esa-sceva/satcom-chunk-collection huggingface dataset:
# Computing Fault-Containment Times of Self-Stabilizing Algorithms Using Lumped Markov Chains
## 1 Introduction
Only in a few cases fault-containment metrics have been considered [10; 11]. This is even more surprising considering the fact that the many techniques available to determine the worst case stabilization time of an algorithm, e.g., potential functions and convergence stairs, can also be used to compute the containment time. This paper discusses a technique to analyze the containment time of randomized self-stabilizing algorithms with respect to memory and message corruption. The execution of the algorithm is modeled as a stochastic process. Let \(X\) be the random variable that represents the number of rounds the system requires to reach a legitimate configuration when started in a 1-faulty configuration. Then the MTTR of the algorithm is equal to \(E[X]\), the expected value of \(X\). Thus, we are interested in upper bounds for \(E[X]\). Sometimes it is possible to derive an explicit expression for \(E[X]\) or use results about absorbing Markov chains for this purpose. These equations may be solvable with a software package based on symbolic mathematics. However, the state space explosion problem precludes success for many real world problems. An important technique for the reduction of the complexity of Markov chains is _lumping_[12]. Lumping is a method based on the aggregation of states that exhibit the same or similar behavior. It leads to a smaller Markov chain that retains the same performance characteristics as the original one. But often lumping is not immediately applicable because the structure of the Markov chain is too complex. In some of these cases a weaker form of lumping can lead to Markov chains with a simpler structure that can still be used to derive an upper bound for the absorption time. The contribution of this paper is a discourse about the containment time of self-stabilizing algorithms. We present and apply techniques based on Markov chains to compute upper bounds for this metric. In particular we demonstrate how lumping can be applied to reduce the complexity of the Markov chains. To demonstrate the usability of the techniques we apply it to a new self-stabilizing coloring algorithm as a case study. We derive an absolute bound for the expected containment time and show that the variance is bounded by a surprisingly small constant independent of the network's size. We believe that the techniques can also be applied to other algorithms.
###### Abstract
However, these estimations are significantly influenced by control system inputs (e.g., thrust change, control surfaces deflections) and the sensitivity of the mathematical model to varying flight parameters (dependents on flight states). The original contribution of this paper is the integration of wind estimation methods based on the three-dimensional navigation triangle, combined with a simplified flight dynamics model. This paper proposes a complementary approach: analyzing measurements in three reference frames and their interdependencies can yield accurate results for slowly varying signals, while dynamics-based models are more effective for estimating rapidly changing components. Based on prior findings [28], a complementary filter was chosen for data fusion. The study assumes that all signals necessary for estimation were practically available. An algorithm was developed using data from existing onboard systems, like Air Data Inertial Reference Unit (ADIRU) and the Global Positioning System (GPS). A unique aspect of this study is the use of a simulation environment that allows for both the generation and evaluation of ideal wind gust parameters. Simulations were conducted using a complex nonlinear aircraft dynamics model in the X-Plane \(11\) environment, while the 3D gust estimation algorithms were built on mathematical models suitable for efficient computations.
## 3 Results and Discussion
### Comparison of Spectral Index Separability for Different Fire Severities  
From Figure 9, the comparison of spectral index separability for different fire severities based on the post-fire image shows that the highest separability (M \(>\) 3) for the three VIs was primarily concentrated in the near-infrared to shortwave infrared region (705 nm-1341 nm) and the visible to near-infrared region (396 nm-740 nm), the shortwave infrared region (1341 nm-2468 nm) combined with the visible-near-infrared-shortwave infrared region (560 nm-1341 nm), and the shortwave infrared region (1784 nm-2468 nm) combined with the shortwave infrared region (1341 nm-1784 nm). For the pre- and post-fire image combination, the regions of high separability (M \(>\) 3) for the three dVIs were generally similar to those in the post-fire image. And there was a notable decrease in separability for the dDI category, a slight decrease for the dRI category, and an increase in separability for the dNDI category. Specifically, the dNDI category showed higher separability, particularly in the shortwave infrared region (1341 nm-2468 nm) combined with the visible-near-infrared-shortwave infrared region (560 nm-1341 nm), where large areas exhibit high separability (M = 5). The distinction between unburned and burned areas was clear, with varying levels of separability between different fire severities in the burned areas. Notably, the separability between light and moderate-high severity was relatively low. In summary, the dNDI category derived from the pre- and post-fire image combination exhibited the highest separability capability.
