esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Equipment and Signal Processing
### Labeling Data  
Marking defects is one of the key steps of model training and precision optimization in the process of pressing rigging defect detection. To ensure that the convolutional neural network can accurately identify the defect area of the compressed rigging, this paper adopts the COCO data set, which is mainly used for target detection due to its characteristics of multi-class object labeling. The annotation method is used to label the data set of clamped rigging defects; that is, each defect instance is labeled with a bounding box and a class label. The bounding box is used to indicate the specific location of the defect in the image, while the category label is used to distinguish the different types of defects. To facilitate the marking of images, the original images after noise reduction are spliced, and 25 original images are spliced vertically, which helps to visually present the overall distribution of defects more clearly and delimit the boundary box of defects more accurately. The image after splicing is marked, and the sample image with marked defect is obtained, as shown in Figure 5. To train the CNN model for the detection of defects in pressed rigging, the defects of pressed rigging are mainly divided into two categories: broken wire defects and stress concentration defects. The shape of broken wire defects is more obvious, usually manifested as red dots, so it is easier to mark by visual labeling. When marking, the broken wire defect is usually framed with a yellow border to identify the location of its break. The stress concentration defect usually reflects the phenomenon of stress concentration in some areas due to excessive or uneven force in the long-term use of the wire rope. This type of defect usually has no obvious fracture or physical damage but can manifest as stress anomalies in local areas. Due to the blurred visual representation of stress concentration defects, a different strategy was adopted in the marking process, using a pink border to frame and identify these stress concentration areas.  
By using the above labeling method, this paper constructs a data set of 1000 annotated images of pressed rigging defects. These images cover different defect patterns and stress concentration areas, providing rich training samples for subsequent training of convolutional neural networks.  
Figure 4: Cloud image of defect distribution before and after noise reduction.
## 4 Experimental Results on DNNs
### Experimental Setup  
**Models and datasets.** We train the AlexNet network [53], ResNet [54], and MobileNet-V2 [55] on the CIFAR-10/100 [56] and the ImageNet [57]. Additionally, for language modeling tasks, we train the Transformer model [58] on the WikiText-103 [59] and the LSTM model [60] on the PTB [61].  
**Training settings.** We utilize the standard training setting in all our experiments, including epoch, batch size, momentum, learning rate, and other hyperparameters. Specifically, we follow SOTA settings in [62] for CIFAR-10 and [54] for ImageNet. We follow [63] for Transformer on WikiText-103 and [64] for LSTM on PTB. We opt to implement the GMMLOWP quantization scheme, which is extensively documented in Google's open-source repository [4]. Our experiments are based on the classic low-precision training framework presented in [34], which has demonstrated comparable performance to full precision across various models and tasks using uniform low-precision. For instance, when training ResNet-50 on ImageNet, the accuracy degradation is less than 1% compared to its full precision counterpart. Our code adjusts the bit width based on the open-source repositories [34] and CPT method [46]. To mitigate the influence of stochastic elements in our experiments, the results are averaged over four runs.  
**Precision settings.** In our experiments, we investigate the impact of weight quantization on training. We compress the weights of different layers to various bit-widths and train the models to observe the final training performance. Referring to Section 3, we establish three comparative **factors**:  
* A: The training bit-width is set based on the number of **Accumulations** in matrix multiplication during computation.
* F: The training bit-width is set based on the **Frequency** with which elements in the matrix are used during computation.
* D: The training bit-width is set based on the **Depth** of the matrix within the model. Concurrently, we establish the following four **strategies** for bit-widths of layers:  
Figure 6: Distance and variance with increasing depth of layers.  
U: Regardless of the variations in the comparative factors, assign the same training bit-width to all layers (the method proposed in [34]).
* C+: Set the training bit-width according to the comparative factors, with a positive correlation to the comparative factors.
* C\(-\): Set the training bit-width according to the comparative factors, with a negative correlation to the comparative factors.
* R: Randomly assign training bit-widths to each layer.  
Since higher bit-widths typically provide more accurate numerical representations while simultaneously increasing model size, we conduct comparative experiments using similar bit-width ranges and model sizes to ensure fair comparisons between different strategies.
