esa-sceva/satcom-chunk-collection huggingface dataset:
# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
###### Abstract
Additionally, if these systems are used in contexts deemed high-risk by regulations such as the EU AI Act--e.g., medical diagnostics, recruitment, or critical infrastructure--ensuring robust security and privacy is paramount not only for technical efficacy but also for legal compliance [11]. The proposed solution for these problems will be SecureLLM, with the following aims:  
1. **Lightweight cryptography:** Integrating modular encryption techniques with minimal computational overhead;
2. **Decentralized fine-tuning:** Enabling collaborative training while maintaining confidentiality through multi-party computation (MPC);
3. **Differentially private inference:** Preserving individual user data privacy by injecting carefully calibrated noise during both the training and inference stages. Our contributions can be summarized as follows:
* **Framework design:** We introduce a unified approach to safeguarding LLMs through cryptography, differential privacy, and decentralized fine-tuning;
* **Conceptual guidance:** We present the high-level best practices for implementing SecureLLM in real-world deployments and discuss how its security measures could address the regulatory requirements for high-risk AI systems, including those under the EU AI Act. **Overview and organization:** In this paper, we first provide an extensive background on the security challenges with LLMs (Section 2) and describe related work. Section 3 details the proposed SecureLLM framework, including the formal formulations for decentralized gradient aggregation and differential privacy noise injection. Section 4 discusses implementation considerations and potential performance impacts. In Section 5, we outline potential applications, empirical benchmarks, and future work, and Section 6 provides a comprehensive discussion of the results. Finally, Section 7 concludes this paper.
## 5 A Framework for SDN-SDR-Based Maritime Communications
### The SDN Approach
#### 5.2.2 SDN Adoption  
In maritime networks, establishing stable communication links is a significant challenge due to the constantly changing sea surface, which increases the risk of link fragility caused by sea waves. Therefore, finding a stable route is crucial to ensure network stability and minimize delays [64, 65]. Recently, SDN has emerged as a solution to reduce the complexity of network management tasks [66, 67, 68]. By utilizing SDN, networks can be deployed and managed with greater flexibility, which reduces cost and increases availability. Additionally, the SDN controller integrates and learns from the information contained in the network itself to make intelligent decisions continuously.  
In [14], the authors propose a solution for developing stable and efficient communication strategies for UAVs is an SDN-based framework that takes into account intermittent links, changing network topology due to UAV motions, and power constraints causing departures and arrivals of UAVs. Their framework also addresses the problem of link switching and routing to achieve load balance and extend the system's lifespan. In [54], a controller in the SDN-based UAV network collects network statistics and parameter information and uses the calculated results to make informed decisions. The efficacy of an SDN-based architecture is contingent on the responsiveness of the controller, which is heavily influenced by the controller load and the proximity of the SDN devices. The load imposed on a controller is determined by the volume of requests it receives from the  
Figure 3: Mapping SDN and SDR to OSI model.  
IoT devices it manages. SDN holds the potential for cost reduction, reduced latency, and enhanced network adaptability and reliability.  
In [55], the authors present a joint sleep scheduling and opportunistic transmission scheme in delay-tolerant maritime wireless communication networks based on SDN to find a viable trade-off between energy consumption and delay. Another solution to reducing the delay of response is through the use of multiple controllers, as presented in [56]. Figure 4 presents an SDN with a maritime communications network.  
Figure 5 depicts a potential maritime communications architecture leveraging SDNs and SDRs with multi-controllers. The proposed multi-controller SDN-based maritime network architecture has three planes--service, controller, and resource. This maritime network has a variety of aerial, space, ocean, underwater, and terrestrial IoT objects. Each controller controls a portion of the maritime network. The three controllers must work together to make the best decisions regarding the entire maritime network. This approach reduces both the response time and the load placed on a single controller. The sharing and load balancing of multiple SATCOM links in maritime networks, as well as the ability to overcome bandwidth constraints, are also challenges faced by SATCOMs in maritime networks. To solve these problems, each ship can be considered to be an SDN switch, and their management will be handled by a remote SDN controller [65].