## 3 Methodology  
This section outlines a detailed methodology for recognizing human actions using our proposed ConvResBiGRU-SE model. As shown in Figure 1, the methodology includes three main components: data acquisition, data pre-processing, and model development.  
In the data acquisition phase, we collect sensor data from wearable devices, focusing on the UCI-HAR and WISDM datasets, which offer diverse real-world scenarios for activity recognition. During the data pre-processing process, we perform essential phases such as noise reduction, data normalization, and segmentation to prepare the raw sensor data for deep learning.  
Finally, the model development phase describes our innovative architecture integrating CNNs, residual connections, BiGRU, and SE mechanisms to achieve robust activity recognition. Each component is meticulously designed to address specific challenges in sensor-based HAR while ensuring computational efficiency and model interpretability.
## 3 Results
### SCr Measurement during Routine Care  
A total of 673,011 SCr assessments were collected during the 126,367 included admissions and 68% (\(n\) = 460,154) of SCr assessments were collected between the hours of 3:00 a.m. and 7:59 a.m. (Figure S2). Of the 673,011 SCr assessments, 126,367 were the first (index) SCr recorded during the hospital stay and 546,644 were follow-up (non-index) assessments. The duration of time from the previous SCr assessment was 21 to 27 h for 53% (290,446 of 546,644) of non-index SCr assessments, representing fluctuations in the time of day that blood was collected each morning (Figure 5). Only 298,989 (54.7%) of non-index SCr assessments were collected \(\leq\)24 h from the previous SCr. Adding a 4-h margin to AKI calculations (from 24 to 28 h) captures an additional 150,523 (27.5%) of SCr assessments. This 4-h margin accounts for variability in clinical practice regarding the time of day that blood was collected on subsequent days and the impact of expanding observation windows by this 4-h margin was further investigated.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 4 Problem Statement
### Design Goals  
Under the system and threat models mentioned above, aiming to design a privacy-preserving swarm learning (PBSL) scheme, the proposed scheme should provide confidentiality and privacy guarantees, resist Byzantine attacks, and reduce computational overheads. And our scheme should achieve the same precision as classical swarm learning (SL). Specifically, the following goals are achieved:  
* **Confidentiality**: Guaranteeing the confidentiality of the global model stored in the blockchain. Specifically, after swarm learning, only **MC**s can access the global model stored in the blockchain.
* **Privacy**: Guaranteeing the privacy of each **MC**s' local sensitive information. Specifically, during swarm learning, every **MC**s' local training data cannot be leaked to other **MC**s.
* **Robustness**: Detecting malicious **MC**s and discarding their local gradients during swarm learning. Specifically, a mechanism is constructed to identify malicious medical centers and discard their submitted local model gradients.
* **Accuracy**: While protecting data privacy and resisting malicious attacks, the model accuracy of PBSL training should be the same as that of the original SL training.  
Figure 2: System model of PBSL.  
* **Efficiency**: Reducing the high computational and communication cost of encryption operations in a privacy-preserving SL scheme.
* **Tolerance to MC drop-out**: Guaranteeing the privacy and convergence even if up to \(D\)** MCs** are delayed or dropped.
## 6 Conclusions and Future Work  
A method to reduce overheads associated with redundancy in the design of radiation-induced fault-tolerant systems with SIHFT techniques was presented. The proposed method combines AC techniques at the software level with selective hardening strategies. This combination compensates for overheads derived from hardening techniques and increases the spectrum in the design space. The proposal was validated through a case study, using the TI MSP430 microcontroller and four programs with concept test. Results showed that, with the proposed method, the overheads associated with traditional SIHFT techniques were reduced while increasing the reliability of the system. It was also observed that the error in the result, as well as the reduction of overheads, depends on the program to be hardened. Through selective hardening tests, it was shown that in some cases protecting a single register is better, in terms of reliability, than performing a complete hardening.  
