esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management
###### Abstract  
Proper maintenance planning is critical for maintenance management to contribute to increasing availability, ensuring quality requirements, and controlling the safety and environmental risks associated with physical assets. As supporting tools for developing maintenance strategies, Reliability-Centered Maintenance (RCM) and Risk-Based Maintenance (RBM) methods are currently used in several organizations. Nevertheless, these strategies are often approached separately although they are complementary. In this context, this paper proposes a novel method that effectively integrates RCM and RBM by adapting the traditional RCM method to incorporate risk management into maintenance planning decision-making to support maintenance management. The proposed Reliability and Risk Centered Maintenance (RRCM) method allows organizations to determine maintenance plans that ensure the reliability of the physical assets while considering and prioritizing the risks associated with their potential functional failures. The proposed method was demonstrated through a case study considering the operational context of a hydroelectric power plant. The results show the ability of RRCM to assist in the development and implementation of maintenance plans oriented to reliability, risk, and cost.  
reliability-centered maintenance; RCM; risk-based maintenance; RBM; maintenance planning; RRCM +
Footnote †: journal: Applied sciences
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Environmental Monitoring
#### 3.2.3 Data Fusion and Analysis  
Once data are collected, they need to be aggregated and analyzed to draw meaningful conclusions [9]:  
* Bee foraging behavior can inspire algorithms for data aggregation where robots communicate and share their findings, ensuring that data from different parts of the environment are combined efficiently.
* SI algorithms facilitate dynamic clustering and analysis of data, helping to identify patterns, anomalies, and trends in environmental parameters such as temperature, humidity, pollutant levels, and biodiversity.
# A Relay Optimization Method for NOMA-Based Power Line Communication Systems
## 2 System Model
### Signal Model
#### 2.2.1 Uplink Model  
The uplink spectrum \(f_{u}\) is divided into \(M\) sub-bands, with each sub-band having a bandwidth of \(B_{u}\left/M\right.\). In the \(k\)-th sub-band \(f_{uk}\) of the uplink, it is assumed that there are \(K_{k}\) users performing NOMA transmission. The transmission signal of user \(U_{i}\) on \(f_{uk}\) is represented as:  
\[x_{i}^{(u)}(t) \tag{9}\]  
Each user \(U_{i}\) transmits signals to the source with uncorrelated transmission power \(p_{i}^{(u)}\), constrained by its maximum transmission power \(p_{max,i}^{(u)}\). If there are sufficient differences in channel gains \(h_{i}^{(u)}\) among users, they can transmit using their maximum power \(p_{max,i}^{(u)}\) to achieve better system performance. The total received signal is:  
\[y_{k}^{(u)}(t)=\sum_{i=1}^{K_{k}}h_{i}^{(u)}\sqrt{p_{i}^{(u)}}x_{i}^{(u)}(t)A_ {i}^{(u)}(d_{i},f)+n_{k}^{(u)}(t) \tag{10}\]  
where \(h_{i}^{(u)}\) is the channel gain of user \(U_{i}\) on sub-band \(f_{uk}\), and \(n_{k}^{(u)}(t)\) is the noise on sub-band \(f_{uk}\). The receiver uses Successive Interference Cancellation (SIC) technology to decode user signals sequentially in descending order of power. Successive Interference Cancellation (SIC) enables decoding of overlapping signals by iteratively removing the strongest component. High-power signals are decoded first. Its contribution is subtracted from the received signal, allowing clean decoding of subsequent low-power signals. This iterative offset mitigated multi-user interference. Assume the signal power of user \(U_{i}\) is \(P_{i}^{(u)}\). The signal of the user with the highest power, \(x_{i1}^{(u)}(t)\), is decoded first:  
\[x_{i_{1}}^{(u)}(t)=y_{k}^{(u)}(t)-\sum_{j=2}^{K_{k}}h_{i_{j}}^{(u)}\sqrt{p_{ ij}^{(u)}}x_{i_{j}}^{(u)}(t)A_{ij}^{(u)}\big{(}d_{ij},f\big{)} \tag{11}\]The remaining signals are decoded in sequence. In this manner, users transmit at their maximum power on the uplink, allowing the system to exploit differences in channel gains to enhance overall performance.
