esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Summary and Perspective
### Data Volume, Velocity, and Variety  
Although ML methods have been well developed for decades, ML methods for online monitoring are a relatively new topic that needs more investigation. Due to the fast nature of the L-PBF process, the volume, velocity, and variety of collected data are large enough to fit the aspects of \"Big Data\" [22]. The articles reviewed in this study reveal that supervised ML methods are the most common approach used for in situ monitoring and control of the L-PBF. Therefore, it is expected that labeling and training this amount of data is both tedious and labor-intensive [159]. One alternative to reduce the time needed for data training is to use different ML approaches, such as unsupervised and active learning techniques, which are expected to be used more often. This would reduce the lengthy training time and offer an opportunity for faster response time [160].  
Another approach reported in the literature to reduce the training time is to use knowledge-based artificial neural networks (KB-ANN) [161]. The authors used a modular ANN composed of zones where system-related knowledge is already available in this approach. The weight of neurons can be precomputed without the need for training. Classical artificial neural networks (ANN) were trained using experimental data in areas where knowledge is insufficient. The authors applied this approach to a fused deposition modeling (FDM) process; it is expected that this approach might also be suitable for the L-PBF process and is worth investigating. Another concern is the huge resource consumption needed to create enough data for model training. A suggested approach is integrating physics-based models to ML models [162]. The physics-based model was used to generate large thermal history data used for training the ML model. The model suggested by [PERSON] et al. [162] was validated experimentally and yielded good results; however, it was created for a single layer. The ability to expand this model to multilayer and expand the library of materials may be computationally expensive but still necessary.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 2 State-of-the-Art
### Comparison of Different EDFA Models
These models offer two major advantages: accuracy and flexibility. There are several examples in the literature of models that consistently estimate gain with an RMSE of around 0.15 dB [7; 22], and these models can be easily adapted or extended to many different devices and brands with little modification or training. The primary drawback of these models, and a significant barrier to their practical implementation, is their typically high requirement for large training datasets. Acquiring such datasets is often expensive or impractical. While smaller datasets can be utilized, they pose challenges in ensuring performance reliability, particularly in complex systems. One of the goals of the model proposed in this work is to offer a rapid and adaptable solution capable of being trained with datasets of varying sizes, aiming to strike a balance between achieving target accuracy and minimizing the costs associated with required measurements.
# Subway Station Capacity Maintained by Optimizing a Maintenance Schedule of Key Equipment
## 6 Simulation Study
### Reliability Modeling of the Key Equipment
#### 6.3.1 Reliability Modeling of the Gate  
The components of the gate include the box body, the blocking body, the machine core, the control module and the auxiliary module. The failure-prone components of the gate are mainly the machine core and control module. In the light of the maintenance records of the gate, the faults of the gate can be divided into two categories: software faults and hardware faults. The common software fault is the system crash which requires restart of the gate. The familiar hardware faults include poor contact of circuit, abnormal component parameters and component damage which requires replacement of components. Based on the statistical analysis of fault data, the software faults account for 43% and the hardware faults account for 57%. The software and hardware fault distribution fitting are completed separately.  
In this study, 51 samples of the software fault intervals of the gate are used. The average fault interval is 34 days, and the standard deviation is 40. The result of goodness of fit test is shown in Figure 12. As shown in Figure 12, _Weibull_ distribution fits the fault data best, and the Anderson-Darling statistic of _Weibull_ distribution is the smallest. Therefore, the optimal fault distribution of software is _Weibull_ distribution, and the shape parameter is 0.80, the scale parameter is 30.05. For the hardware fault of the gate, 63 samples of the fault interval are used. The average fault interval is 62 days, and the standard deviation is 82. The result of goodness of fit test is shown in Figure 13. As shown in Figure 13, _Weibull_ distribution fits the fault data best, and the Anderson-Darling statistic of _Weibull_ distribution is the smallest. Therefore, the optimal fault distribution of hardware is _Weibull_ distribution, and the shape parameter is 0.843, the scale parameter is 55.92. The reliability function of _Weibull_ distribution is formulated as follows:  
\[R(k)=\exp[-(\frac{k}{\eta})^{\beta}] \tag{16}\]  
where \(k>0\), \(\beta>0\) and \(\eta>0\), \(\beta\) is the shape parameter and \(\eta\) is the scale parameter.  