As future work, it is intended to further validate the proposal through complementary case studies by applying it to other architectures (specifically ARM and RISC-V) and to use more complex test programs to implement selective and total hardening. It is also planned to validate the method through radiation tests. In addition, we plan to explore possible approximated hardening strategies that involve AC techniques combined with fault tolerance approaches in a non-isolated way to optimize results.  
Conceptualization, A.A.-M., F.R.-C. and C.P.; investigation, A.A.-M., F.R.-C. and C.P.; methodology, A.A.-M., F.R.-C. and C.P.; data curation, A.A.-M., F.R.-C. and C.P.; project administration, F.R.-C.; supervision, F.R.-C.; validation, A.A.-M.; original draft, A.A.-M.; review and editing, F.R.-C. and C.P.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Related Works
As detailed in Table 1, the image-based supervised classification methods have demonstrated considerable effectiveness, achieving performance metrics such as an IoU exceeding 80% and a max mAP of 76.4%. Despite their promising results, these image-based methods suffer from several fundamental limitations. They primarily rely on intensity features, which overlooks the critical geometric information, especially at regions with distinct geometric deviations like circumferential joints and bolt holes where leakage commonly occurs. Furthermore, as summarized in Table 1, these methods are (1) sensitive to lighting conditions and object occlusion, (2) suffer from a loss of spatial information during 2D projection, and (3) fail to visualize leakage patterns effectively in 3D space. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
Additionally, the intensity threshold-based segmentation approaches are also constrained by significant drawbacks. As noted in Table 1, they are (1) sensitive to lighting conditions and object occlusion, (2) highly sensitive to the selection of a varying threshold, and (3) likewise incapable of visualizing leakage in 3D space. These collective limitations highlight the urgent need for a methodology capable of directly detecting leakage from a fully 3D perspective while integrating robust geometric features.
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 4 Optimization Problem of the Proposed System
### Mathematical Model of the Constraints
#### 4.2.2 The Generators Operating Constraints  
* Capacity Constraints \[P_{min,i}^{t}\leq P_{i}^{t}\leq P_{max,i}^{t}\] (37) where \(P_{min,i}^{t}\) and \(P_{max,i}^{t}\) are the minimum and maximum delivered power of the \(i\)th DG.
* Ramp Rate Constraint  
This constraint should be satisfied at each operation period to protect the generators from damage. The following equation is utilized to integrate this constraint with the optimization problem:  
\[-DR_{i}\cdot\Delta T\leq P_{i}^{t+1}-P_{i}^{t}\leq UR_{i}\cdot\Delta T \tag{38}\]where \(DR_{i}\) and \(UR_{i}\) are the ramp-up/down limit of the \(i\)th DG.
# Federated Distillation Methodology for Label-Based Group Structures
## 2 Related Works  
### Clustering in Federated Learning  
In FL, several clustering criteria are available for categorizing and grouping clients based on various attributes. One example is Data Source-Based Clustering [9], which organizes clients according to the origin of their data, such as X-rays or ultrasound, in medical settings. Geographical and Diagnosis-Based Clustering [10] groups clients based on their locations or shared diagnoses. Loss Function Values-Based Clustering [8; 11] focuses on similar model behaviors as deduced from the loss function values. Clusters based on inherent data distribution seek to enhance model generalization by considering the intrinsic characteristics of the data. Model Parameter-Based Clustering [12; 13] gathers clients with analogous model parameters, reflecting parallel learning stages. Gradient Information-Based Clustering [7; 14] forms clusters by examining shared gradient information. Prototype-Based Clustering [15] simplifies the global model by forming clusters around generalized prototypes that represent distinct data patterns.  
