esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art  
### The Maintenance Concept  
Maintenance is an essential factor for the sustainability of the asset's operating functions and, by consequence, its availability and reliability. Maintenance is also a way to mitigate the damages that will occur in assets; therefore, the people in charge must be competent in their professional fields. This paper is based on existing norms and relevant research papers relating to maintenance aiming to support new ideas that may be relevant for further improvement, namely based on the following quotations. The American Hospital Association (1980) mentions that \"proper maintenance of the power system is essential to its safety and reliability. The designer may incorporate certain features into the system to make maintenance safer and more comfortable and to make it possible to perform routine maintenance and inspection without dropping essential hospital load\" [1]. [PERSON] and [PERSON] (1990) reported that \"support deals with the specific procedures, tasks, instructions, personnel qualification, equipment needed to satisfy the system maintainability requirement within an actual environment use\" [2]. According to the Department of the Army, maintenance is defined as \"those operations and actions that directly retain the appropriate activity of an item or renewing that operation when it is disturbed by failure or some other anomaly--within the context of RCM, the necessary process of an object means that it can perform its intended function\" [3]. [PERSON] (2011) also referred to the norm EN 13306:2010 that defines maintenance as the \"combination of all technical, administrative and managerial actions during the life cycle of an item intended to retain it in or restore it to, a state in which it can perform the required function\" [4]. [PERSON] (2009) stated that \"Maintenance is concerned with keeping an asset in right working conditions, so that the asset may be used to its full productive capacity. The maintenance function includes both upkeep and repairs\" [5]. [PERSON] (1997) stated that \"the role of Maintenance is to ensure that physical assets continue to do what their users want to do\" [6]. [PERSON] (2012) said that \"Maintenance is a function that operates in parallel to production and can have a significant impact on the capacity of the production and quality of the products produced, and therefore, it deserves continuous improvement\" [7]. It can be considered that maintenance is a management tool to prevent failures in the physical assets, using both planned and non-planned interventions to maintain their useful lives, in charge of the maintenance engineers.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 3 Results and Discussion
### Oxidizing Gas Sensors  
Figure 7 presents the average concentrations and standard deviations of the low-cost sensors relative to the Personal Ozone Monitor during the ozone decay experiment. Average concentration levels and the standard deviation (\(y\)-axis error bars) were calculated for all sensors. The average slope, intercept, and \(R^{2}\) were 1.1, 5.4 ppm and 0.98, respectively. The decay experiment was exponential, so there was more variability at high concentrations than lower concentrations. The variability in sensor response reflects the variability in ozone concentration during the measurement period. In addition, each sensor has a unique response compared to the average slope applied to the sensors for this work. For the NO\({}_{2}\) span gas experiment, the bias values varied from \(-39\%\) to \(-11\%\), and the precision for all monitors was \(11\%\).  
Figure 6: Decay test results of the average CO concentration for all sensors relative to the Q-Trak Plus 8552 reference measurements. The y-axis error bars represent one standard deviation.  
Figure 7: Decay test results for the average O\({}_{3}\) concentration for all sensors relative to the reference measurements from the Personal Ozone Monitor (PO\({}_{3}\)M). The error bars represent one standard deviation.  
The sensors were highly correlated with the reference instrument throughout the measurement range of interest of 20-180 ppb. The bias variability and lower precision of these sensors compared to the CO sensors is probably a consequence of using one calibration equation for all sensors when in reality there is substantial variability in response among the oxidative gas sensors.