Figure 11: The impact of the reliability of the escalator on expected travel time.
## 2 Various Types of Flexible Sensors (Temperature, Pressure, Humidity and Chemical)
### Temperature Sensors
#### 2.4.1 Working Mechanism  
Temperature sensors operate based on a material property that varies with temperature. This could be resistance, volume or light properties. The changes in environmental temperature triggers changes in the property of interest (e.g., resistance). The changes in the property are measured and mathematically correlated to changes in temperature. There exist several types of temperature sensors and, among these, the most popular are resistance temperature sensors (RTS), thermistor and thermocouple. Thermocouple sensors are fabricated using two different metals such as copper/constant, iron/constant or chromel/alumel. Thermocouples work on the basis of the Seebeck effect [33]. Thermistors contain materials that vary in resistance with changes in temperature. This change can be measured and calibrated to temperature measurements. Thermistors can measure small variations in temperature, and they come in various shapes and sizes. Common thermistor shapes are bead, rod and disk shapes. The bead type is the smallest in size with a diameter of \(\leq\)1.25 mm [34]. For construction of RTSs, silver, copper, gold and nickel are the most used materials [35]. Recently, carbon-nanomaterials and conductive polymers have gained much attention in the fabrication of RTSs [2; 26; 36; 37].
## 4 Discussion
### Mangrove Belt Expansion Identification in Google Earth Engine  
The expansion of the mangrove belt has been substantiated with the supervised land cover classification. We have evidenced a total of \(11\) years of mangrove development (2009-2019) with high accuracy (more than 98%). Cloud computation with GEE to our advantage, we generated three monthly mangrove maps, with the exception of sixth-monthly maps during the period of 2009-2012. Most importantly, our map series has higher frequency than the above-mentioned global mangrove products.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Cohesive Subgraph Search
#### 4.3.3 Core Model
However, the problem of core maintenance on streaming graphs remains largely unexplored and presents a significant area for future research. For the time complexity, Ref. [20; 71] propose parallel core maintenance algorithms in incremental dynamic graphs whose worst-time complexity for both are bounded by a polynomial in \(\Delta I\), \(m_{S}\), \(L_{S}\) and \(n_{S}\), where \(\Delta I\) is used to represent the maximum number of edges inserted to each vertex in the origin graph, \(L_{S}\) is the max times that a vertex \(u\) can be visited by negative DFS procedures in the algorithm execution, and \(m_{S}\) represents the max number of edges traversed when Ref. [20] computes superior degree and Ref. [71] computes constraint superior degree in the case of inserting edges. The worst-time complexity of [69] has the same structure as that of [20; 71], with the only differences being \(n_{S}(k)=|V_{S}(k)|\) and \(m_{S}(k)=|E_{S}(k)|\). In [75], \(\phi_{G}\) represents the largest core number of vertices in \(G\), \(|G^{\prime}|\) denotes the size of updated graph and \(|G_{k}|\) is used to denote the number of \(k\)-core. The time complexity of [76] is superior to that of other listed parallel algorithms. Here, \(|E^{+}|\) is the largest number of adjacent edges for all vertices in \(|V_{+}|\) among each inserted edge where \(|V_{+}|\) is a larger set of vertices that is traversed to determine which vertices the core needs to update. For single edge/vertex update, [PERSON]. [70] proposed a linear algorithm named TRAVERSAL whose worst-time complexity is bounded by the number of edges. Moreover, based on the finding that the order algorithm [69] significantly outperforms the traversal algorithm [74] across various real-world graphs, Refs. [76] further performs better than [69]. Ref. [76] improves the existing parallel core maintenance methods [71; 72] because the prepossessing of \(\Delta E\) is not required so that edges can be inserted or removed on-the-fly.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 4 Simulation Studies for a 6-DOF Manipulator
### Trajectory Error Compensation  
The PTP trajectory of the previous part was compensated, and the compensation angle of joint angle \(\theta_{1}\sim\theta_{6}\) was calculated by using Equations (38)-(42), as shown in Figure 8. The space coordinate errors before the error correction are shown in Figure 9.  
