esa-sceva/satcom-chunk-collection huggingface dataset:
# FREDY: Federated Resilience Enhanced with Differential Privacy
## 3 Proposed Framework  
### Design Overview  
As discussed above, FL-PATE addresses the context of federated learning by employing a client-level differential privacy mechanism for teacher model training. In contrast, PATE introduces a vote-level differential privacy mechanism. That said, we introduce FREDY, a novel federated learning framework rooted in the PATE methodology for knowledge transfer, while also incorporating differential privacy measures. Analogous to PATE's configuration [29], FREDY employs a teachers-student scheme. The teachers undergo training within a federated learning scenario, and, upon the completion of the FL process, each teacher's model performs inference on the student public data. These predictions from public data are amalgamated and subjected to noise addition to facilitate the training of the student model. A schematic representation of FREDY's design is presented in Figure 2. Our objective is to cultivate a privacy-preserving model that can tackle intricate real-world tasks. Each teacher's model is trained on its private, sensitive data. The outcome of this comprehensive procedure is the student model, slated for later public release. The detailed steps of our proposed framework are described in Algorithm 1.  
Figure 2: Proposed FREDY architecture.  
```
1:\(\text{\it PublicData}\)
2:procedureServerExecution(\(n,T\))
3:\(\text{\it Initialize global model weights }W_{0}^{\text{\it global}}\)
4:for\(round\;r\gets 1,2,\ldots,n\)do
5:if\(r=n\)then// last round
6:\(W_{n}^{T}\leftarrow\text{\it TrainTeachers}(r,T,W_{n-1}^{\text{\it global}})\)
7:\(\text{\it Labels}\leftarrow\text{\it NoisyAgg}(\text{\it PublicData})\)
8:\(W_{st}\leftarrow\text{\it TrainStudent}(\text{\it Labels},W_{0}^{\text{\it global }})\)
9:return\(W_{st}\)
10:else
11:\(W_{r}^{T}\leftarrow\text{\it TrainTeachers}(r,T,W_{r-1}^{\text{\it global}})\)
12:\(W_{r}^{\text{\it global}}\leftarrow\frac{1}{T}\sum_{t=1}^{T}W_{r}^{T_{t}}\)
13:endif
14:endfor
15:endprocedureTrainTeachers(\(r,T,W_{r}^{\text{\it global}}\))
16:for\(\text{\it teacher }T_{i}\gets 1,2,\ldots,T\)in paralleldo
17:\(W^{\text{\it Tri}}\gets W_{r}^{\text{\it global}}\)
18:for\(local\)\(epochs\gets 1,2,\ldots,E\)do
19:\(\text{\it Update }W^{\text{\it Tri}}\) using \(SGD\)
20:endfor
21:endfor
22:return\(W_{r}^{T}\)
23:endprocedure
24:procedureTrainStudent(\(\text{\it Labels},W_{r}^{\text{\it global}}\))
25:\(W_{st}\gets W_{r}^{\text{\it global}}\)
26:for\(local\)\(epochs\gets 1,2,\ldots,E\)do
27:\(\text{\it Update }W_{st}\) using \(SGD\) and Labels
28:endfor
29:return\(W_{St}\)
30:endprocedure
```  
**Algorithm 1** Training Procedure of FREDY
Sensors_**2009**, \(9\), 3269-3288; doi:10.3390/s90503269  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Evaluation of Cartosat-1 Multi-Scale Digital Surface Modelling Over France**  
**[PERSON] ***  
Politecnico di Milano, Remote Sensing Laboratory, BEST Department, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy; E-Mail: [EMAIL_ADDRESS]; Tel. +39-02-23996204; Fax: +39-02-23996550  
_Received: 30 March 2009; in revised form: 20 April 2009 / Accepted: 29 April 2009 / Published: 29 April 2009_  
**Abstract:** On 5 May 2005, the Indian Space Research Organization launched Cartosat-1, the eleventh satellite of its constellation, dedicated to the stereo viewing of the Earth's surface for terrain modeling and large-scale mapping, from the Satish Dhawan Space Centre (India). In early 2006, the Indian Space Research Organization started the Cartosat-1 Scientific Assessment Programme, jointly established with the International Society for Photogrammetry and Remote Sensing. Within this framework, this study evaluated the capabilities of digital surface modeling from Cartosat-1 stereo data for the French test sites of Mausanne les Alpilles and Salon de Provence. The investigation pointed out that for hilly territories it is possible to produce high-resolution digital surface models with a root mean square error less than 7.1 m and a linear error at 90% confidence level less than 9.5 m. The accuracy of the generated digital surface models also fulfilled the requirements of the French Reference 3D(r), so Cartosat-1 data may be used to produce or update such kinds of products.  