## 2 Related Works  
### Bearing Remaining Useful Life Prediction  
The prediction of the RUL of bearings has been extensively explored to improve the reliability and maintenance of mechanical systems. [PERSON] et al. [12] proposed a weighted time-embedding transformer network that enhances the extraction of time-correlation features, and prediction error was significantly reduced in industrial applications. [PERSON] et al. [13] introduced a cross-domain RUL prediction model with dynamic hybrid domain adaptation and attention contrastive learning to process domain shifts, and high accuracy and generalization were achieved. [PERSON] et al. [14] developed a comprehensive framework utilizing a fractional generalized Pareto degradation model; its performance was enhanced in early fault detection and adaptive failure threshold determination.  
Several studies have emphasized the integration of innovative machine learning techniques with domain-specific adaptations. [PERSON] et al. [15] presented a digital twin-driven graph domain adaptation model in which a multi-layered graph neural network was utilized for robust RUL prediction with limited data. [PERSON] et al. [16] addressed the uncertainty in RUL prediction using a multi-task learning mixture density network; model-driven and data-driven strategies were effectively combined for improved prediction accuracy. Similarly, [PERSON] et al. [17] integrated physical principles into an LSTM-based network to align the prediction results with physical laws, and consistency and accuracy were enhanced. [PERSON] and [PERSON] [18] explored a novel approach by combining entropy-based feature extraction with Elman neural networks; the proposed method showed good prediction accuracy over traditional time-domain features. [PERSON] et al. [19] developed a meta-weighted network that quantifies uncertainty and mitigates negative transfer in limited data scenarios. [PERSON] and [PERSON] [20] proposed an attention-aware graph convolutional network that simultaneously handles temporal and feature correlations and is better than traditional models in prediction accuracy. [PERSON] et al. [21] developed an intelligent framework combining dynamic analysis-assisted wavelet filtering with graph convolution networks, providing a holistic approach to degradation monitoring and RUL estimation.  
Bearing RUL prediction from previous studies has been widely researched with good results [15; 16; 17], but there is still a research gap in exploring bearing RUL prediction to support LSP remanufacturing optimization. The current studies mainly focus on improving RUL prediction accuracy [20; 21], so there is a lack in the integration of RUL prediction results into LSP process optimization, which is essential to enhancing remanufacturing outcomes and extending component life in a more targeted manner.
# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
###### Abstract  
Pile foundations are critical structural elements, and their integrity is essential for ensuring the stability and safety of construction projects. Low-strain pile integrity testing (LSPIT) is widely used for defect detection; however, conventional manual interpretation of reflectograms is both time-consuming and susceptible to human error. This study presents a deep learning-driven approach utilizing transfer learning with convolutional neural networks (CNNs) to automate pile defect detection. A dataset of 328 reflectograms collected from real construction sites, including 246 intact and 82 defective samples, was used to train and evaluate the model. To address class imbalance, oversampling techniques were applied. Several state-of-the-art pretrained CNN architectures were compared, with ConvNeXt-Large achieving the highest accuracy of 98.2%. The accuracy reported was achieved on a dedicated test set using real reflectogram data from actual construction sites, distinguishing this study from prior work relying primarily on synthetic data. The proposed novelty includes adapting pre-trained CNN architectures specifically for real-world pile integrity testing, addressing practical challenges such as data imbalance and limited dataset size through targeted oversampling techniques. The proposed approach demonstrates significant improvements in accuracy and efficiency compared to manual interpretation methods, making it a promising solution for practical applications in the construction industry. The proposed method demonstrates potential for generalization across varying pile lengths and geological conditions, though further validation with broader datasets is recommended.  