Figure 6: Stiffness changes in the translation direction of X, Y, and Z.  
Figure 7: Position errors in the X, Y, and Z translation directions.  
In Figure 9, \(\Delta x\), \(\Delta y\), and \(\Delta z\) represent the position error in the X direction, Y direction, and Z direction, respectively, before the compensation of the joint angles, while \(\Delta x\_SM\), \(\Delta y\_SM\), and \(\Delta z\_SM\) represent the corresponding position errors after angle compensation. The simulation results show that the position errors of the manipulator end were reduced in the X, Y, and Z directions.
# Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System
## 3 Materials and Methods
### Image Processing Stage
#### 3.2.4 Feature Extraction  
Four quantities are defined to be used for the development of the classification algorithm. These are the eccentricity and the surface area of the coffee beans, their roundness, and a particular area relation between the damaged surface area of the coffee beans and their entire area. For the computation of these chosen features, surface areas, damaged surface areas, eccentricity and the perimeter must be extracted from the coffee beans segmented in the binary images shown in Figures 7 and 8.  
* Surface Area: this quantity refers to the total surface area of the coffee beans and it is particularly useful for the distinction of small and immature beans.
* Roundness: The roundness of coffee beans is defined as follows [38]: \[\text{roundness}:\frac{4\pi A}{P^{2}},\] (5) where A refers to the surface area of the coffee bean and p refers to its perimeter. This quantity is particularly useful for the classification of very long berry or broken coffee beans.
* Area Relation or Color Feature: this quantity is simply a ratio between the damaged surface area (A1) and the total surface area (A2) of the coffee beans: \[\text{ratio}=\frac{A_{1}}{A_{2}}.\] (6) This feature is very useful to distinguish coffee beans containing external defects related to their color such as sour or black coffee beans. This quantity value tends to 0 for coffee beans that do not have any external defect on their surface since the damaged surface area tends to 0 by segmenting the image in the color spaces. MATLAB label functions were used to label the individual coffee beans in those images containing more than one coffee bean. Next, the classification algorithm is applied individually.
* Eccentricity: this quantity characterizes the shape of a conic section; it is particularly useful when talking about ellipses. The eccentricity of a circle is 0 and the eccentricity of an ellipse which is not a circle is greater than 0 but less than 1. The eccentricity value is particularly useful for the distinction of very long berry coffee beans and broken beans, which have similar roundness values.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 3 Security Analysis
### Correlation Analysis  
The strong correlation between adjacent pixels is one of the characteristics of image data. It is necessary to make adjacent pixels as irrelevant as possible. The correlation coefficient can reflect the degree of association between adjacent pixels. A correlation coefficient close to 0 represents a weak correlation between adjacent pixels, and it is difficult for attackers to use this information to conduct statistical attacks. The correlation coefficient is computed as follows [16,17]:  
\[E(x)=\frac{1}{K}\sum\limits_{i=1}^{K}x_{i} \tag{18}\]  
\[D(x)=\frac{1}{K}\sum\limits_{i=1}^{K}(x_{i}-E(x))^{2} \tag{19}\]  
\[cov(x,y)=\frac{1}{K}\sum\limits_{i=1}^{K}(x_{i}-E(x))(y_{i}-E(y)) \tag{20}\]  
\[H=-\sum\limits_{i=0}^{255}p_{i}\log_{2}(p_{i}) \tag{21}\]  
where \(r_{xy}\) represents the correlation coefficient, \(x\) and \(y\) represent the pixel values of adjacent pixels, and \(K\) represents the number of randomly selected pixel pairs; \(K=5000\) is set in this paper.  