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 3 Experimental Results
### Goodness of Fit for Different Distributions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**S\({}_{4}\)**} & **Intervals in Nakagami-\(m\) Distribution** & \begin{tabular}{c} **Interval** \\ **Probability in Nakagami-\(m\) Distribution** \\ \end{tabular} & \begin{tabular}{c} **Intervals in** \\ **\(\alpha\)-\(\mu\) Distribution** \\ \end{tabular} & \begin{tabular}{c} **Interval** \\ **Probability in \(\alpha\)-\(\mu\)** \\ **Distribution** \\ \end{tabular} & \
\begin{tabular}{c} **Intervals in \(\kappa\)-\(\mu\)** \\ **Distribution** \\ \end{tabular} &
\begin{tabular}{c} **Interval** \\ **Probability in \(\kappa\)-\(\mu\) Distribution** \\ \end{tabular} \\ \hline \multirow{4}{*}{0.3} & (0,0.32) & \multirow{4}{*}{63.91\%} & (0,3.63) & \multirow{4}{*}{100\%} & (0.0.32) & \multirow{4}{*}{66.39\%} \\  & (1.23,3.63) & & & & (1.82,3.63) & \\  & (0–0.29) & & & & (0,0.29) & \\ \multirow{4}{*}{0.4} & (0.65,0.76) & \multirow{4}{*}{70.27\%} & \multirow{4}{*}{(0,4.07)} & \multirow{4}{*}{100\%} & (0.61,0.75) & \multirow{4}{*}{71.74\%} \\  & (1.24,1.41) & & & & (1.78,4.07) & \\ \multirow{4}{*}{0.5} & (0,0.23) & & & & (0,0.23) & \\  & (0.56,0.72) & & & & (0.55,0.73) & \\ \multirow{4}{*}{0.5} & (1.26,1.49) & \multirow{4}{*}{71.8\%} & \multirow{4}{*}{(0,4.22)} & \multirow{4}{*}{100\%} & (0.55,0.73) & \multirow{4}{*}{72.51\%} \\  & (1.81,4.22) & & & & (1.81,4.22) & \\ \multirow{4}{*}{0.6} & (0.4,0.64) & & & (0.44,0.67) & \multirow{4}{*}{66.98\%} \\  & (1.25,1.52) & \multirow{4}{*}{68.38\%} & \multirow{4}{*}{(0,4.27)} & \multirow{4}{*}{100\%} & (1.26,1.5) & \multirow{4}{*}{66.98\%} \\  & (1.86,4.27) & & & & (1.88,4.27) & \\ \multirow{4}{*}{0.7} & (0.0,8) & \multirow{4}{*}{91.78\%} & \multirow{4}{*}{(0,3.65)} & \multirow{4}{*}{100\%} & (0.07,6) & \multirow{4}{*}{88.77\%} \\  & (1.1,3.65) & & & & (1.17,3.65) & \\ \multirow{4}{*}{0.8} & (0,3.54) & \multirow{4}{*}{100\%} & (0,3.54) & \multirow{4}{*}{100\%} & (0,0.76) & \multirow{4}{*}{88.7\%} \\  & & & & & (1.16,3.54) & \\ \multirow{4}{*}{0.9} & (0,3.76) & \multirow{4}{*}{100\%} & (0,3.76) & \multirow{4}{*}{100\%} & (0,0.77) & \multirow{4}{*}{89.63\%} \\  & & & & & (1.16,3.76) & \\ \multirow{4}{*}{1.0} & (0,3.72) & \multirow{4}{*}{100\%} & (0,3.72) & \multirow{4}{*}{100\%} & (0,0.75) & \multirow{4}{*}{89.78\%} \\ \hline \end{tabular}
\end{table} 
Table 4: Intervals falling into the “mean \(\pm\) standard deviation” and the corresponding interval probability of the fitting performance under different scintillation intensities. Figure 11: Skewness–kurtosis profile.