CNN; deep learning; pile integrity test; transfer learning Article
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 2 Related Work
### Considered Cloud-Based Streaming Architecture  
In this paper, we consider the commonly used architecture for cloud-based on-demand and live streaming applications (such as Amazon CloudFront media streaming [24], IBM Video Streaming [25], Microsoft Azure Media Service [26] and Google Video Streaming services [27]). The architecture allows the user of the service to create a streaming application by enabling a chain (or pipe) of video processing starting from the video supply (live or recorded content) until video streaming to the potential clients of the enabled application. The nodes in the chain are often deployed as virtual machines or containers in the cloud.  
As illustrated in Figure 1, the different services (video reception, processing, storing and streaming) are often running in virtual machines or containers.  
A management tool is used to control and orchestrate the virtual machines (VMs)/ containers involved in the application pipe/chain created in the cloud based on load balancing and VMs/containers health (processor load, memory load and network load) [24; 25; 26; 27]. Streaming video nodes are often deployed on physical servers spread all over the world to be as close as possible to the final users. These nodes are connected to the main datacenter through private networks (most likely virtual private networks) often deployed on the Internet. Cloud companies have full control of routers, enabling private networks that allow multicast routing protocols to be activated over them to minimize the consumed bandwidth in the cloud backbone network. To avoid firewalls, HTTP-based streaming protocols are used where the video serves as small segments (chunks), such as web pages [5].  
Generally, cloud-based streaming applications provide the client with a web page presenting the video catalog as HTML5 video tags, as done in YouTube, Dailymotion and Facebook video, for example. The links encoded on the page are generated to minimize the consumed resources in the cloud and offer the best possible user experience. The client is often redirected to the nearest video streaming node to him. As multicast routing is disabled on the Internet, each client is served through a unicast stream.  
In this paper, we focus on minimizing the server node bandwidth as well as the consumed bandwidth in the network connecting the different nodes in the chain run by the cloud-based streaming application. This is portrayed as a set of interconnected network devices in the cloud/CDN of Figure 1.
###### Abstract
A distributed fiber Bragg grating (FBG) sensor can provide more accurate and reliable strain measurement data in underground mines than can traditional monitoring equipment [33]. Such sensors are also highly sensitive to the vital signs of rescuers and can meet the needs of emergency rescue [34]. This technology is now widely used in geotechnical engineering structures, where it enables real-time monitoring of overall structural health and offers major practical benefits [35; 36; 37; 38; 39; 40]. This paper applies FBG sensing technology to the new coal pillar-free mining method. A big data remote online real-time data monitoring system is designed and established that integrates multi-source information publishing and monitoring technology via the cloud server ECS (Elastic Compute Service), cloud database RDS (Relational Database Service) and cloud website technologies. This approach can improve the intelligence, adaptability, security and stability of the whole monitoring system and realize the mine pressure-monitoring and early warning target 'find quickly, control well, sort out early, resolve well.'
## 3 Proposed Method  
Figure 2 presents the flowchart of the proposed method. The main scheme is implemented in four steps: PQD generation in a computer simulation, feature extraction, optimization of feature selection, and pattern recognition. In this paper, the proposed algorithm is designed to classify 13 classes of different signals, which are generated as a pure sine signal, sag, swell, interrupt, harmonics, sag with harmonics, swell with harmonics, interrupt with harmonics, flicker, oscillatory transient, impulsive transient, periodic notch, and spike. Second, by utilizing VMD to decompose the target signal into several IMFs, the feature vector is extracted in the statistical analysis method. Third, permutation entropy is imported to filter the invalid IMFs. Subsequently, the Fisher score is adopted to eliminate the redundant features to optimize feature selection. Finally, the classifier is realized by SVM to recognize different PQD patterns.  