The correlation coefficients are divided into the horizontal direction, vertical direction, and diagonal direction according to the different ways of taking adjacent pixels. Table 5 shows the test results. The correlation coefficients of the cipher image are close to 0 in three directions, indicating that the correlation degree of adjacent pixels can be effectively reduced.  
Figure 10 shows the correlation of Lena in different directions. The pixel value of adjacent points of plain images are all clustered near \(y=x\), which has a strong correlation. The pixel value of the adjacent points of the cipher image are uniformly distributed in the rectangular region, which shows that the distribution of the pixel value of the cipher image is discrete and the correlation degree of the adjacent pixels is weak.
# A Novel Adaptive FCM with Cooperative Multi-Population Differential Evolution Optimization
## 4 Proposed Co-Evolutionary Framework
### Fitness Function  
A candidate \(\textbf{p}_{i}\) is decoded for the cluster number \(c_{i}\) and cluster prototypes \(v_{i}=\{v_{i1},\)\(v_{i2},\ldots\)\(v_{i\alpha}\}\) and a preliminary assignment of data in the subset to clusters is performed as,  
\[u_{i\textit{lik}}=\frac{\left(\frac{1}{||v_{i\alpha}-x_{i}||^{2}}\right)^{ \frac{1}{n-1}}}{\sum\limits_{j=1}^{c_{i}}\left(\frac{1}{||v_{i\beta}-x_{k}||^{ 2}}\right)^{\frac{1}{n-1}}}\text{, }1\leq h\leq c_{i}\text{, }1\leq k\leq p \tag{17}\]  
The \(XB\) index is defined as the ratio of the total variation \(\sigma\) to the minimum separation \(\lambda\) of the clusters [12]. The total variation is in fact the objective function with \(m=2\) and depends on the memberships and location of the cluster prototypes, while the minimum separation is the squared Euclidean distance between the closest cluster prototypes.  
\[\begin{array}{c}\sigma_{i}(U_{i},V_{i};X)=\sum\limits_{k=1}^{\tau_{i}}\sum \limits_{k=1}^{\tau_{i}}\sum\limits_{k=1}^{\tau_{k}}\omega_{k}^{\lambda}||v_{ \alpha}-x_{k}||^{2}\\ \lambda_{i}(V_{i})=\frac{||v_{i\alpha}-v_{k}||^{2}}{\sigma_{i}(V_{i})}\\ X_{B}=\frac{||v_{i\alpha}||v_{i\alpha}||^{2}}{\sigma_{i}(V_{i})}\end{array} \tag{18}\]  
When the clustering is compact and the number of clusters is evaluated correctly, the value of objective functional based \(\sigma\) is minimized and the value of separation function \(\lambda\)will be maximized, resulting in smaller values of the \(XB\) index. A cluster validity index \(\Psi\) is defined in [58] as,  
\[\Psi_{i}^{2}=\frac{J_{1}(X)D_{i}(V_{i})}{n_{i}I_{i}(U_{i},V_{i};X)} \tag{19}\]  
where \(J_{i}(U_{i},V_{i};X)=\sum\limits_{h=1}^{c_{i}}\sum\limits_{k=1}^{p}u_{ihk}||v_ {ih}-x_{k}||\), \(D_{i}(V_{i})=\max\limits_{h\
eq j}||v_{ih}-v_{ij}||\) and \(J_{1}(X)\) is constant for a given dataset and is calculated by setting \(c_{i}=1\) in the above equation (all datapoints are considered to be in the same cluster). It has been shown that good, compact clusters with the correct number of clusters identified will tend to maximize \(\Psi\). The fitness for the candidate \(\mathbf{p}_{i}\) in the population is evaluated for the differential evolution algorithm as a weighted sum of the two cluster validity indices,  
\[f(\mathbf{p}_{i})=\frac{a}{XB_{i}}+b\Psi_{i}^{2} \tag{20}\]  
where \(a\) and \(b\) are weights in \([0,1]\) that define the relative importance of the respective cluster validity index. In this paper, we choose \(a=b=0.5\).