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
## 2 Model Description and Problem Statement
### Attitude Model of a Quadrotor
Let \(m\) denote the total mass of the quadrotor, and \(l\) denote the approximate distance between the motor and the center of mass, both of which can be practically measured. Similar to the literature [23], we assume that the mass of each motor is \(\frac{m}{\overline{d}}\) and treat the four motors as point masses, then the inertia matrix of this simplified physical model is \(\mathrm{diag}\left(\frac{m|^{2}}{2},\frac{m|^{2}}{2},ml^{2}\right)\). This simplified physical model has a larger moment of inertia along each axis than a quadrotor with the same size. Thus, we make the following assumption. **Assumption 3**. : _Inertia matrix \(\mathbf{J}\) is unknown, but the upper bound of its 2-norm can be estimated by the mass and size of the quadrotor; i.e., we can select \(\overline{J}=ml^{2}>0\) such that \(\|\mathbf{J}\|\leq\overline{J}\)._
# PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network
###### Abstract  
Air is the environmental foundation for human life and production, and its composition changes are closely related to human activities. Sulfur dioxide (SO\({}_{2}\)) is one of the main atmospheric pollutants, mainly derived from the combustion of fossil fuels. But SO\({}_{2}\) is a trace gas in the atmosphere, and its concentration may be less than one part per billion (ppb). This paper is based on the principle of photoluminescence and uses a photomultiplier tube (PMT) as a photoelectric converter to develop a device for real-time detection of SO\({}_{2}\) concentration in the atmosphere. This paper focuses on the impact of noise interference on weak electrical signals and uses wavelet transform to denoise the signals. At the same time, considering that the photoelectric system is susceptible to temperature changes, a multi parameter fitting model is constructed, and a BP neural network is used to further process the signal, separating the real data from the original data. Finally, a high-precision and wide-range trace level sulfur dioxide concentration detection device and algorithm were obtained.  
BP neural network; data fusion; temperature drift; weak current signal detection; wavelet transform +
Footnote †: journal: Applied Sciences
# Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences
## 4 Discussion
### Is Sum-of-Pair Score or Its Derivative a Good Criterion for Choosing the Best MSA?  
The best MSA should produce the true tree, especially when the objective of sequence alignment is accurate phylogenetic reconstruction. However, phylogenetic accuracy cannot be used directly as a criterion because the true tree is unknown, except in simulated sequences. One would hope that the sum-of-pair score (SPS) or its variations, such as weighted SPS, which is computationally practical and widely used as a criterion for choosing the best MSA, would be equivalent to the criterion of phylogenetic accuracy. In other words, the MSA with the highest SPS is also the MSA that would result in the most accurate phylogeny. Our results (Figures 5 and 6) suggest that this is not the case. From each set of our simulated sequences, two MSAs were produced, one with the true tree as the guide tree (trueTree) and the other using the guide tree from the L-INS-i approach (L-INS-i). When these two MSAs were fed to PhyML for phylogenetic reconstruction, the MSA from the trueTree approach produced trees more similar to the true tree than that from the L-INS-i approach. However, the latter has significantly higher SPS than the former. Thus, the two criteria are inconsistent.  
It is difficult to provide a specification of time complexity with the Carrillo-Lipman algorithm. This algorithm for the three-way alignment includes three pair-wise alignments, followed by a simplified three-way alignment that does not need to visit all cells in the cube. The time complexity for this last step is difficult to express because the time required for this step depends on the nature of the three sequences. If the three sequences are nearly identical, then we have the best scenario, and the time required for this step would be almost linear. If the three sequences are highly diverged and differ much in length (i.e., many indel events), then the time requirement for this step would be similar to the plain three-way alignment using dynamic programming. Because we aim to improve the sequence alignment of highly diverged sequences, the time saved with the Carrillo-Lipman algorithm is not substantial.  