The training data is generated from computer simulation with the 13 classes mentioned above. The dataset size of each class is 1000. After training, several analysis methods, such as confusion matrix and receiver operating characteristic curve, are imported to evaluate the performance of the training model, which is presented in Section 4.4. In another word, the artificial dataset of the simulation signals is utilized for the training and developed set. Additionally, the trained model will be tested on the real experimental setup to verify the effectiveness of the classifier, which is presented in Section 4.5.  
As specific implementation, the data generation and preprocessing, including the VMD algorithm, is realized on the Python script in the training stage. Subsequently, the public feature extraction library, LibXtract, is integrated with the console application to extract features. Afterwards, the feature heuristic selection and classifier model is trained on the server setup in Anaconda Python. In the training stage, the fault simulator system and the data acquisition system are both integrated on  
Figure 2: Main flow chart of the proposed method.  
the LabVIEW environment. In particular, the communication is realized by the data-logging and supervisory control (DSC) packet. Additionally, the DAQmx driver packet realizes the data acquisition system. The experimental voltage/current data in time domain is obtained and saved as csv format in files. As an alternative method, the proposed algorithm can be realized on other platforms, such as C++ and Matlab.
# Micro-Satellite Systems Design, Integration, and Flight
## 1 Introduction
### Systems Analysis Approach  
The manuscript will use a model-based systems engineering (MBSE) approach [26; 27; 28] to analyze and design both the CubeSat and the ChipSat. The MBSE approach maintains traceability throughout the satellite's life cycle [29]. The design process can be shown by the systems engineering Vee diagram [30], which is a flow chart of the product life cycle. The left side of the Vee diagram shows how the design process goes from a high-level concept to specific component-level requirements. Then, the right side of the Vee diagram shows the integration process. On the right side, the system is assembled from component to working prototype through validation and verification methods. Expressed briefly, a product is designed from the top down but tested from the bottom up. The Vee approach makes sure that the design remains consistent with the original problem description. It also guarantees final functionality. Testing begins at the component level, so that functionality is tracked up to the final acceptance testing. Below, in Figure 2, is a visual representation of the Vee diagram used in the manuscript.  
The manuscript will use the Vee diagram process. Section 2 starts with modeling the mission systems. The satellite systems are described and then broken down (top-down from concept design to the component level).  
Sections 3 and 4 track the ChipSat and CubeSat development. The satellite requirements are stated and then followed bottom-up through verification and validation tests.  
Figure 2: The systems engineering “Vee Diagram”.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 2 Background
### Taxonomy
#### 2.1.3 Ad-Hoc vs. Post-Hoc  
For ad-hoc explanation methods, the model itself is designed to be intrinsically explainable. Common current approaches are limiting learned features to only explainable features or by altering the optimization procedure to focus on optimizing for explainability rather than accuracy. The goal of this approach is to design a deep learning model that is inherently explainable and opposes the notion that there is a trade off between accuracy and explainability. On the contrary, post-hoc explanation techniques provide explanations after the classification is made. These are more common in the literature due to ease of implementation. Some refer to these as diagnostic methods [86] due to their utility for diagnosing and their apparent limitations for providing a complete explanation for the end user. Examples of these are saliency maps [87; 88], Grad-CAM [84], Respond-CAM [89], and SHAP [80] where the explanation is provided after-the-fact.
## 3 Trajectory Tracking Controller Design Based on LADRC  
The trajectory tracking of a powered parafoil system faces an underactuation problem, requiring the utilization of two control variables to facilitate movement along the three coordinate axes, as shown in Figure 2. Hence, it becomes imperative to employ the guidance law before controller design to surmount the challenge posed by underactuation.
## 1 Introduction
### Contribution
* \(CI_{ij}^{(l)}\): Information transmitted from the \(i\)-th CN to the \(j\)-th VN at the \((l)\)-th decoding iteration, where \(l=1,2,\ldots,T_{max}\). The initial value \(CI_{ij}^{(0)}\) is set to \(\mathbf{0}\). * \(VI_{ij}^{(l)}\): Information transmitted from the \(j\)-th VN to the \(i\)-th CN at the \((l)\)-th decoding iteration. * \(\mathbf{v}^{(l)}\): Posterior information at the \(l\)-th decoding iteration, where \(v_{j}^{(l)}\) represents the posterior information of the \(j\)-th bit at \(l\)-th iteration. * \(c_{j}^{(l)}\): The \(j\)-th bit of the codeword after the \(l\)-th decoding iteration.