Table 1 shows the commonly utilized clustering methods in federated learning, clustered FL (CFL) and FL+HC utilize the similarity of model parameters for clustering. However, the computation required to find the similarity of model parameters between clients  
is proportional to the model size. Furthermore, as the number of clients increases, the computation grows exponentially. On the other hand, our method utilizes the number of labels predicted by each client, which is not affected by model complexity or the number of clients. FedSEM and IFCA find the cluster identity by sending multiple model parameters to each client and selecting the model with the lower loss. This method is inefficient because each client has to repeat the learning process using multiple models. On the other hand, our method only adds clustering and obtaining the cluster logit at the end of the existing federated distillation process, so the overhead is small.
## 3 One-Dimensional Convolution Neural Network (IDCNN)  
CNN is a typical deep learning model that extracts the characteristic information of the signal layer by layer through convolution, activation, and pooling [37]. At present, from the different points of view of the input signal, it can be divided into two types: 1 DCNN and 2 DCNN. Among them, the input of 2 DCNN is two-dimensional image data, and in practical engineering, the signal collected by the sensor is a one-dimensional time signal, which needs to be converted into a two-dimensional signal by certain methods, such as short-time Fourier transform (STFT) [22], wavelet transform (WT) [38], and so on. In this kind of conversion process, there is no guarantee to avoid the risk of distortion and even the loss of useful information, and the network layer of 2 DCNN is relatively complex, which may lead to insufficient feature learning and reduce the accuracy of network model training and learning. Therefore, the direct use of a one-dimensional signal as input can ensure that all the characteristic information of the original signal can be included and the above problems can be avoided. 1 DCNN is a forward propagation artificial neural network, which updates the network parameters through the back propagation algorithm. Compared with 2 DCNN, its network structure is simpler and compact, and it can effectively classify and train with limited data.  
CNN usually consists of two parts: feature extraction and classification. Feature extraction is a compound structure, which is composed of a convolution layer, activation layer, and pooling layer, which is used to map the original signal or two-dimensional imagedata to the feature space to represent various fault features. It is an important part of CNN. The full connection layer and classifier mainly integrate and classify the extracted features. The flow chart of 1 DCNN is shown in Figure 2.
# A Visitor Assistance System Based on LoRa for Nature Forest Parks
## 5 The Proposed Visitor Assistance System
### Optimization  
The location of totems is relevant to achieving the best possible performance in locating visitors. There are higher-risk locations that require greater connectivity and therefore better coverage. It is vitally important to maximize wireless network coverage, especially addressing risk points while maintaining minimal infrastructure. This reduces the cost of the visitor assistance system and also any environmental impacts that may be caused to fauna and flora.  
The positioning of totems is treated as an optimization problem, which can be solved using Integer Linear Programming (ILP) [32]. For this, a matrix modeling of cubes that map the track in three-dimensional form is developed. This modeling considers the signal coverage on the trails, the attenuation caused by the height difference between the totem antennae and the antenna characteristics. Monopole antennas were assumed, of which an irradiation diagram shows differencesin the azimuth plane and elevation plane [33]. To solve the proposed model, real parameters obtained from practical experiments were used. The cubes that represent sites of the grid where it is feasible to deploy a totem are delivered to CPLEX to solve the ILP problem.  
Our use-case trail is the crossing from Petropolis to Teresopolis, made through the mountains of Serra dos Orgaos National Park (PARNASO). This route measures about 30 km and is covered in a three-day trek. To this trail, considering full track coverage, 200 LoRa technology totems are required. However, due to the cost-to-implement, maintaining full coverage is unfeasible. An initial survey of the trail makes it possible to identify danger points, water collection, trail intersection, and shelter. Thus, it is possible to estimate the use of 23 totems.
