esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this article, we report a facile method to fabricate free-standing reduced graphene oxide paper (rGOP) by vacuum filtration. Pt nanoparticles were electrodeposited on reduced graphene oxide paper to construct a sensitive and flexible hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) sensor. The properties of Pt/rGOP were characterized by scanning electron microscopy, energy dispersive X-ray, X-ray photoelectron spectroscopy and Raman spectroscopy. The electrochemical characterizations of the resulting sensor were carried out by cyclic voltammetry, chronoamperometry and electrochemical impedance spectroscopy. The Pt/rGOP hybrid electrode had an excellent electrochemical property, with superb electrocatalytic activity, a large electrochemical active surface area, flexibility and high stability. The linear ranges of the as-prepared sensor for H\({}_{2}\)O\({}_{2}\) detection were divided into two linear sections: 0.2 umol/L to 2.0 mmol/L and 2.0 to 8.5 mmol/L, with a detection limit of 100 nmol/L (S/N = 3) and a response time of less than 5 s. The proposed sensor has great potential to become a reliable and flexible tool in biosensor and point-of-care medical devices.  
reduced graphene oxide paper; hydrogen peroxide; electrodeposition; Pt nanoparticles; biosensor +
Footnote †: journal: Applied Sciences  
Article
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 5 Literature Review
### Power Distribution Network
#### 5.2.6 Resilience Strategies  
Table 3 summarizes the different implemented measures to enhance PDN resilience. Infrastructure hardening, energy storage, and distributed generation resources are intensively explored owing to their wide deployment and availability. In addition, both distribution automation and network reconfiguration (which can be manual or automatic)contribute to enhancing the robustness and adaptability of the network, and enable very efficient recovery. It can be seen thereby that all works from Table 2 that handle recovery either in pre-event, or event real-time, implement one or both of these two strategies. Contribution in [128] develops a set of probabilistic metrics that capture features and a detailed process of automatically locating, isolating faults, and restoring the service to customers in distribution systems. More precisely, the proposed algorithm devises a switching sequence and calculates load interruption when dealing with a large number of switches in large-scale distribution networks. Despite promising results to boost resilience, attention should be paid to the level of automation to be introduced in the network, because it can produce the inverse effect in rare events [146].  
Various smart grid functions of improved safety, self-healing, high DER penetration, and active load control can be enhanced using microgrids [147]. Microgrids (MGs) are in some cases operated in parallel with the main distribution grid, where the possibility to have their separate resilience analysis [30; 148; 149], meaning that MGs can be taken as a testbed to illustrate the applicability of the proposed resilience quantification [124; 136; 137]. In another approach, MGs are adopted as a resilience strategy that can be enabled in case of a disaster through islanding technique [150; 151], thus the need to schedule the formation of MGs and associated DER dispatch and remote switches operation [133; 138]. Further resilience benefit is achieved when multiple MGs are interconnected, given a better situational awareness conveyed between networked grids and eventually sharing of distributed resources [30; 127]. Contributions in [124; 136; 137; 138; 133; 136; 134; 137; 138] are only a small part of the increased interest in MGs for distribution grid resilience enhancement [65]. In a general sense, resilience strategies are in some cases adapted only to certain disruption, and can be even a shortcoming during different
## 5 Instance Verification
### Verification of GRU-HMM Fault Prediction Model  
The HMM initial vector, state transfer matrix, and confusion matrix are first calculated from the training set with the following values:  
\[\pi=[0.782,0.115,0.06,0.025,0.018];\]  
\[A=\begin{bmatrix}0.9530&0&0.0118&0.0352\\ 0&0.9855&0.0145&0\\ 0.0097&0.0179&0.9470&0.0255\\ 0.0822&0&0.0254&0.8925\\ \end{bmatrix};\]  
\[B=\left\{\begin{bmatrix}18.5143&0.1184\\ 0.1184&0.0508\\ 526.172&1.82\\ 1.82&0.0967\\ \end{bmatrix}&\begin{bmatrix}654.462&4.5942\\ 4.5942&0.0551\\ 569.049&-8.9019\\ -8.9019&0.4765\\ \end{bmatrix}\right\}\]  
The hybrid feature domain is substituted into the GRU for feature fusion, and the initial vectors and state transfer matrices are updated. The log-likelihood probabilities of the initial vector updates for each hidden fault type are shown in Figure 13. Where the log-likelihood probability of the driving gear increases at increasing speed levels, especially at high speeds, the risk of failure of the driving gear increases. The log-likelihood probability of the universal joint has the highest risk of failure at moderate speeds. The log-likelihood probability of the universal joint has the highest risk of failure at moderate speeds. The log-likelihood probability of the driven gear is higher at lower speeds, decreases with increasing speed, and then increases again at higher speeds, indicating that the risk of failure of the driven gear is higher at low- and high-speed operations.  
