esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The spatial resolution of a magnetic anomaly model based on satellite magnetic data is mainly influenced by two factors. First, small-scale magnetic anomaly signals, which decay with the altitude for spaceborne sensor devices, are difficult to capture [19]. Second, the sensors are strongly influenced by the external magnetic field at satellite altitude, and the magnitude even exceeds the intensity of the magnetic field signal itself. Therefore, the interference of the external field should be excluded from the modeling process of magnetic anomalies. Two common modeling methods are available [20], as follows: comprehensive inversion (CI) and sequential modeling. CI utilizes comprehensive mathematical models to describe the sources only in time and space on the average and on the inversion of all types of field sources using iteratively reweighted least-squares [21; 22; 23]. However, the time-varying characteristic of the external field is evident in the repeat period of a satellite orbit. The time-varying signal may overlap with the weak small-scale crustal magnetic anomaly signal. Thus, the time-varying signal may be difficult to distinguish. The sequential approach selects the observed data with less external field interference and revises the selected data using an established empirical model. Furthermore, along-track filter processing is conducted for the revised data. Finally, inversion modeling is conducted for the refined data [24]. However, this method may induce spectral leakage in the process of correcting the external field. Regional modeling exhibits better fitting effect on the fine structures of geomagnetic anomalies than spherical harmonic analysis when the spatial distribution of regional magnetic anomalies is studied [8]. The advantages of regional modeling are evident when satellite observation data are densely distributed across layers of different heights or are unevenly distributed in a horizontal direction. Common regional modeling methods mainly include the equivalent source method [25], rectangular harmonic analysis [26; 27], the spherical cap harmonic/revised spherical cap harmonic analysis (R-SCHA) method [28; 29; 30; 31], and the 3D Taylor/Legendre polynomial method [32; 33; 34]. These methods have various advantages and disadvantages. Using CHAMP and Swarm satellite magnetic data, the current study utilizes Sq local time conditions, Dst/Kp geomagnetic index conditions, Hamming along-track high-pass filtering, and the hierarchical gridding method, and combines EMM2015, MF7 and other auxiliary models to thoroughly process satellite magnetic data. The present paper adopts zonal revised spherical cap harmonic analysis to inverse the lithospheric magnetic field over the Tibetan Plateau and analyzes the relationship between magnetic anomalies and geological structures to explore the active region of geological structures.
## 5 Experimental Evaluation  
To validate the effectiveness and robustness of the proposed TAIM in realistic federated learning environments, this section conducts a comprehensive empirical study across multiple representative datasets, attack types, and baseline methods. The evaluation focuses on the following research questions: (1) Can TAIM improve global model accuracy and convergence efficiency under heterogeneous client participation? (2) Is TAIM more robust against various attack types and capable of identifying adversarial behaviors? (3) Is the overhead of TAIM acceptable under realistic constraints such as limited resources and client uncertainty?  
To ensure reproducibility, we describe the experimental setup, attack modeling, evaluation metrics, baseline methods, and result analysis in the following sections.
# Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks
## System Model  
### Path Loss Model  
In urban dense environment, the propagated radio signals are subject to many electromagnetic phenomena such as, reflection, refraction, scattering and diffraction. Hence the received signal will be guided through multiple paths, and this will create the multi-paths fading subject to destructive or constructive interference. In large scale fading cases, obstacles trigger shadowing effects and create multi-paths and in small scale fading interference will occur and create multi-paths as well.  