## 5 Results and Analysis
### Live Measurement Results and Comparative Analysis
The SNR results revealed  
Figure 6: Relative Plots used to obtain Mean RSSI and Standard Deviation by Gateway in Table 2.  
Figure 7: Relative Plots used to obtain Mean SNR and Standard Deviation by Gateway in Table 3.  
a significant noise on channel zero and that the noise source was closer to GW-2 than GW-1. This was inferred from the GW-2, channel zero mean RSSI of \(-96.6\) dBm and SNR of \(-8.9\) dB compared to the GW-1 results for the same channel of a mean RSSI \(-110\) dBm and SNR of \(-8.05\) dB. Based on these figures, the noise in the \(868.1\) MHz channel was \(14\) dB higher in the proximity of GW-2 compared to GW-1. Despite this, GW-2 performed better at capturing packets on the \(868.1\) MHz channel due to a better RF path and subsequent higher signal strength. Because of the RSSI and SNR results, some further testing was performed over seven days, and some results graphed in Figure 8. Comparing Figures 5 and 8, it was found that (a) a source of noise that operated predominately at night, and (b) the RF path improved at night. This meant that SF12 transmissions could be received at a level of \(-95\) to \(-100\) dBm despite noise levels at \(15\) dB higher. During this test period, very few night-time SF7 transmissions were successful, so the reception of SF12 packets could be attributed to the additional receiver sensitivity when demodulating SF12 transmissions. Deduplication errors were identified on outdoor testing, and this was expected as both gateways used in this instance used mobile network backhaul. During this testing ICMP, ping packets ran on both backhaul links and the latency recorded in Table 6. Although the mean latency for both backhaul links was similar, the GW-2 link had nearly 10,000 instances of latency over 350 ms, and both had instances of latency of over two seconds. The inspection of the gateway receive times indicated that the gateways recorded receive times within microseconds of each other, but these packets could be received by the NS a second apart on occasion. The LoRaWAN range was not explicitly measured on this test, although it is known that sensors are operating at ranges greater than 4 km from gateways on this network.
# Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing
###### Abstract  
The sharing of DNA sequence data is essential for advancing medical technology and fostering innovation in healthcare. However, DNA sequences encode sensitive information, such as gender, physical attributes, and genetic predispositions, necessitating stringent privacy safeguards. Existing data-sharing frameworks often fail to adequately address these privacy concerns. To overcome these challenges, this study proposes a blockchain-based, privacy-preserving ecosystem for DNA sequence sharing. The system employs a decentralized architecture to manage digital identities and access permissions, ensuring robust privacy and data security. Smart contract functionalities allow users to assign granular access controls to specific DNA sequence segments, enabling selective sharing with trusted recipients. Furthermore, research institutions are required to obtain certification and classification from governmental authorities, enhancing trust and system reliability. The user-centric design prioritizes privacy, security, and autonomy, simplifying operational processes and fostering user trust. By incentivizing DNA data sharing, the proposed model aims to accelerate medical advancements while maintaining stringent privacy protections, establishing a secure and scalable ecosystem for DNA sequence sharing. Experimental results from a prototype implementation indicate that the system achieves a throughput of up to 10-20 transactions per second for identity and access operations while incurring acceptable on-chain costs (\(\approx\)1.3 million gas to deploy contracts and 400-800 k gas per user registration). These performance metrics underscore the feasibility and efficiency of the proposed approach.  
blockchain; DNA sequences; access control; privacy preservation +
Footnote †: journal: Applied sciences