###### Abstract  
Wireless sensor networks (WSNs) are considerably used for various environmental sensing applications. The architecture and internal specifications of WSNs have been chosen based on the requirements of particular applications. On this basis, WSNs consist of resource (energy and memory)-limited wireless sensor nodes. WSNs initiate data communication from source to destination via physical layer management principles, channel slot scheduling principles (time division multiple access), wireless medium access control (WMAC) protocols, wireless routing protocols and application protocols. In this environment, the development of WMAC principles, routing protocols and channel allotment schemes play crucial roles in network communication phases. Consequently, these layering functions consume more energy at each sensor node, which leads to minimal network lifetime. Even though the channel management schemes, medium control protocols and routing protocols are functionally suitable, the excessive energy consumption affects the overall network performance. In this situation, energy optimization algorithms are advised to minimize the resource wastage of WSNs during regular operations (medium control and routing process). Many research works struggle to identify the optimal energy-efficient load balancing strategies to improve WSN functions. With this in mind, the proposed article has conducted a detailed literature review and notable experimental comparisons on energy-efficient MAC protocols, channel scheduling policies and energy-efficient routing protocols. To an extent, the detailed analysis over these wireless network operations helps to understand the benefits and limitations of recent research works. In the experimental section of this article, eight existing techniques are evaluated under energy optimization strategies (WMAC, channel allocation, sleep/wake protocols, integrated routing and WMAC policies, balanced routing and cooperative routing). The proposed review and the classified technical observations collected from notable recent works have been recognized as crucial contributions. The results infer the suggestions for feasible WSN communication strategies with optimal channel management policies and routing policies. Notably, the simulation results show that cross-layer or multi-layer energy optimization policies perform better than homogeneous energy optimization models.  
wireless sensor network; energy optimization; MAC; routing; review and data communication 2022158883. [[https://doi.org/10.3390/en15238883](https://doi.org/10.3390/en15238883)]([https://doi.org/10.3390/en15238883](https://doi.org/10.3390/en15238883))
## 4 Open Issues and Challenges
### Straight Path
The precise location and speed of the \(S\) which are recorded in join message are obtained by the UAV. The location coordinates is denoted by \(S_{t_{k}}(x_{t_{k}},y_{t_{k}},0)\). The vehicles are supposed to mobile on a flatland and it assumes that the altitude changes are negligible. The distance between \(S\) and the midline of the path is denoted by \(a_{0}=y_{t_{k}}-f(x_{t_{k}})\) (Figure 7). Without loss of generality, we assume that the \(S\) moves along a line which is given by \(y=f(x)+a_{0}\) before the next beacon coming. This assumption is based on an estimation that the influence brought by the lane change of bicycles is negligible compared to the path length. Figure 6: Simple scenario with one UAV and one Sensor (curve path). Figure 7: An illustration of the trajectory of \(S_{i}\). In this figure, \(l_{k}\) is the curve length. Suppose that, the \(m+1\)th beacon is sent at \(t_{l}\) (then, the \(IBD=t_{l}-t_{k}\)). Then, the coordinates of \(S\) at \(t_{j}\) (\(t_{k}\leq t_{j}<t_{l}\)) is given by,  
\[\left\{ \begin{array}{l}v_{t}t_{j}\doteq\int_{0}^{x_{t_{j}}}\sqrt{(1+(y^{\prime })^{2})}dx\\ y_{t_{j}}=f(x_{t_{j}})+a_{0}\end{array} \right. \tag{12}\]  
where \(\int_{0}^{x_{t_{j}}}\sqrt{(1+(y^{\prime})^{2})}dx\) is the curve length of \(y=f(x)+a_{0}\) when \(x\in[0,x_{t_{j}}]\). Accordingly, the coordinates of the UAV at \(t_{j}\) can be obtained through,  
\[\left\{ \begin{array}{l}v_{t_{j}}\doteq\int_{0}^{x_{t_{j}}^{u}}\sqrt{(1+(y^{ \prime})^{2})}dx\\ y_{t_{j}}^{u}=f(x_{t_{j}})\end{array} \right. \tag{13}\]  
Let \(T_{cd,t_{k}}\) be the _remaining contact duration_ of the node \(S\) and the UAV at \(t_{k}\). It means that the \(S\) will out of the range of the UAV after \(T_{cd,t_{k}}\), and it can be obtained through,  
\[T_{cd,t_{k}}\doteq\frac{1}{v}\int_{x_{t_{k}}}^{x_{t_{\lambda}}}\sqrt{(1+(y^{ \prime})^{2})}dx \tag{14}\]  
with a boundary conditions \(d_{t_{\lambda}}(U_{t_{\lambda}},S_{t_{\lambda}})=R\) where \(t_{\lambda}=t_{k}+T_{cd,t_{k}}\) and \(R\) is the transmission range of the UAV.