The behavior of the Radio Signal Strength (RSS) in time and in space, is two folds: deterministic and stochastic process. The degradation of power signal strength is defined as Path Loss \(PL\). Many factors could be used for the estimation of \(PL\), such as multi-paths phenomenon, distance from transmitter, penetration losses through walls, etc. The RSS expression is the combination of deterministic effects and stochastic effects using Gaussian model as  
\[PL_{d}=\underbrace{PL_{d_{0}}+10n\log 10(\frac{d}{d_{0}})}_{\text{ Deterministic effect}}+\underbrace{\chi_{\sigma}}_{\text{Stochastic effect}}[\text{dBm}] \tag{1}\]  
where \(d\) represents the \(l^{2}\)-norm distance between the transmitter at \((x_{t},y_{t})\) and the receiver \((x_{r},y_{r})\). \(PL_{d_{0}}\) is the loss at a reference distance \(d_{0}\). The constant \(n\) is generally greater than 2 and specific to propagation environment. \(\chi_{\sigma}\) is the main stochastic behavior of \(PL\) and it could be modeled as zero mean Gaussian random variable with variance \(\sigma^{2}\). However, when the channel is time variant the wide-sense stationary process assumption is lost and the shadowing or the multi-paths fading effects will dominate \(\chi_{\sigma}\)[24].  
The RSS could be modeled as described by the equation below:  
\[\text{RSS}(d,t)=\text{RSS}_{d_{0}}-\underbrace{10n\log 10(\frac{d}{d_{0}})}_{ \text{Path loss}}-\underbrace{\Phi_{\text{dBm}}(d)}_{\text{Shadowing}}- \underbrace{\Xi_{\text{dBm}}(d,t)}_{\text{Multipath}} \tag{2}\]  
From (2) the path loss is considered as large scale fading (very slow process) and the remaining part is considered as small scale fading (fast process) due to multi-paths and shadowing. The main part responsible for shadowing effects is \(\Phi_{\text{dBm}}=\log 10(\Phi_{\text{mW}})\) and \(\Phi\sim N(0,\sigma_{\Phi}^{2})\) is a zero mean Gaussian distribution with variance \(\sigma_{\Phi}^{2}\). Next to shadowing, multi-paths fading could be also modeled in function of space and time as \(\Xi_{\text{dBm}}(d,t)=10\log 10(\Xi_{\text{mW}})\) and \(\Xi_{\text{dBm}}(d,t)\sim N(0,\sigma_{\Xi}^{2})\). In many commercial wireless devices the path loss (RSS) can be recoded using the Received Signal Strength Indicator (RSSI) metric. For the rest of this paper, the RSS will be used as RSSI recoded values from wireless devices.
# Guidelines towards Information-Driven Mobility Management
## 4 Mobility in ICN
### Mobility Management in Different ICN Solutions  
While ICN approaches have in common the advantages described in the previous section, different approaches tackle mobility management in different ways. To assist in a better understanding of the current situation, Table 2 describes whether/how producer mobility, consumer mobility, and multi-homing are supported by reference approaches.  
As described in Table 2, none of the main ICN architectures provides seamless producer mobility support. As for consumer mobility, existing approaches either take care of such support based on anchor-based approaches, following IP-based learning, or via anchor-less approaches. Furthermore, multi-homing is supported only in JUNO, CCNx and NDN.  
These aspects are further debated on the next sections. The description provided is focused on the line of work derived from CCNx, including NDN.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Approach** & **Mobility Management Description** & **Consumer Mobility** & **Producer** & **Multi-Homing** \\  & & & **Mobility** & \\ \hline DONA [19] & Anchor-based, early-binding & Supported, but not & No & No \\  & approach, producers register identifier & intrinsic & & \\  & to locator mapping that must be & & & \\  & resolved before data can be sent. & & & \\  & Intends to be interoperable with DNS. & & & \\ \hline CCNx [20] & Anchor-less, late binding approach, & Intrinsic. When a & No & Yes \\  & as data is only sent after an Interest & consumer moves, Interest & & \\  & packet is received. There is no direct & packets are again sent. & & \\  & identifier–locator mapping CCNx & & & \\  & can handle 97\% of requests during & & & \\  & high mobility. & & & \\ \hline NetInf [21] & Anchor-based, early-binding, similar & Supported but not & No & No \\  & to DONA, even though it requires & intrinsic. & & \\  & consumer lookups & & & \\ \hline PSIRP [22] & Anchor-based, late-binding, requires & Intrinsic. When a & No & No \\  & consumer re-registration after & consumer moves, Interest & & \\  & moving. & packets are again sent. & & \\ \hline JUNO [23] & Middleware takes care of & Supported but not intrinsic. & No & Yes \\  & information-centric functionality. & Middleware takes care of & & \\  & Relies on a DHT approach, where flat & the mobility. & & \\  & identifiers for content are registered. & & & \\ \hline NDN [24] & Similar to CCNx. & Intrinsic. When a & No & Yes \\  & & consumer moves, Interest & & \\  & packets are again sent. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ICN main approaches, mobility challenges.