**Keywords:** Satellite remote sensing; Cartosat-1; digital surface model generation; accuracy assessment; C-SAP.
# Are Green Buildings an Indicator of Sustainable Development?
## 3 Results and Discussion
### Climate and GHG Emissions  
The relationship between the yearly HDD (dependent variable) and GHG emissions from buildings (independent variable) is studied through simple linear regression with data taken from Eurostat Database.  
The ten points in Figure 8 (blue dots) represent the years between 2010 and 2019. The trendline is linear with an equation of y = 0.4321x + 327.26, a \(p\)-value = 0.0003 and an R\({}^{2}\) = 0.8237. However, if we do not consider 2014 (highlighted with a red triangle in Figure 9 and characterised by extreme climate conditions with significantly fewer heating degree days compared to other years), the chart assumes a more linear equation y = 0.479x + 176.93 with more accurate \(p\)-value = 0.0003 and R\({}^{2}\) = 0.8673.  
According to the assumptions stated in the Methodology, \(p\)-value and R\({}^{2}\) are consistent, and the equation correlates the two variables in a very accurate way with a strong significance.  
As expected, fewer heating degree days are related to fewer GHG emissions from buildings due to less dependency on space heating and fossil fuel-based energy generation. This is aligned with the findings of a study [38] that highlights how global warming is decreasing the energy needed in heating-dominated zones. However, global warming will be an issue for the cooling-dominated zones, where the electricity demand and related GHG emissions will increase exponentially [39].  
Figure 7: Relationship between % of renewable energy and GHG emissions in the EU (2010–2013 and 2015–2019).
###### Abstract  
Background: Readout-segmented echo-planar imaging (rs-EPI) with simultaneous multislice (SMS) technology has been successfully applied to tumor research in many organs, but no feasibility study in rectal cancer has been reported, and the optimal acceleration of SMS with rs-EPI in rectal cancer has not been well determined yet. Objective: To investigate the feasibility of SMS rs-EPI of rectal cancer with different acceleration factors (AF\({}_{8}\)) and its influence on image quality, acquisition time and apparent diffusion coefficients (ADC\({}_{8}\)) in comparison to conventional sequences. Methods: All patients underwent rs-EPI and SMS rs-EPI with AF\({}_{8}\) of 2 and 3 (2 \(\times\) SMS rs-EPI and 3 \(\times\) SMS rs-EPI, respectively) using a 3T scanner. Acquisition times of the three rs-EPI sequences were measured. Image qualitative parameters (5-point Likert scale), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion, and apparent diffusion coefficient (ADC) values of the three sequences were compared. Results: A total of eighty-three patients were enrolled in our study. rs-EPI and 2 \(\times\) SMS rs-EPI offered equivalently high overall image quality with a scan time reduction to nearly half (rs-EPI: 137 s, 2 \(\times\) SM rs-EPI: 60 s). 3 \(\times\) SMS rs-EPI showed significantly poorer image quality (\(p<0.05\)). ADC values were significantly lower in 3 \(\times\) SMS rs-EPI compared to rs-EPI in rectal tumors and normalise (tumor tissue: rs-EPI 1.19 \(\pm\) 0.21 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s, 3 \(\times\) SMS rs-EPI 1.10 \(\pm\) 0.26 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s, 0 \(<\)0.001; normal tissue: rs-EPI 1.68 \(\pm\) 0.13 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s, 3 \(\times\) SMS rs-EPI 1.54 \(\pm\) 0.20 \(\times\) 10\({}^{-3}\) mm\({}^{2}\)/s, \(p<\) 0.001). Conclusions: SMS rs-EPI using an AF of 2 is feasible for rectal MRI resulting in substantial reductions in acquisition time while maintaining diagnostic quality and similar ADC values to those of rs-EPI when the slice distance and number of shots are the same among three rs-EPI sequences.  