# Eigenstate Transition of Multi-Channel Time Series Data around Earthquakes
###### Abstract  
To decrease human and economic damage owing to earthquakes, it is necessary to discover signals preceding earthquakes. We focus on the concept of \"early warning signals\" developed in bifurcation analysis, in which an increase in the variances of variables precedes its transition. If we can treat earthquakes as one of the transition phenomena that moves from one state to the other state, this concept is useful for detecting earthquakes before they start. We develop a covariance matrix from multi-channel time series data observed by an observatory on the seafloor and calculate the first eigenvalue and corresponding eigenstate of the matrix. By comparing the time dependence of the eigenstate to some past earthquakes, it is shown that the contribution from specific observational channels to the eigenstate increases before earthquakes, and there is a case in which the eigenvalue increases as predicted in early warning signals. This result suggests the first eigenvalue and eigenstate of multi-channel data are useful to identify signals preceding earthquakes.  
Keywords:data-driven approach; time-series data; multi-channel data; bifurcation analysis; early warning signals +
Footnote †: journal: Applied Sciences
## 5 Hardware Implementation and Experimental Results
### ASIC Synthesis of the Overall DFE Architecture  
Since the building blocks of the proposed DFE operate at different rates, the blocks were synthesized individually in a 180 nm digital CMOS technology. Areas (2-input NAND _GE_), delays, and slacks (= specification delay \(-\) delay) of the DFE building blocks are summarized in Figure 31. The most stringent delay was taken as the specification delay. FIR5_carrier1 had the largest size with the slowest speed, owing to many coefficients and accordingly many multipliers that were programmable multipliers to support a multitude of cases, which also degraded speed relative to constant multipliers. A slack of >2 ns was  
Figure 30: FIR5 channel selection filter (CSF) block diagram.  
considered during design but FIR1 had a slack of 1.48 ns. This block ran fastest of all at front of the decimation chain and hence a large margin was not easy to attain for it. FIR1 (CIC) had the smallest slack or timing margin while FIR5 had the largest slack secured. The pie graph for the areas of the building blocks is shown in Figure 32. FIR1 (CIC filter) occupied 1% of the entire area, FIR3 (Farrow interpolator) occupied the entire area by 15%, the digital mixer by 10%, FIR4_carrier1 by 17%, FIR4_carrier2 by 16%, FIR5_carrier1 by 24%, and FIR5_carrier2 by 17%.
# A Structure for Accurately Determining the Mass and Center of Gravity of Rigid Bodies
## 1 Introduction  
Mass and Center of Gravity (CG) are prerequisite parameters when designing the dynamic performance of an aerospace vehicle, car, etc. [1, 2]. When the shape of the vehicle is complicated, it is necessary to obtain mass and CG by experiments [3]. The mass can be achieved by weighing machines, for instance, load cells. There are several methods to measure the CG, which can be roughly classified into three categories as follows: (1) static method such as multi-point weighing method or unbalance moment method [1, 4, 5], (2) dynamic method such as spin balance method or inverted torsion pendulum method [6, 7, 8, 9], and (3) other methods such as the photogrammetric technique for determination of the CG for large scale objects [10].  
Each of these methods has its own strengths and weaknesses. For instance, the multi-point weighing method, which uses three or more load cells to support a test platform, has been widely used, and the reason is that this method can measure the mass and CG simultaneously. This method only depends on gravity force acting through the CG, and CG location is calculated by static equilibrium from the reaction forces on the load cells [1, 4]. Due to these reasons, it is the most suitable method for some massive objects [4], and it is also the cheapest automatic system.  
However, there are shortcomings in the multi-point weighing method, for example, a load cell is a spring and has a designed loading axis. In addition, the generated force against the load cell outside of this axis can result in errors and may shorten the operating life of the load cell [11, 12, 13, 14]. Besides, the measurement accuracy of CG is related to the position of the loading force.  
There are several types of load cells or bears to avoid incorrect loading, such as self-centering pendulum load cell, pendulum bearing, and pendulum supports. These load cells automatically guide the superstructure back to its original position when the load is introduced with lateraldisplacement [15]. However, these methods are usually used for a single load cell. Towards the multi-point weighing method where three or more load cells need to be coupled, no related literatures report the measurement structure.  
In order to achieve high accuracy when measuring mass and CG, optimizing the mechanical structure to make the force load on the designed axis is necessary. This paper focuses on designing a structure used in a multi-point weighing method to avoid lateral forces and get high repeatability. Moreover, mathematical modeling of the structure is further established to analyze the proposed method. Finally, simulation and experiment results are conducted to validate the effectiveness of the proposed method.