## 2 Description of the Classification Method
### Preprocessing  
Although the practically captured data from the environment may have a variable duration, underwater acoustic processing methods based on CNN or DNN or ResNet networks generally need the length of the input sample to be fixed. Therefore, preprocessing is a prerequisite before feeding the signals of different lengths to the designed networks. One feasible way is to split the underwater acoustic signal into several frames of fixed length using a sliding designed window of appropriate width. Considering that an underwater acoustic signal can be seemed as stable in a very short time, the framing of the recorded signal could be performed. This naturally increases the number of samples as some parts of the underwater acoustic signal are reused and that can be viewed as some sort of data augmentation [19]. For simplicity, our framing method may do not take overlapping and no window is used in it. Note that each preprocessed signal must be labeled as the raw label. Besides, for underwater acoustic, a sampling rate of 20,480 Hz may be considered a good trade-off between the quality of the input sample and the computational cost of the model. We present the demonstration of the framing of the signal of the type of passengers into several framess and s + 1 with no overlapping in Figure 2.
## 3 Methods  
### Burn-Type Differentiation  
#### 3.1.1 Agricultural Burning Identification  
Agricultural burning includes planting preparation burning, crop residue burning, and stubble burning. Fuel load and emission factors vary for different crop residues [37]. Agricultural burning has different emission patterns than prescribed burning due to the differences in fuel type, fuel load, timing, and frequency, so it is important to differentiate agricultural burns from prescribed burns. Georgia does not provide detailed burning purposes in its permit records, and Florida does not have burn type data for 2017 and 2020. Satellite-derived FINN data does not differentiate burn types. To identify agricultural burns, we utilize the National Land Cover Database (NLCD), which has a 30 m resolution and is updated every 2 to 3 years [38]. We use 2013, 2016, and 2019 NLCD data to provide the land cover type of fires for their following years (e.g., 2013 for 2013-2015). For each fire, we assume a square shape with the same area as the burned area. The dominant land cover type in the square is assumed to be the land cover type for the fire. For fires that happened in areas classified as agriculture, we use agricultural burn estimation. Fires in open water or barren land in FINN or permits are removed as they are likely the wrong coordinates in FINN or permit records.
## 4 Analysis of Sound Radiation Characteristics for Reinforced Laminated Cylindrical Shell Structures  
### Finite Element Model  
Building upon the previously validated method for the finite element model of reinforced laminated plates, this section incorporates the reinforced laminated plate structure as a part of the shell structure and establishes a finite element model for the reinforced laminated composite cylindrical shell.  
The finite element model of the reinforced laminated cylindrical shell is shown in Figure 10, and the overall structure consists of a laminated cylindrical shell, reinforcement ribs, and upper and lower cover plates. The total length of the cylindrical shell is 1200 mm, and it is sealed by wooden cover plates with inner and outer shell thicknesses of 2 mm and 3 mm, respectively. The inner shell has a radius of 500 mm  
The finite element model is imported into the acoustic simulation software for preprocessing. Node quality is checked, and relevant parameters from Table 1 are assigned to the structure and acoustic cavity. The cylindrical shell and reinforcement are constructed from steel material, while the upper and lower cover plates are made of wood with material density of 800 kg/m\({}^{3}\), elastic modulus of 2 GPa, and Poisson's ratio of 0.4. The acoustic medium is air. In typical engineering practice, the imaginary part of the speed of sound is usually no greater than 0.5% of the real part. Therefore, in this study, the real part of the speed of sound is set to 340 m/s, and the imaginary part is set to 0.5 m/s [13].  