rectal cancer; simultaneous multislice; apparent diffusion coefficient; readout-segmented echo-planar imaging
###### Abstract
The relative pixel localization accuracy, which is the standard deviation for the absolute location error, has been determined to be below 0.5 m in range and azimuth, in particular for Stripmap mode. However, for all four studies, an offset in azimuth of about 2 m was found. In all cases, the azimuth time derived from SAR data precedes the predicted overpass time given by precise orbit data. The reason for this azimuth bias could arise from inaccurate SAR data processing or from non-considered additional geometric offsets like a significant distance between the spacecraft's center of gravity and the SAR antenna phase center. In order to address this issue, this paper proposes a new and easily applied method to verify the geometric accuracy based on SAR raw data (L0). The method analyzes the timing information from L0 raw data and compares it to the target responses of precisely surveyed point targets before (an explicit) SAR data processing is applied. The method includes simplified range and azimuth compression procedures and focuses mainly on selected data, which contain the target response. The purpose of this method is twofold: on the one hand, the potential geometric accuracy of SAR products is evaluated and, on the other hand, the geometric results after SAR data processing can be cross-checked and analyzed. This helps to detect potential error sources that may have been introduced to L1 products during SAR data processing.
## 7 Results and Analysis
### Privacy Analysis  
Privacy preservation is a fundamental requirement in FL, especially in O-RAN environments where user and network data are sensitive. This subsection evaluates the trade-off between model utility and privacy budget, measures membership-inference attack resistance, and quantifies the runtime overhead of our adaptive differential privacy (DP) mechanism.  
Figure 16 shows that our adaptive noise mechanism consistently outperforms the fixed-noise DP-FL baseline, achieving a 4% boost in utility at the commonly targeted budget \(\epsilon=2.0\). Below this budget, our framework narrows the accuracy gap by up to 6%, demonstrating stronger noise tuning.  
To assess actual privacy protection, we simulate membership-inference attacks under varying budgets:  
As Figure 17 illustrates, membership-inference attack success falls sharply under our scheme at 61% lower than the DP-FL baseline at \(\epsilon=2.0\). This confirms the effectiveness of our adaptive noise scaling in thwarting privacy attacks. We also measure the runtime overhead introduced by privacy operations:  
Figure 16: Privacy–utility trade-off: at \(\epsilon=2.0\), our framework retains 92% accuracy versus 88% for DP-FL [61].  
Figure 15: Reduction in unauthorized access attempts: Our ZTA significantly outperforms both RBAC ([PERSON] et al. [15]) and no defense in preventing unauthorized access.  
Table 13 summarizes these metrics: our method adds only 3.8 ms per round and 8% computation overhead--both well within acceptable bounds for Near-Real-Time O-RAN operation--while delivering stronger privacy protection and higher utility than the classical DP-FL approach. Whereas authors in [62] demonstrated \((\epsilon,\delta)\)-DP with static Gaussian noise, our dynamic, layer-wise noise application keeps accuracy higher across budgets. Membership inference studies in the literature often report 30-40% attack success at \(\epsilon=2.0\)[63]; our framework cuts that to 12.3%. By combining adaptive noise scaling (via a Renyi accountant) with robust aggregation, we reconcile the traditional privacy-utility tension. This makes our solution uniquely suited for latency-sensitive, privacy-critical O-RAN environments, where both stringent timing and strong data protection are mandatory. Future directions include exploring user-level privacy (e.g., DP on user identifiers) and hybrid cryptographic-DP techniques for even stronger guarantees.
## 3 Results and Discussion
It is specified as  
\[Network\ lifetime=Min_{ij}\bigg{(}\frac{\sum_{i=1}CM_{ij}*L_{i}}{N_{m}}\bigg{)} \tag{21}\]  
Here, \(CM_{ij}\) provides the total matrix coverage, \(L_{i}\) gives the life of the sink node, and \(N_{m}\) provides the total number of nodes.The lifetime of a network of this approach is compared with previous routing techniques and is plotted in Figure 8. It is evident from the graph that the hybrid method attains a good life for all SNs than other compared approaches. More than 2400 rounds with 500 nodes are used for analysis. The increased lifetime of this SN can be achieved due to the multi-criteria clustering approach, which reduces the network over-lapping. Table 8 shows the proposed routing protocol achieves an improvement in a network lifetime of more than 8% in comparison with the LEACH approach of 500 nodes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Nodes** & **Proposed** & **MFO** & **SSO** & **Fuzzy-PSO** & **LEACH** \\ \hline
100 & 97.7 & 96.51 & 93.35 & 90.06 & 88.01 \\
200 & 97.01 & 95.01 & 91.39 & 88.01 & 86.02 \\
300 & 96.51 & 96.23 & 89.38 & 87.06 & 85.11 \\
400 & 96.13 & 93.13 & 87.56 & 84.56 & 82.41 \\
500 & 96.12 & 93.31 & 87.88 & 84.66 & 82.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of packet delivery ratio with various routing protocols. Figure 7: Analysis of packet delivery ratio vs. number of nodes.