# Parasitic Parameters Extraction of High-Speed Vertical-Cavity Surface-Emitting Lasers
## 2 Materials and Methods  
### Fabrication and Performance of the Six-Layer Oxide Aperture VCSEL  
There are many ways to improve the bandwidth of the device, and we focused mainly on the improvement of external parasitics to obtain the maximum small-signal modulation bandwidth. The epitaxial structure was grown by MOCVD, which consists of four secondary oxide layers above the upper primary oxide aperture, to reduce the capacitance. Four InGaAs quantum wells with five AlGaAs barriers were used as the active medium in the \(\lambda/2\) cavity VCSEL. The distributed Bragg reflectors (DBRs) were optimized with graded interfaces [5]. The n-doped bottom-DBR comprised Al\({}_{0.88}\)Ga\({}_{0.12}\)As/Al\({}_{0.12}\)Ga\({}_{0.88}\)As layers and AlAs/Al\({}_{0.12}\)Ga\({}_{0.88}\)As layers; AlAs layers can improve the dissipation of heat due to their high thermal conductivity [4]. For device fabrication, the wafer was processed into the first cylindrical mesas of 12-18 \(\upmu\)m in diameter and etched down to the active region in SiCl\({}_{4}\)/Ar plasma by using an inductively coupled plasma etcher. Then, the second cylindrical mesas of 27-33 \(\upmu\)m in diameter were etched down to the n-GaAs contact layer. The oxidation aperture was formed by wet oxidation with a diameter of 3-9 \(\upmu\)m. Benzocyclobutene (BCB) was coated on the device. Figure 1a shows the top view of the VCSEL with ground-signal-ground (GSG) Ti/Au probe pads, and Figure 1b shows the scanning electron microscope image of the cross-section of the VCSEL.  
Figure 2a shows the measured L-I-V curves and the lasing spectra of the fabricated VCSEL. The threshold current is 0.6 mA for a 3 \(\upmu\)m oxide aperture, and the differential resistance is 70 \(\Omega\) at 8 mA. The slope efficiency is approximately 0.45 W/A, and the maximum emission power exceeded 4 mW. Figure 2b shows the lasing spectra, which show that the device operated at a multimode of approximately 850 nm.  
Figure 3a shows the small-signal modulation response of the VCSEL measured at 25 \({}^{\circ}\)C by a vector network analyser N5247B, and Figure 3b shows the resonance frequency and \(-3\) dB bandwidth plotted against the square root of the bias current above the threshold for the analysis of the D-factor and modulation current efficiency factor (MCEF). The D-factor reflects the rate of increase of the relaxation frequency with current and determines the  
Figure 1: (**a**) Top view of the fabricated VCSEL with GSG probe pads, and (**b**) SEM image of the cross-section of the device after wet oxidation. The dark lines are the oxidized high-Al-composition layers.  
modulation speed of an ideal laser [19]. It can be seen that the bandwidth reached 23.3 GHz at 12 mA, the MCEF is 9.04 GHz/mA\({}^{1/2}\), and the D-factor is 6.38 GHz/mA\({}^{1/2}\).
## 2 Materials and Methods  
### CPs Containing NORMs  
In this study, 225 raw data containing specific ACs of various CPs containing NORMs were obtained from previous literature and the database of Nuclear Safety and Security Commission (NSSC) in South Korea. The data were grouped into two categories on the basis of combustible and noncombustible CPs containing NORMs. To take care of the outliers in data representation, we further them grouped in two categories according to the range of AC that is below and above 1 Bq/g. The mean specific activity for various product concentration ranges was obtained and statistically analyzed using an interval plot in Minitab for \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K with a 95% confidence interval for the mean of each product for different concentration ranges. An assumption was made that, after the incineration of CPs containing NORMs, ash residues are disposed of in landfills, which contributes to a slight increase in the AC of NORM-added CPs at landfill sites. Therefore, the total specific ACs of combustible and noncombustible CPs containing NORMs were used as input data to RESRAD codes in Bq/g to determine the exposure dose to onsite and offsite residents at a landfill disposal facility. The 95% confidence interval for the mean specific ACs of all radionuclides in question is summarized in Figure 1.  
Figure 1: 95% confidence interval for the mean activity concentration of \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K (Bq/g).