Two end-restraints are established at the interface between the upper and lower cover plates and the inner and outer shells to constrain the radial displacement between them, and an AML layer is set at the outermost layer of the acoustic envelope. A unit force excitation is applied at the center of the inner shell of the reinforced laminated cylindrical shell, directed radially outward. An ISO standard spherical sound field is established outside the cylindrical shell, and the acoustic computational model is shown in Figure 11. There are a total of 38 computational nodes on the spherical sound field, and the distance of each node from the cylindrical shell is greater than 500 mm. The sound pressure at all nodes on the field is superimposed and averaged to characterize the structural acoustic radiation response. A direct acoustic-structural coupling analysis is conducted, with the solving frequency range set to 1-1000 Hz and solving step size set to 1 Hz. Finally, the solving computations are performed to investigate the impact of cylindrical shell structure's radiation noise.  
Figure 11: Acoustic finite element computational modeling.  
Figure 10: Finite element model of a laminated reinforced cylindrical shell.
## 5 Conclusions
From the response graph (Q) it could be seen that the laser power had a more pronounced effect in reducing the Kerf size compared to the pressure. To obtain a small Kerf it is necessary to select an average value of the gas pressure observed using the regression model (Q). In the case of the calculated linear model (L) it is observed that a low Kerf is obtained at the low value of the laser power and the increased gas pressure. These models can be used successfully to anticipate Kerf at the right profile in the case of CO\({}_{2}\) laser cutting of the H400 sheet. The RSM model can be used to predict Kerf under the influence of other cutting parameters to optimize Kerf at the right profile. A minimum straight profile Kerf of 0.33 mm is obtained when cutting parameters such as gas pressure and laser power are maintained at 0.55 bar and 4900 W, as indicated by model (L). A minimum kerf of 0.30 mm can be obtained when we maintain the laser power at 5000 W and the cutting gas pressure at 0.50 bar. As a result, we propose to use the combination of values: laser power \(p\) = 5000 W, auxiliary gas pressure \(p\) = 0.50 bar at constant cutting speed v = 1900 mm/min to obtain a fine cut. **Author Contributions:** Conceptualization, C.C.G. ; methodology, C.C.G., C.G. and C.R. ; software, C.R. and C.C.G. ; validation, C.G., C.R. and C.C.G. ; formal analysis, D.C.; investigation, C.C.G. ; resources, C.R. ; data curation, C.R. ; writing--original draft preparation, C.C.G., C.R. and C.G. ; writing--review and editing, C.C.G. and C.G. ; visualization, D.C. and C.R. ; supervision, C.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** The experimental research used in this study was conducted with the support of SC BYSTRONIC Brasov, the authors thank for the technical support provided in conducting this experiment with CO\({}_{2}\) laser. Figure 3a,b was made at the Oltenia Energy Complex, Measurement Laboratory of the Rovinari Thermal Power Plant. **Conflicts of Interest:** The authors declare no conflict of interest.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 5 Discussion  
We also found some problems in our research, briefly explained here. Due to the small test set of our dataset, if some image segmentation results are not good, it may cause relatively large changes in the MIoU value. Whether they are real data or realistic synthetic data, we must pay attention to the problem of noise in the network design because, in real tasks, noise is unavoidable. Our Spacecraft A is more regular in geometric structure and has salience in the image. The segmentation results of Spacecraft A are significantly improved when the effect of noise in the image is addressed. We show some failed cases in Figure 13 below. The second row in the figure is the segmentation result of the baseline method. The third row is the segmentation result of our method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Spacecraft A** & **Spacecraft B** & **Spacecraft C** & **MIoU** \\ \hline Baseline & 83.15 & 81.26 & 88.43 & 84.28 \\ Baseline + A & 84.87 & 82.74 & 88.11 & 85.24 \\ Baseline + B & 78.56 & 86.64 & 91.46 & 85.55 \\ Baseline + D & 91.87 & 83.83 & 90.61 & 88.77 \\ Baseline + A + B & 84.6 & 86.07 & 88.96 & 86.54 \\ Baseline + A + D & 93.25 & 84.15 & 91.5 & 89.63 \\ Baseline + B + D & 93.28 & 87.2 & 92.56 & 91.01 \\ \hline Baseline + A + B + D & 93.11 & 87.62 & 92.87 & 91.20 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The MIoU results of the ablation study on the test set.  