## 4 Experiment and Results
### Comparison with Other Frameworks  
We compared the classification performance of our framework with other states of the art methods. Our framework outperforms all the traditional approaches in terms of performance (SIFT, COLOR, PITS, GTBB, PRICoLBP, etc.) [26] on all datasets. Moreover, none of these methods incrementally learn food classes and ingredients.  
The deep learning approach Wiser-Net [44] has a better classification performance than our approach on UECFOOD100, UECFOOD256, and Food101 datasets, but it requires training of the network from scratch that requires long training time as compared to fine-tuning. Moreover, it cannot detect food ingredients, and like other CNN suffers from catastrophic forgetting during progressive learning. The comparison with CNN+INCEPTION [45], Ensemble Net [46] shows improvement in accuracy by 6.17% and 11.07% on the Food101 dataset. Although they trained both classifiers by using a batch learning protocol. For the UECFOOD100 and UECFOOD256 datasets, it improves accuracy as compared to CNN [47] by (2.74%, 1.69%), and CNN+INCEPTION [45] by (5.24%, 14.59%) while incrementally learning new categories and ingredients in the food. Similarly, compared to SELC [26] architecture, the improvement in accuracy on the PFID dataset is 7.16%, and the Food101 dataset is 27.28%. The KELM classifier in SELC is non-adaptive. It cannot incrementally learn new classes, and it uses a batch protocol for model training. The framework enhances accuracy by 4.19% for the category and by 20.81% for ingredients recognition as compared to the Arch-D [24] architecture. The comparison with the incremental classifier ARCIKELM by [PERSON] et al. [1] shows that classification performance is improved for UECFOOD100 by 2.24%, UECFOOD256 by 2.83%, and 4.5% for the Pakistani Food dataset. The comparison with SPC [25] (personalized incremental classifier) shows that SPC is unable to detect ingredients or malicious images during online learning. As it is a personalized framework, the authors did not evaluate their model on standard benchmarks. Tables 8 and 9 summarizes the comparison for the food category classification dataset. Table 10 shows the comparison for ingredient detection with other methods.  
Figure 24: Activation values on network trained with UECFOOD256 dataset with a test network of 200 food samples. Blue dots represent correctly labeled food images. Orange dots show mislabeled food images. Grey dots represent food images that are determined as noisy samples.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 5 Conclusions  
The performance of a multi-antenna GNSS receiver under narrowband and wideband jamming conditions was investigated for high precision applications. Different types of electronic interference sources were considered for performance evaluations in terms of tracking, measurement, and position distortions for PM and MPDR beamformers. A number of GPS signal simulations were performed to evaluate measurement distortions. It was observed that MPDR did not induce measurement distortions in the simulations. With actual data, carrier phase measurement biases were minimal and pseudorange measurement errors up to 2 m were observed using MPDR. However, with PM, pseudorange biases up 50 cm were observed in the simulations and up to 2 m with actual data. Carrier phase measurement biases up to few cm were observed with PM in both simulations and actual data processing. The biases observed were different for different PRNs. Carrier phase positioning performance with MPDR was better than PM; the latter was unable to provide fixed carrier phase ambiguity solutions. With PM, position errors up to 10 cm were observed in simulations and up to 1 m with actual data.  
The major contribution of the paper was by [PERSON]. Technical discussions and experimental tests were performed by [PERSON] and [PERSON]. The software was developed by [PERSON] and [PERSON]. [PERSON] supervised the work and provided technical feedback. All the authors contributed to the writing of the manuscript.  
The authors declare no conflict of interest.