One time-saving protocol is to first identify regions of consistency among the three pairwise alignments in each three-way alignment using the approach proposed by [PERSON] [44]. The regions of consistency do not need three-way alignment. They can serve as anchors so that one only needs to do three-way alignment for sequence segments between such anchors.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 4 Numerical Simulation, Experimental Results and Discussions
### Numerical Simulation and Discussions
#### 4.2.2 Scenario 2: Tracking a Moving Altitude Reference  
Here, the vehicle tracks a moving altitude reference using the same controller gains as in Scenario 1. The moving altitude reference is given as a sine function of time \(t\) as:  
\[z_{d}=10\sin(0.1t) \tag{11}\]  
Clearly, the altitude performance of our new algorithm is superior to a conventional PID controller (Figure 12). While the moving reference altitude reaches 10 m altitude at time \(t\approx 15.7\) s, our controller reaches the peak about 3.8 s later (at \(t\approx 19.5\) s), while the conventional PID controller lags by 9.7 m, reaching the maximum altitude at \(t\approx 21.1\) s. Due to this significant delay, the conventional PID does not even reach the full altitude. In addition, the tracking error of the PID controller exhibits large amplitude oscillation and a slow response compared to our algorithm (Figure 13).  
Figure 11: Comparison of the control signal, \(U_{1}\), between our new controller and a conventional PID controller for Scenario 1.  
Figure 10: Comparison of the vertical acceleration performance between our new controller and a conventional PID controller for Scenario 1.
# Machine-Learning-Based Path Loss Prediction
## 2 Theory
### Machine-Learning-Based Predictive Modeling
#### 2.2.2 Random Forest (RF)  
Random Forest (RF) is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees [45]. Random Forest is a classifier consisting of a collection of tree-structured classifiers \(\{h_{k}(x,\Theta_{k}),k=1,\ldots\}\) where the \(\{\Theta_{k}\}\) values are independent identically distributed random vectors and each tree casts a unit vote for the most popular class at input \(x\). The decision function for an RF can be represented as  
\[H(x)=\arg\max_{Y}\sum_{k=1}^{K}I(h_{k}(x)=Y) \tag{7}\]  
where \(H(x)\) shows the combined classification model, \(h_{k}(x)\) is the individual decision tree model, \(Y\) depicts the output variable, and \(I(\cdot)\) is the indicator function.
## 5 Simulation Results
### Performance Analysis of the Optimization Algorithm
This process is closely related to the control strategy proposed in the article, which demonstrates excellent compatibility while ensuring the core functions of 5G base stations and IDCs. The dynamic load models of 5G base stations and IDCs constructed in the article play a key role at the load model level. 5G base stations adjust their power by changing the number of activated terminals and data traffic based on (1), achieving flexible regulation while ensuring communication quality. IDCs, on the other hand, use strategies such as geographic load balancing and batch processing workload scheduling to adjust power rationally while meeting computing demands and service quality. These models fully consider the operational characteristics and constraints of both, such as the downlink data rate constraints of 5G base stations and the power constraints of IT equipment and cooling systems in IDCs, ensuring that the control strategy does not affect their core functions when regulating power, providing a fundamental guarantee for the effective regulation of the total load within UDNs. At the optimization algorithm level, the ITLCO algorithm has made significant contributions. This algorithm enhances global optimization capabilities and convergence efficiency through chaotic mapping initialization and incremental pheromone update models. When solving the optimization and reconfiguration model of UDNs containing 5G base stations and IDCs, it can comprehensively consider the stability and economic goals  
Figure 9: The total load variation of UDN within one day. of the power grid and various constraints. It meets the requirements of power grid voltage deviation and network loss indicators and ensures that the operational needs of 5G base stations and IDCs are met. Compared with other algorithms, the ITLCO algorithm can find the optimal solution more quickly, reduce calculation time, and ensure the stable operation of 5G base stations and IDCs in the power grid, effectively supporting the optimization regulation of the total load within UDNs and further demonstrating compatibility with their core functions.