Figure 13: Failure cases.  
The segmentation errors in the first column are caused by the similar structures of different types of spacecraft. As shown in Figure 14 below, the Spacecraft A and Spacecraft C objects have similar solar panel structures, and, in some perspectives of Spacecraft A, only the solar panels are more conspicuous. Similar structures are prone to the misclassification of pixels. There is also some degree of pixel clutter in the second column. It is related to the texture features similar to the spacecraft. Our network enhances the contextual information to alleviate such phenomena. The third and fourth columns of images are poor lighting conditions that make some structures invisible and make it easier for pixels to be misclassified as other spacecraft or space backgrounds. The segmentation results of DeepLabv3+ will have large cavities or misclassification. Our method can better segment a simply connected domain, but there are still cavity cases, and the segmentation effect can continue to improve in the future.
## 3 Results
### Out-Of-Domain Data Detection  
The bar chart, showing the distribution of ROC-AUCs of the OOD data detection method discussed above, is presented in Figures 10 and 11. Unfortunately, the results of the [PERSON] signed rank test state the absence of statistically significant differences between the ensemble and BNN under isotropic prior constraint (T = 15.0, \(p\) = 0.67) for EEGNet architecture. The same picture is for different prior constraints (IG vs. IG + t: T = 17.0, \(p\) = 0.88, IG vs. CG + t: T = 18.0, \(p\) = 0.65). Similar conclusions can be made for the Shallow ConvNet architecture (IG vs. Ensemble: T = 12.5, \(p\) = 0.25, IG vs. IG + t: T = 11.0, \(p\) = 0.33, IG vs. CG + t: T = 7.5, \(p\) = 0.07).  
Figure 10: ROC-AUC of OOD detection method, for all participants, using logit vector norm variance as data for different prior constraints compared to the ensemble for EEGNet architecture. Ens stands for the same plot.  
Figure 9: The 4-classes accuracy distribution averaged across all participants. Ens stands for ‘En-semble of 120 deterministic models’. The error bar represents the 95% confidence interval. The cyan dashed line represents the 0.25 threshold of random guessing.  
for 'Ensemble of 120 deterministic models', IG stands for Isotropic Gaussian prior for all layers, IG + t-Stud stands for IG for all layers and t-Student prior for the classification layer, CG + t-Stud stands for Correlated Gaussian prior for temporal and spatial convolution layers, t-Student prior for the classification layer and IG for all other layers. The cyan dashed line represents the ROC-AUC of a random classifier, which is equal to 0.5.  
However, the average (Figures 12 and 13) results are unsatisfying, though the average ROC-AUC of BNNs is slightly higher than that of the ensemble.  
Figure 11: ROC-AUC of OOD detection method, for all participants, using logit vector norm variance as data for different prior constraints compared to the ensemble for Shallow ConvNet architecture. The cyan dashed line represents the ROC-AUC of a random classifier, which is equal to 0.5. See Figure 9 caption for other details.  
Figure 12: ROC-AUC of OOD detection method, averaged across all participants, using logit vector norm variance as data for different prior constraints compared to the ensemble for EEGNet architecture. The error bar presents the 95% confidence interval. The cyan dashed line represents the ROC-AUC of a random classifier, which is equal to 0.5. See Figure 9 caption for other details.