Figure 13: Initial vector log-likelihood probabilities for each hidden fault type.  
The updated initial vectors and state transfer matrices are substituted into the GRU-HMM for training. The probability distribution of hidden states is calculated using the test set, and the results are normalized to obtain the fault prediction results for each type of failure mode. The results of the velocity-hidden state distribution for one of the sample sets of the test set are shown in Figure 14. The probability of hidden failures of the driving gear and driven gears is higher from the figure. Through the maintenance records of the EMU undergoing advanced repairs in the manufacturing plant, comparing the replaced gear sets, it is proven that the predicted results match the actual results, and the feasibility of the GRU-HMM fault prediction model is demonstrated.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 2 Frequency-Reconfigurable Antennas  
Frequency reconfigurable antennas have attracted researcher and industry communities' attention due to its capability to reduce the size of the front end systems and minimize interference with other wireless systems and maximize throughput [1]. The resonance of a proposed flexible antenna can be switched through the change of the effective length of radiator and the modification in the ground plane and feeding networks with the help of an electrical switch [17, 18, 19, 20, 21, 22, 23, 24, 25], by microfluidic-based antenna [26, 27] and the use of a conductive fluid as a switch for a main radiating element [27], changing the antenna's permittivity [28, 29] or the antenna's shape or type [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53].  
Figure 1: Categorization of reconfiguration techniques for all types of antennas.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 4 Discussion
### Prediction of Urban Growth  
The prediction results for the year 2050 (Figure 5 and Table 6) show that there will be LULC changes in the study area that are mainly due to urbanization. There will be a reduction in the area covered by agricultural fields, bare soil, grassland, water bodies and woody vegetation (Table 6). There will be an increase in the area covered by built-upstructures due to the current continuation of urban dwellers and projected population growth in Maseru. This is supported by [PERSON] [64], who states that Maseru will continue to develop and build new infrastructure to cater to the rising population and migrants from rural communities and South Africa. This projection shows that the population of Maseru is expected to rise from 519,186 in 2016 to about 716,773 in 2036 [63]. This is consistent with the United Nations [2], which noted that urbanization in Lesotho has risen, and the urban population is expected to rise from 39% by 2025 to 58% by 2050. Other LULC classes are expected to decrease to accommodate this tremendous growth of areas covered by built-up structures.
## 5 Experiments
### A TensorFlow Lite Model for Object Detection
#### 5.2.1 Deployment Results of Scenario I  
The suggested object detection model for Scenario I (SSD Mobilenet v1 0.25) is tested on the STM32H7 series microcontroller with both the validation and test data of the COCO Person Dataset. The graphs provided in Figure 5 illustrate the precision-recall (PR) curves for the \"person\" class on the validation and test data.  
According to the first PR curve, the model achieves an average precision (AP) of 28.32 on validation data, which indicates a slightly low performance in detecting the target class. Precision begins at 1.0 for low recall values, meaning that the model is highly accurate when detecting a small number of high-confidence instances. However, as recall increases and more instances are detected, precision gradually declines, falling below 0.94 at a recall value of 0.30. This indicates that the model struggles to maintain accuracy as it detects more objects, likely due to false positives or difficulties in distinguishing objects in more complex scenes. Such behavior is typical of lightweight models like SSD MobileNet deployed on resource-constrained devices like the STM32H747I-DISCO board. Lightweight models are specifically designed to prioritize efficiency, low power consumption, and compact size, which are critical for embedded systems with limited computational resources. However, these benefits come at the cost of reduced capacity for complex feature extraction and lower robustness in challenging detection scenarios. The declining precision at higher recall levels underscores the model's inability to maintain high confidence in its predictions as it attempts to detect a greater number of objects. This suggests that while the model performs well in identifying easily distinguishable objects, it struggles with overlapping objects, cluttered scenes, or small objects, which require more sophisticated feature representations and higher computational capacity.  
