urbanization and environmental challenges, UAVs emerge as a cornerstone for crafting cities that are both well-prepared for climate-induced threats and genuinely sustainable for their inhabitants.
## 3 Experiment
### Experiment Results
#### 3.3.2 Parameter Selection  
The impact of parameters \(k\) and \(\lambda\) of the proposed method on the recommendation performances was investigated to determine the optimal values. Figure 6 shows the recommendation performances of the proposed methods according to the iteration number (\(x\)-axis) in terms of RMSE (\(y\)-axis) for diverse \(k\)s ranging from 10 to 90 at intervals of 10. Each graph in Figure 6 consists of three lines that plot the performances of the proposed method with diverse \(\lambda\)s, 0.2, 0.02, and 0.002.  
Through the comparison of the nine graphs in Figure 6, it was observed that the RMSE tends to decrease as \(k\) increased. The RMSE decreased rapidly until \(k\) reached 30, and it became steady as \(k\) became larger than 80. In terms of \(\lambda\), overfitting was observed when \(\lambda\) was 0.02 and 0.002. The performance of the method stopped improving and started to degrade at a certain iteration number. Specifically, overfitting clearly occurred for \(\lambda=0.02\) and \(k>=30\) and for \(\lambda=0.002\) and \(k>=80\) while when \(\lambda=0.2\), no overfitting was evident for every \(k\). Thus, we set \(k\) and \(\lambda\) to 80 and 0.2, respectively, for the rest of the experiments.
## 4 Results and Discussion  
### Dataset Sources  
1. UIIS dataset: This is the first general underwater image instance segmentation dataset, released in 2023. This dataset includes seven challenging categories, such as fish, divers, reefs, and more. The UIIS dataset consists of 4628 images, divided into training, validation, and test sets with a ratio of 7.4:1.3:1.3. The images in the dataset vary in resolution, including \(240\times 320\) pixel images captured by low-resolution handheld cameras, and \(720\times 1280\) pixel images taken by medium- to high-resolution industrial equipment, ensuring diversity and high quality in the dataset. Among the images, fish constitute a significant proportion. For instance, there are 16,749 fish instances annotated in images. These annotations are valuable for fish detection and behavioral analysis in smart fish farming.
2. USIS10K dataset: This is the first large-scale underwater salient instance segmentation dataset, released in 2024. This dataset contains 10,632 images with pixel-level annotations from various underwater scenes and is divided into training, validation, and test sets with a ratio of 7:1.5:1.5. The dataset includes two types of annotations: single-class annotations, where all labels are marked as foreground, and detailed annotations, with seven sub-categories such as fish, ruins, aquatic plants, etc. In the USIS10K dataset, there are 9663 fish instances annotated. This detailed annotation provides high-quality training resources for tasks such as fish identification, disease detection, and water quality monitoring in smart fish farming. The diversity of fish annotations in the dataset supports both single-category and multi-category segmentation tasks, enhancing the model's adaptability in complex underwater environments.