## 1 Introduction
Particularly, the test environment, organized in a grid network topology, envisages the separation of the so-called communication and research networks, where the dynamically routed devices within the communication network are used to access and configure the experiment devices within the research network. By using OMF as the experiment controller, researchers can easily setup any reconfigurable mixed wired/wireless testbeds and scenarios on the OMF-enabled research network without requiring any physical access to the corresponding research nodes used. * To support researchers in defining and conducting their experiments on the proposed test environment, several OMF-based template scripts for well-known and frequently used third-party network management and measurement tools are provided. Currently, over twenty tools for artificially causing various network traffic conditions and topology changes have been integrated into the OMF-enabled test environment. Additionally, several configurable experiment descriptions have been prepared that can automatically setup typical evaluation testbed setups and scenarios on the OMF-enabled test environment. Currently, four common testbed setups and scenarios are provided as ready-to-use experiment description scripts: path-persistent and hoppersistent cross-traffic scenarios, mixed wireless and wired network scenario, and wireless ad hoc network scenario. * Finally, the efficiency and applicability of the provisioned test environment, OMF-based template scripts and experiment descriptions are presented by validating the accuracies of several end-to-end round-trip time and available bandwidth (avail-bw) estimation tools and measuring the achievable throughput for TCP and UDP on a two-hop 802.11 g wireless ad hoc network. The structure of this paper is organized as follows: Section 2 presents the related works, providing a review of existing testbeds and frameworks and highlighting the unique contributions of the proposed environment. Section 3 details the architecture of the OMF framework, explaining the roles of its core components and the workflow involved in conducting network experiments. In Section 4, the methods used to develop the testbed setup and integrate third-party tools are described, offering insights into how customizable network scenarios are established within the OMF environment. Section 5 presents four case studies, each demonstrating the application of the OMF-enabled testbed for various network measurement experiments. Finally, Section 6 concludes with a summary of the study's key contributions and proposes potential directions for future research to expand the functionality and applicability of the test environment.
## 3 Design Optimization
### Performance Metrics & Design Criteria
#### 3.3.10 Multi-Parameter Optimization  
Given the complexity of these often somewhat conflicting design objectives, a multi-parameter optimization usually needs to be implemented along the combined metric,  
\[\mathcal{M}=\sum_{I}c_{I}\cdot\mathcal{M}_{I} \tag{17}\]  
to be computationally minimized. Each constituent \(\mathcal{M}_{I}\) (17) is weighted by a numerical coefficient \(c_{I}\), optionally imposing normalization \(\sum_{I}c_{I}=1\). For instance, amongst the numerous possible scenarios, a valving structure \(\Gamma\) may need to be geared to facilitate a certain (minimum) field strength \(f_{\omega}\) (8), while its band \(\Omega\pm M\cdot\Delta\Omega\) (5) and radial extension \(\overline{R}\) (11) need to fit into their still accessible corridors in real and frequency space, respectively. Accordingly, the aggregate metric  
\[\mathcal{M}=c_{f_{\omega}}\cdot\overline{f_{\omega}}+c_{R}\cdot\overline{R}+c_ {\Delta\Omega}\cdot\overline{\Delta\Omega} \tag{18}\]  
ought to be minimized within the available radial and frequency spaces, in the simplest case, with equal coefficients \(c_{f_{\omega}}=c_{R}=c_{\Delta\Omega}=1/3\).
## 3 Proposed Approach
### Brightness Adjustment  
SVIs often show considerable variation in brightness, influenced by outdoor conditions such as time of day and weather. For instance, images captured on sunny days typically exhibit higher luminosity than those taken under cloudy skies. To accommodate these varying lighting scenarios, a process called brightness augmentation is employed [15]. This method alters an image's overall brightness by adjusting each pixel's intensity value, as defined in Equation (1):  
\[I^{\prime}(x)=I(x)+J(x); \tag{1}\]  
Here, \(I^{\prime}(x)\) is the brightness-adjusted image, \(I(x)\) is the original image, and \(J(x)\) is a brightness offset matrix that matches \(I(x)\) in size. In many cases, \(J(x)\) is a uniform scalar value, which is added to or subtracted from all pixels. By altering pixel intensities in this way, one can create images that appear lighter or darker than the original. Figure 2 illustrates two examples: one where a \(-30\) offset darkens the image, and another where a \(+50\) offset makes it brighter.  
Figure 1: Workflow of proposed methods.
An in-depth identification of the primary applications of SMC enhanced by data-driven techniques is achieved, offering a clearer understanding of how these methods have been successfully applied across various domains. 3. A critical evaluation of the most widely adopted SMC schemes is performed, focusing on their integration with data-driven techniques and intelligent algorithms and thus paving the way for more robust and adaptive control strategies. This article not only provides practical insights into the most frequently utilized algorithms but also guides researchers and practitioners in balancing simulation-based testing with real-world prototype implementation when choosing control schemes. Moreover, it emphasizes the performance metrics commonly employed, serving as a reference for improving the efficiency and robustness of systems controlled through DDSMC techniques. This paper is structured as follows: Section 2 introduces the fundamentals of SMC and describes some of the most relevant schemes. Section 3 presents the systematic literature review methodology, and Section 4 shows the findings derived from bibliometric analysis. Section 5 shows the analysis of SMC schemes, applications, and intelligent algorithms under the data-driven framework; the main highlights of the findings are also discussed. Finally, Section 6 provides the conclusions.