## 1 Introduction
This study tries to answer the following questions: To what extent can a single-channel LST model achieve the accuracy of a two-channel LST model? Does a PMW outperform an SMW? Can we characterize uncertainties for single-channel Meteosat LSTs? To address those questions, we compare SMW, PMW and GSW using identical satellite observations from MSG. The evaluation is based on more than 60,000 _in situ_ LST measurements from four dedicated LST validation stations operated by the Karlsruhe Institute of Technology (KIT). The stations are located in different climate zones and include dry to very moist atmospheres. Furthermore, we perform a series of sensitivity analyses to test the robustness of single-channel LST models to input uncertainties. The characterization of input uncertainty and its propagation towards the final LST retrievals is important for the estimation of product uncertainties, which can ultimately be used as quality indicators by users. This study is unique in that it compares PMW, SMW and GSW LST retrievals from identical satellite acquisitions with a large number of _in situ_ measurements across different climate zones.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 3 Indicative Use Cases
### Energy Management and Decision Support System  
The roll-out of smart meters around the world is part of a broader transformation in the energy sector that includes the development of smart grids, microgeneration, and the transition towards low carbon emissions. Smart meters, used to accurately measure energy consumption in households or enterprises, enable, among others, customer cost awareness, which can lead, in turn, to the shifting of energy consumption to cheaper times of the day, providing the customer with cost savings and accurate bills based on actual usage instead of estimates [64]. In addition, smart meters can measure the export of microgeneration and facilitate remuneration for electricity exported to the grid. Therefore, customers will be empowered with several advanced services and applications, such as holistic energy monitoring of their households, as well as optimum policies for buying from or selling energy to the grid. The latter case is applicable when households are equipped with renewable source generation infrastructures, such as solar panels or wind turbines, along with home battery storage. This case is also applicable when end users have electric vehicles (EVs) with bidirectional charging.  
Based on the above, the provision of advanced smart services to domestic customers may include, among others, personalized and automated optimum energy usage for a household equipped with a smart meter, as well as maximization of self-consumption or minimization of carbon footprint based on household goals. Therefore, a fully deployed energy management and decision support system includes data collection from various dispersed nodes and appropriate data processing, leveraging security and privacy policies at the same time. Hence, an IEC continuum infrastructure can effectively address the aforementioned challenges. Since groups of smart meters are connected to data aggregation points via neighborhood area networks (NANs) [65], edge servers can optimize efficient data processing and fast decision-making when required. In the same context, macroscopic data collected from wide area networks (WANs) can be offloaded to the cloud for big data analysis and network reconfiguration at a large scale.  
This approach may be applied to a smart home scenario, where federated learning can be employed for energy consumption prediction in households [66]. The considered structure is also depicted in Figure 6. During training, data remain localized. Hence, on the one hand, privacy concerns are mitigated, and on the other hand, faster training times can be achieved, since each local NN in a household is trained on a different sample collection. The master model is periodically updated, and the new parameters are inferred from the participating households.  
Figure 6: Federated learning in a smart-home scenario [66].
## 5 Discussion
### Navigating Performance vs. Precision Trade-Offs  
The balance between system responsiveness and security controls is a constant tension in the AI deployment landscape. The AVI evaluation shows that security interventions do not necessarily entail prohibitive latency. The measured latency overheads--an average of 85 ms without RAG and 450 ms with RAG--suggest that real-time inspection and filtering can be practically integrated into interactive applications, especially those that do not operate under strict request processing latency constraints [63].  
However, domain-specific tuning remains critical. For example, applications in emergency services, trading platforms, or online learning may have stricter thresholds for processing latency. In these cases, organizations can selectively enable or disable certain modules in AVI or use risk stratification to dynamically apply security controls.