The second PR curve represents the model's performance on the test dataset, showing a slightly lower AP of 27.29 compared to the validation dataset. As with the validation data, precision starts at 1.0 for low recall values and decreases steadily, dropping below 0.92 at a recall value of 0.30. This indicates a similar pattern of performance, with the model excelling at detecting high-confidence instances but struggling to maintain precision as it attempts to detect a greater number of objects. The slightly lower AP on the test data reflects a minor decrease in generalization performance when evaluated on unseen data. The model's consistent behavior across both datasets suggests that it is robust within  
Figure 5: Validation and test results for the pre-trained ST SSD MobileNet v1 0.25 model.  
the scope of its design but remains constrained by the trade-offs inherent to lightweight, low-power hardware. These results reinforce the suitability of this configuration for tasks with limited computational demands, such as detecting a few large or stationary objects in controlled environments.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Network Fault Management  
In network management, fault management is to detect, predict, and eliminate malfunctions in the communication network. The integration of newly emerging technologies and paradigms in 6G networks makes the network more complex, heterogeneous, and dynamic. Consequently, fault management becomes more challenging in 6G networks. ML/DL approaches have been studied recently for efficient fault management. [PERSON], [PERSON], and [PERSON] [60] proposed a Naive Bayes and CNN-based algorithm for fault detection over a wireless sensor network in a distributed manner. The results show that the proposed approach accurately detects faults and is energy efficient. Regarding fault diagnosis, [61] implemented an FNN for fault detection and classification in wireless sensor networks. The DL model was tuned using a hybrid gravitational search and particle swarm optimization algorithm. [PERSON] et al. [62] studied the feasibility of ML and DL approaches for fault prediction on a cellular network. The results showed that a DNN with an autoencoder (AE) predicts the network fault with the highest accuracy compared to autoregressive neural networks and the SVM.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
###### Abstract  
Ultra-reliable low-latency communication (URLLC) is an important application scenario in fifth generation (5G) communication. With the increasing deployment of 5G, spectrum resources are becoming increasingly scarce, and millimeter wave (mmWave) operating in a high frequency has garnered significant attention. However, the short wavelength of the mmWave makes the signals susceptible to fading. Precoding has emerged as a promising solution for mitigating the severe path loss in mmWave, which can enhance the system capacity and communication performance. In our system, an achievable rate maximization problem is established by jointly optimizing the analog and digital precoders at the base station, subject to the constraint of the total power and the constant modulus constraint of the subarrays. The simulation results demonstrate that the algorithm employed in our system can successfully solve the optimization problem in this new scenario. Furthermore, the subarray-structure hybrid precoder has a higher energy efficiency than that of a fully connected hybrid precoder or all-digital precoder. The proposed algorithm also outperforms the MM (Majorization-Minimization) algorithm.  
ultra-reliable and low-latency communication (URLLC); hybrid precoding; millimeter wave; spectral efficiency
## 5 Experiment and Analysis
### Experimental Results
The voltage across the reference resistor and the sensor output voltage, \(V_{DAC}\), are simultaneously measured with two Keithley 2110 digital multimeters, each with a 5.5 digit resolution and a basic DC voltage accuracy of 0.012%. Using a high-precision reference resistor and digital multimeters ensures accurate measurements. The calculated input currents and corresponding sensor output voltages are presented in Tables 3 and 4. The experimental results indicate that the non-linearity of the developed closed-loop fluxgate sensor is less than 0.1% in the measuring ranges of 0-1 A and 0-10 A.  
Figure 8: Closed-loop control graph of current sensor: phase 1: self-learning phase, and phase 2: measurement phase. The voltage \(V_{DAC}\) value can be output as a digital signal via the UART communication protocol. In the experiments, closed-loop control algorithms are optimized to maximize the efficiency of PID control, thus increasing the data output rate of the sensors. The experimental results show that the maximum data output rate of the sensor is limited to about 100 samples per second, i.e., the minimal response time that the sensor can achieve is about 10 ms.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 4 Analysis of the Feature Compatibility Problem
### API Server Configuration  
Various plug-in components and configuration parameters can be set via options of the kube-apiserver command that starts up the API server [36]. These plug-in components and configuration parameters of the API server can be mapped to the following K8s features that are clearly defined as part of the open-source documentation [5; 35]:  
1. Admission controllers. These are modular interceptors that wrap the API server. Although these interceptors are defined as part of the open-source distribution of K8s, we have found in our previous work' [35] that 7 out of 28 admission controllers were not consistently set across the studied vendors for K8s version v1.13;
2. The RESTful APIs. The RESTful APIs of the API server are organized into different API groups that can be enabled or disabled. For example, for K8s v1.13, we found that EKS does not support the k8s.metrics.io API group that is needed for auto-scaling of containers. Another problem are the deprecated APIs that are still highly demanded. For example, the PodPreset API has been removed after K8s version v1.19. Hosted K8s products only offer the latest versions; therefore, the PodPreset API is not available anymore, yet there is still a high demand for this feature in the OpenShift community [9];
3. Feature gates for beta features. Each K8s version introduces new alpha features that can and should be disabled via feature gates when running clusters in production environments. These alpha features may disappear or be promoted to the stable stage in a successive K8s version, after which the feature gate is removed. However, between the alpha and stable stage, there is the beta stage and beta features are enabled by default in the open-source distribution. However, they can be disabled by K8s vendors. In the latest version of K8s, there are more than 40 beta features. In our previous work [35], we found differences between the three K8s vendors with respect to 2 beta feature gates. Unfortunately, also different alpha features were enabled;
4. Encryption of secrets stored in etcd is a feature to prevent attackers to read secrets from the etcd database in the clear.  