# The Analysis of the Applications of Crop Seed Tape Sowing Technology and Equipment: A Review
## 3 Research on Key Technology and Equipment for Seed Tape Manufacture
### Key Technology of Seed Tape Manufacture
#### 3.2.1 The Seed Wrapping Technology and Equipment
In order to overcome the shortcomings of the previous technology, [PERSON] and [PERSON] [69] invented a seed band sowing method suitable for precise sowing of small-particle seeds and designed a seed tape production device, which is equipped with a loose seed drop mechanism. The loose seeds scattered outside the seed point are blown into the collector. Compared with the previous device, it solves the problem of decoupling of fine seed and good method, which is beneficial to the integration of fine seed breeding and promotion,  
Figure 4: Working principle diagram of the rice seed tape twisting machine [27]. Figure 3: Machine for forming seed tape designed by [PERSON] [57]. Part I is the drilling seed supply system; part II is the rope reinforcement system and part III is the seed rope compaction system. seeds and cost-saving. Seed production, processing and circulation are all standardized, and it is convenient to calculate the total output by the number of seeds sown per unit area. [PERSON] [70] designed a kind of tape making machine controlled by a programmable logic controller (PLC). The control system of this machine was composed of several motors and frequency converters, which can feedback the tension signal of seed rope and ensure the production quality of seed tape. [PERSON] et al. [71] designed a precision seed tape wrapping machine for small seeds. A vacuum seeding disk was used which can realize zero-speed seeding of seed tape, and it improved the precision of seeding. To improve the safety of the seed tape twisting machine, [PERSON] et al. [72] designed an electric leakage safety detection device. Seed tape technology also has a good application in landscape agriculture. [PERSON] et al. [73] developed a seed tape weaving technology of mixed rapeseed and wheat, which provides a reference for landscape planting of field crops.
## 2 Methods
### Modelling Cyclists' following Behaviors  
Several previous studies, e.g., [PERSON] et al. [12], [PERSON] and [PERSON] [15], and [PERSON] et al. [13], described bicycle riders' following behaviors using car-following models. Car-following behavior describes the action of the following vehicle, i.e., maintaining a safe distance by accelerating or decelerating, in response to the leading vehicle's actions. The stimulus-response approach, which is the most common method in modelling car-following behavior, considers that the following car responds to a stimulus (a change in driving conditions) after a lapse of time, which is called the reaction time. The stimulus could be a change in spacing or relative speed, whereas the response is the acceleration or deceleration. [PERSON] et al. [22], which is one of the earliest car-following models, formulated the linear relationship between the acceleration of the following vehicle (response) and the relative speed (stimulus) as follows:  
\[A_{F}(t+T)=\lambda\;[V_{L}(t)-V_{F}(t)] \tag{4}\]  
where \(A_{F}\) is the acceleration or deceleration of following vehicle, \(V_{L}\) and \(V_{F}\) are the speeds of the leading and following vehicles, respectively, \(T\) is the reaction time, and \(\lambda\) is a model parameter called the sensitivity factor.  
[PERSON] [23] considered a linear combination of the relative speed and spacing, and proposed another linear model as given in the following equation:  
\[A_{F}(t+T)=\lambda_{v}\;[V_{L}(t)-V_{F}(t)]+\lambda_{l}\;[X_{L}(t)-X_{F}(t)-D( t)] \tag{5}\]  
where \(X_{L}\) and \(X_{F}\) are the locations of leading and following vehicles, respectively, at a given time \(t\), \(D(t)\) is the safety distance between vehicles, and \(\lambda_{v}\) and \(\lambda_{l}\) are model parameters.  
[PERSON] et al. [24] introduced a non-linear model that is called the general motors model by incorporating spacing, relative speed, and the speed of the following vehicle. The functional form of this non-linear model is as follows:  
\[A_{F}(t+T)=\lambda\;\frac{[V_{F}(t)]^{m}}{[X_{L}(t)-X_{F}(t)]^{l}}\;[V_{L}(t)- V_{F}(t)] \tag{6}\]  
where \(\lambda\), \(m\), and \(l\) are model parameters.  
It can be noted that the car-following models use desired speed, relative speed, and spacing as key model parameters. Using car-following models, the current study describes the dynamic nature and instability characteristics of the microscopic following behaviors of bicycle riders.