## 4 Discussion  
The primary aim of this investigation was to assess the validity and reliability of a commercially available IMU (Enode) to measure barbell kinematics and kinetics during a snatch. The results show that the Enode is valid and reliable for most variables, but it often overestimated horizontal-displacement-related data. These findings are important because, to the authors' knowledge, this is the first study to establish the validity and reliability of the Enode for snatches and associated variables that have previously been identified as measures of technique. Furthermore, our findings indicate that the Enode may be an affordable and accessible option to help coaches monitor weightlifting technique during a snatch, particularly between sessions, providing an accessible method that does not rely on laboratory or bespoke motion capture systems. While this may be the first study to investigate the validity and reliability of the Enode within the context of weightlifting, it has previously been studied for its validity and reliability across various lower-body ballistic and non-ballistic movements, such as squatting [10; 21; 35; 36; 37], jumping [21; 38; 39], and the hang power snatch [21]. The two primary variables extracted for comparison against 3D motion capture criterion were the mean and peak velocity. In the present study, peak velocity (and instantaneous end-of-phase velocity; v1, vT, and v2) was extracted for analysis given it is important within weightlifting-type exercises, as it provides an indication as to whether the barbell will be displaced at a high enough point for the athlete to receive it. Only one study to date has attempted to assess the validity and reliability of the Enode during a weightlifting derivative [21]. This study showed that Enode peak velocity demonstrated proportional bias, but these data had been pooled across different ballistic and non-ballistic exercises and so should be interpreted with caution [21]. Additionally, the aforementioned authors only used a 20 kg barbell for the hang power snatch, which may likely incur a longer active deceleration following the second pull, which may account for the systematic underestimation of mean velocity across all devices. A potential consideration around unit validity vs. criterion is the placement of the unit with respect to the barbell.
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 5 Simulations
### Accuracy Evaluation on CIFAR and MNIST Datasets
Additionally, MACS assesses computational load, excluding clients with high processing demand or limited resources, to prevent delays in model updates. Although MACS effectively handles scalability matters when dealing with rising IoT end-users and complex network models, it is essential to acknowledge potential computational trade-offs. In large-scale systems with high-mobility applications, the increased computational demands of MACS may impact real-time operations. Balancing the need for accurate mobility prediction and resource-aware selection with the constraints of real-world IoT environments will be an  
Figure 8: Accuracy comparison for MNIST dataset. ongoing challenge. Future research could explore optimizations to reduce computational overhead while maintaining performance. For instance, integrating advanced predictive models like Kalman filters for state estimation or LSTM networks to analyze sequential mobility patterns could improve selection accuracy in dynamic IoT environments. These enhancements ensure that MACS continues evolving and is effective in practical applications. By addressing these challenges and exploring future enhancements, MACS can continue to provide efficient and reliable client selection in federated learning, even as IoT networks grow in size and complexity. In each round, MACS dynamically adjusts client selection, reducing interruptions and improving training efficiency. While the current approach relies on real-time evaluation, future enhancements could incorporate predictive models like Kalman filters for state estimation or LSTM networks to analyze sequential mobility patterns. These advancements would further improve selection accuracy in dynamic IoT environments.The integration of LSTMs and Transformers for mobility prediction and federated reinforcement learning offers promising avenues for future work. However, real-world deployment in large-scale IoT systems presents its own set of challenges. Ensuring reliable connectivity across numerous devices and managing diverse hardware capabilities are significant hurdles that must be carefully addressed. Overcoming these challenges will be crucial for successfully adopting MACS in practical applications. For instance, network deployment complexities can arise from the number of connected devices and their varying capabilities. Some devices may have limited processing power or intermittent connectivity, which can disrupt the training process. Additionally, maintaining consistent performance across different hardware types can be difficult. Addressing these issues will require innovative solutions and careful planning. By incorporating advanced predictive models and addressing real-world challenges, MACS can continue to evolve and become even more effective in dynamic IoT environments. This evolution will ensure that the method remains relevant and practical for various applications, from smart cities to healthcare.
## 3 Communication Protocols  
In a smart home, devices need to communicate with each other to be able to exchange data. For making an efficient decision and changes, the intelligence system needs to have a detailed image of the environment. As a single sensor is not enough, several sensors are needed to collect various type of useful information (temperature, humidity, occupant's behaviour, etc.). To expand the functionality of collected information, sensors need to communicate as well. Communication protocols determine the manner in which these devices and sensors can mutually interconnect [3].  