Note that since the control plane and its components are completely hidden in hosted K8s products, it is not possible to access the configuration of the API server in anyway. The only tactic that works is the construction of a replica API and replica admission controller by using a CRD and a dynamic admission web hook that intercepts requests for creation,update and deletion of CRD objects. This tactic is explained in Section 5.1 and is illustrated by means of the PodPreset API.  
Moreover, when the control plane is hidden by a K8s vendor, etcd encryption nor feature gates concerning the behavior of the control plane itself can be reconfigured in a vendor-agnostic manner. For those feature gates that concern the Kubelet on worker agents, the KubeletConfiguration manifest on all worker nodes can be changed as is described in the next section.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 3 Proposed Scheme  
Upcoming generation communications demand much higher mobility requirements along with efficiency in resource utilization in a massive IoT environment [30; 31]. To achieve the mobility requirements in IoT, the main purpose of the presented research is to provide a resource-efficient location-based PMIPv6 extension protocol for enhanced mobility in 6G-enabled IoT. The main contributing aspects of the proposed extension include integrating RSS and the location of network entities for accurate prediction of handover initiation, optimizing the signaling efficiency, reducing handover latency, and enhancing buffering efficiency in addition to the existing RSS-based PMIPv6 protocol extensions. For signaling efficiency, RSS is integrated with location information in an effective way for avoiding unnecessary communication of networking entities. Buffering efficiency is achieved using the LMA and nMAG in an optimized way for storing the packets. Handover latency is optimized by reducing by involving MN, LMA, and nMAG-ID in the handover process. To understand the basic working of the presented protocol extension, the following are the details of the same.
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 2 Model
### Curve Correction Model
#### 2.5.3 Curve-Correction Calculation Based on Ray-Tracing Simulation
Such non-uniformity is observed by the measurement of the mini-module attached to various places on the car roof [33]. To avoid complexity, the validation by the measurement in this article does not consider the non-uniformity and the resultant mismatching loss in the module, but only considers the total flux absorbed by the module. The mismatching loss is handled separately by a Monte Carlo simulation, but it is not validated in this article. Although the mismatching loss due to non-uniformity may be expected, it will be significantly relaxed by the appropriate string design [24], and thus less significant than the reduction of the total flux. The curve correction factor is also affected by the angular distribution of the solar irradiation. Roughly, the angular distribution of the sunlight in the horizontal surface moves to the higher incident angle when the sun height is low, thus higher latitude, lower average height of the shading objects, and seasonal fluctuation of the sunshine duration are high in winter, like the area of the south-east side of mountains in Asian monsoon regions. The curve correction factors, \(f\) and \(f_{2}\), significantly vary by the site conditions, including latitude, climate pattern, and shading environment (urban zone or rural zone). This is because of the difference in the distribution of the incident angle onto the curved surface (Figure 10).
###### Abstract
For EEG analysis, CNNs exhibit an inherent ability to differentiate subtle spatial and temporal relationships within time-frequency spectrum (TFS) images. This capability aligns well with the intricate variations in neural activity, characteristic of different epileptic states [18]. Furthermore, CNNs inherently possess translation invariance, allowing them to detect patterns regardless of their location within TFS images [18; 19]. While other methods have demonstrated efficacy in various machine learning applications, the unique architecture and feature extraction capabilities of CNNs were deemed highly advantageous for deciphering the intricate patterns present in EEG data, ensuring a more nuanced and accurate classification of healthy and epilepsy states [20]. In this work, we propose using variable-frequency complex demodulation (VFCDM), a high-resolution, time-frequency domain method of analyzing various biomedical signals [21], to prepare 2D TFS of EEG signals for input into a deep convolution neural network for automated detection of epileptic traits. VFCDM can be summarized as a two-step procedure. First, complex demodulation is used to obtain an estimate of the time-frequency spectrum from which the dominant frequencies of interest are selected to be further refined. The second step involves passing the signal into a series of band-limited signals using a collection of low-pass filters (LPFs) to isolate the dominating frequencies. A Hilbert transform is applied to obtain an analytic signal for each band, providing a time-frequency spectrum containing simultaneous estimates of amplitude, frequency, and phase. To the best of our knowledge, no similar work has been performed with VFCDM spectrum-based inputs with convolutional neural networks.