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 3 Problem Formulation
### Joint Optimization of UAV Location and RIS Phase Shifts
This ensures an accurate Q-value estimation and maintains stability throughout the training process. In step 11, the actor policy is updated using a sampled policy gradient, which links the action-value function \(Q(s,a|\theta^{Q})\) to the actor's actions \(\mu(s|\theta^{u})\), aiming to maximize expected returns computed across a mini-batch of transitions for stable learning. Subsequently, in step 12, the target critic network \(\theta^{Q^{\prime}}\) and target actor network \(\theta^{u^{\prime}}\) are updated by adjusting their weights towards the main networks \(\theta^{Q}\) and \(\theta^{u}\), respectively. \(\tau_{tc}\) and \(\tau_{ta}\) represent the learning rates for the target critic and actor networks' soft updating coefficient, respectively, where \(\tau_{tc}\), \(\tau_{ta}\ll 1\). The utilization of soft updating prevents the instability and divergence issues typically associated with Q-learning. Finally, in step 13, the algorithm assigns the next state value \(s_{t+1}\) to the current state \(s_{t}\) at each time step of an episode during training, effectively updating the current state variable. Steps 5 to 13 are repeated for all time steps \(t=1,\ldots T\) within an episode to ensure effective learning through experience replay and updates to both the actor and critic networks. The proposed DDPG-based algorithm efficiently handles the continuous action space of joint UAV trajectory and RIS phase shift optimization in dynamic urban environments. Its primary advantage is the ability to learn optimal policies through continuous interaction with the environment without requiring a full system model. This allows for adaptive and real-time optimization under complex mobility and blockage conditions. Additionally, DDPG stabilizes learning using experience replay and target networks, ensuring robust convergence. However, its limitations include a higher computational complexity compared to classical optimization methods, sensitivity to hyperparameter tuning, and potential convergence instability under extremely sparse or highly dynamic training scenarios.
# A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data
## 3 Methods
### Baselines  
Here, we present the baseline methods used during the experimental section, whose performances will be compared with those of the proposed DBL-FS model. In particular, we considered three approaches, one baseline aimed to solve regression problems (as DBL-FS) and the other two methods specifically designed to solve classification tasks:  
* As the first baseline, we included a regression Gaussian Process (GP) [35], using the implementation provided by the GPy library (available at github accessed on 9 December 2021). We have selected this model since it allows us to define lineal kernels with ARD, so that we can work in the dual space and learn the relevance of the different input features.
* Next, we included an SVM [36] with a linear kernel using the scikit-learn library [37] to also optimize the model in the dual space.
* The last selected baseline is the recently proposed adaptation of Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA) [38] to work in the dual space, the Kernelized SSHIBA (KSSHIBA) [39] is available at github accessed on 29 March 2021. This algorithm can simultaneously combine different data sources or views (in our case, different tissues) in a common latent space providing a low-dimensional representation of the data. In addition, this model can also include an additional output view to categorically model the target variable (patient or control sample), as well as a linear kernel with ARD coefficients over the input features (equivalent to the GP configuration).  
Both GP and KSSHIBA use an ARD to determine the relevance of the input features, but they do not have a prior distribution or constraint to force their input weights to be sparse and, therefore, obtain a real FS. Meanwhile, DBL-FS imposes sparsity with the Gamma prior to actually promote zero values in the model weights which, in turn, automatically eliminates the least relevant features.  
Furthermore, it is important to mention that deep learning models are not included in this study, as these methods are severely limited by the sample size required to learn the model parameters. Therefore, although models such as convolutional neural networks have promising results in image analysis, they also pose serious challenges when working with datasets of small sample size. Furthermore, we have explored other baselines such as random forests but have not included the results due to their poor results. Nevertheless, all the methods under study will be evaluated with different configurations to be able to analyze different properties and, hence, to carry out an extensive study and analyze their advantages and disadvantages in comparison to our model.
## 1 Introduction
### Gaps and Contributions  
Table 1 provides an overview of the relevant studies, highlighting key aspects that were overlooked in previous research. The following research gaps have been identified based on this review:  
* Limited Objective Scope: Most studies derive their optimization results based on a single objective or a limited set of objectives, without considering a broader set of factors impacting system resilience.
* Insufficient Integration of MES and Dynamic Reconfiguration: A significant gap exists in the integration of MES deployment and dynamic boundary reconfiguration in response to natural-disaster-induced events. While some studies address the static placement of MES, they overlook the critical need for dynamic network reconfiguration in post-disaster scenarios.
* Overlooked MES Capacity-Economic Cost Relationship: The relationship between MES capacity and its economic cost is not adequately addressed in the literature. Specifically, there is a lack of simultaneous optimization of both MES capacity and deployment strategies.  