Smart home networks are expected to be based on wireless sensor networks and machine-to-machine (M2M) communications protocols [28]. Considering the transmission medium, the communication protocols are classified into following groups:  
1. Wired
2. Wireless
3. Hybrid (which use both wired and wireless media)  
Body area networks (BAN), personal area networks (PAN), and local area networks (LAN) are the most common type of area networks used in smart homes [3]. Among different network topologies, a mesh network is the ideal one for wireless communication indoors because of obstacles such as walls and furniture [19].  
Choosing the right communication protocol could be done based on the case and the following concerns [3]:  
* Range of Coverage
* Level of security
* Level of power consumption
* Network size  
Another factor in the communication protocol selection is the price. For example, the cost of the ZigBee License is multiple of that charged for Z-Wave devices [29]. While the most popular communication protocol among smart home researchers and academic studies is ZigBee [30], the Z-Wave protocol has more vendors and is widely used in practical projects, mainly due to its interoperability. According to the Z-Wave Alliance, over 94 million Z-Wave products have been sold since 2001 [31].
## 1 Introduction
In this regard, many approaches have been proposed to modify the electron transfer and surface chemical properties, among which the doping of nitrogen into graphene has displayed enormous potential for widespread applications [35]. Compared to pristine graphene, N-doped reduced graphene oxide (NrGO) possesses a more biocompatible C-N microenvironment, a much larger functional surface area, a better electrical conductivity, a higher ratio of surface-active groups to volume, and enhanced electrocatalytic effects [35; 36]. Therefore, NrGO has been widely used to construct a variety of electrochemical sensors. For example, [PERSON] and coworkers [37] reported a facie one-step hydrothermal preparation of Fe\({}_{2}\)O\({}_{3}\)/NrGO nanohybrids toward DA detection. Fe\({}_{2}\)O\({}_{3}\)/NrGO showed superior electrocatalytic activity toward DA oxidation, with a broad detection range (0.5 uM-0.34 mM), a low limit of detection (LOD, 0.49 uM), and good sensitivity (418.6 uA mM\({}^{-1}\) cm\({}^{-2}\)). [PERSON] et al. [38] prepared NrGO/MnO nanocomposite via the freeze-drying technique to construct a selective electrochemical sensor for the detection of DA in the coexistence of UA and AA. Although NrGO has been intensively utilized in electrochemical sensing, as far as we know there is no report available for the use of MnO\({}_{2}\)/N-doped graphene composite for the simultaneous detection of DA and UA. Herein, MnO\({}_{2}\)NFs/NrGO nanocomposites were prepared by a facile, cost-effective and highly efficient route rather than the conventional hydrothermal method. Specifically, MnO\({}_{2}\)NFs were prepared by a slow addition of MnSO\({}_{4}\) into KMnO\({}_{4}\) solution followed by a simple stirring procedure, then composited with NrGO nanosheets with an ultrasonication assistant. The combined virtues of MnO\({}_{2}\)NFs and NrGO nanosheets are expected to enhance electrochemical sensing properties, which has been proven by using the MnO\({}_{2}\)NFs/NrGO as an efficient electrocatalyst for the simultaneous determination of DA and UA in serum samples. The proposed sensor showed remarkable catalytic capacity toward the oxidation of DA and UA, with two detection ranges (0.2-6.00 uM and 6-100 uM), low LOD (36 and 29 nM for DA and UA respectively), and good selectivity as well as reproducibility.
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 3 CLAMBDA-Search Model
### Error Analysis
#### 3.3.4 Maximum Pitch and Roll Angle Check  
The pitch and roll angle are often within a certain range in ground vehicle applications. So, the coordinate difference between the two fixed baseline vectors in the u direction should meet this condition. In this search, we set the maximum pitch and roll to 45 degrees.