To address these gaps, we have developed a novel two-stage stochastic programming framework for resilient NMGs with dynamic boundaries. This framework simultaneously considers the sizing, pre-positioning, and re-allocation of MES, allowing for dynamic reconfiguration of NMG boundaries using remote smart switches. This enables MES to be adaptively relocated, facilitating a flexible network topology that can accommodate evolving system conditions. Additionally, we have implemented a customized progressive hedging algorithm (PHA) to efficiently solve the two-stage stochastic programming problem. The main contributions of this paper, which directly address the identified research gaps, are summarized as follows:  
* In this new problem, a scenario-based two-stage mixed-integer stochastic programming model is formulated. Specifically, the objectives are carefully chosen considering various dimensions, including reconfiguration time, effectiveness, operational constraints, and economic costs.  
* A two-stage framework is proposed for the collaborative utilization of dynamic boundaries and mobile energy storage within NMGs. This framework enables real-time reconfiguration of the network topology and the adaptive re-allocation of MES.
* The proposed model simultaneously optimizes the sizing, pre-positioning, and re-allocation of MES, considering their economic costs. To the best of our knowledge, this is the first work to integrate these three aspects in the reconfiguration of NMGs.  
The remainder of this paper is structured as follows. Section 2 describes the problem focused on in this paper. Sections 3 and 4 present the formulation of the first-stage and second-stage problems. Section 5 provides the customized progressive hedging algorithm. The results of simulation experiments are discussed in Section 6. Section 7 concludes this paper.
## 4 Experimental Validation  
### Experimental Environment  
The field diagram of the antenna array AOA positioning test is presented in Figure 3. The test was performed in a room which was 7 m long and 6 m wide. The passive antenna array was placed on the ceiling, and its axes were as shown Figure 1. The source used in this test was a tag that transmitted the Bluetooth signal.  
The hardware of the array antenna and source is shown in Figure 4. The receiver collected data from the peripheral circle of the polarized antenna in a time-sharing manner, and the collection sequence was (1-2, 3-4, 5-6, 7-8, 10-11, 12-13). The number of collection cycles was two, and eight data points were collected at each time. The data collected by antenna No. 9 in the middle, which was the frequency estimation antenna, entered the receiver through another channel. The source uses a tag that emits Bluetooth signals.  
The Bluetooth array AOA position was determined by measuring the source emission signal, realizing the signal arrive angle positioning. The accuracy of frequency estimation has a significant influence on positioning accuracy; therefore, in the test, the Bluetooth tag antenna used an omnidirectional antenna. However, in the actual environment, the source is placed in a different direction so that the positioning result can have a certain degree of difference. Under the condition of different source locations, positioning accuracy was different. The farther the distance between the source and the base station, the lower the positioning accuracy. To verify the validity of the proposed algorithm, the static test, rotation test, and dynamic test were conducted on the source. It was assumed that the height of the source was known, and the true height of the source was substituted into the positioning formula. The defined relative coordinate system had the origin right below the center of the antenna array. In the actual scene, the \(x\)-axis and \(z\)-axis point, as shown Figure 3, which is consistent with the direction in Figure 2. The height of the source was about 3.35 m. To reduce the calculation complexity, the search range was limited to the range of the array beamwidth. The simulation results based on Matlab were as follows.  
Figure 4: The hardware of the array antenna and source.  
Figure 3: Test field image.
# A Real Model of a Micro-Grid to Improve Network Stability
## 2 General Technical Specifications of the Smart Energy Model  
Considering the simplicity of the physical materialization, safety of experiments, and low implementation costs, together with the proposed usage in households, farms, and other small energy consumers, the SEM operates with 24 VDC. In all, the entire model is based on physical testing and simulation, stabilizes the grid, and enables maximal exploitation of the electric energy produced by the appended renewable energy sources (such as photovoltaics, wind power plants, hydroelectric stations,and fuel cells). Energy consumption from the grid is controlled by the real-time embedded industrial controller made by National Instruments for industrial control systems--CompactRIO (Austin, TX, USA) and CB1 control blocks (Brno, Czech Republic). The smart grid portion of the SEM is connected with the smart metering portion; smart metering and consumption control represent typical small consumers that participate in the smart energy system, utilizing the demand-response principles. Depending on the cost of the energy, the individual consumer, in conjunction with the central control station, will switch on or off large electrical appliances in the household or, in general, \"control\" their consumption rates. A block diagram of the proposed experimental micro-grid is shown in Figure 1, which includes a functional characterization of the blocks and principles of the computer-based control; the thick lines denote the energy flows.