## 2 System Model
### Computational Model  
(1) Local Computing  
When a vehicle performs a computational task locally, the delay and energy consumption of the computation depend on the vehicle's own computational capability, which is determined by the on-board unit (OBU, on board unit) [18] placed on the vehicle. Therefore, the energy consumption and latency of the task computed locally are, respectively:  
\[E_{m}^{LOC}=\delta\left(f_{m}^{LOC}\right)^{2}C_{m} \tag{2}\]  
\[T_{m}^{LOC}=\frac{C_{m}}{f_{m}^{LOC}} \tag{3}\]  
where \(f_{m}^{LOC}\) denotes the computing power of vehicle \(m\), \(\delta\left(f_{m}^{LOC}\right)^{2}\) denotes the power consumed by the core computing of each vehicle, and \(\delta\) denotes the effective open capacitance factor of the corresponding chip structure of the vehicle [19].  
(2) Edge Server Computing  
When a vehicle performs a computationally intensive and delay-sensitive task and uses an edge server to process it, the delay and energy consumption of the results fed back to the vehicle are negligible because the size of the output data after computation at the edge server is much smaller than the size of the input data [20]. Therefore, the energy consumption and latency of the task computed at the edge server are:  
\[E_{m}^{MEC}=\frac{D_{m}P_{m}}{R_{m}} \tag{4}\]  
\[T_{m}^{MEC}=T_{m}^{up}+T_{m}^{count} \tag{5}\]  
where \(R_{m}\) denotes the wireless transmission rate between the task vehicle and the edge server, and \(T_{m}^{up}\) and \(T_{m}^{count}\) denote the transmission delay of the task vehicle and the edge server and the processing delay of the task in the edge server, respectively, defined as:  
\[T_{m}^{up}=\frac{D_{m}}{R_{m}} \tag{6}\]  
\[T_{m}^{count}=\frac{C_{m}}{f_{m}^{MEC}} \tag{7}\]
# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 3 Results and Discussion  
### Analysis of Dielectric Properties under Different Gdd  
Figure 4 shows the dielectric parameters of golden passion fruit under different _GDD_. With the increase in the frequency of the test signal, the dielectric parameters \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\) of the fruit decrease under different _GDD_. This trend was most significant at low frequencies (0.05 to 10 kHz), a pattern also observed in studies of the dielectric properties of other fruit and vegetables such as tomatoes and mangoes [40; 41]. Additionally, at the same frequency, the \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\) values were generally lower for ripe samples compared to unripe ones. The main reason for this is that as passion fruit ripens, the membrane permeability and free water content increase. As a result, the ability of the cell membrane in mature fruit to bind charges weakens, leading to a gradual decrease in \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\)[42; 43]. Overall, in the frequency range of 0.05-5 kHz, there are significant differences in the dielectric properties of passion fruit under different _GDD_.
# Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D
## 3 Methods
### Velocity Field Level Set Method  
The geometry of a material during optimization is described using the level set function (LSF) \(\mathbf{\varphi}:\mathbb{R}^{3}\rightarrow\mathbb{R}\). The following relationship applies to the LSF \(\mathbf{\varphi}(\mathbf{x})\):  
\[\begin{cases}\mathbf{\varphi}(\mathbf{x})>0,&\forall\mathbf{x}\in\Omega\quad\text{( part)}\\ \mathbf{\varphi}(\mathbf{x})=0,&\forall\mathbf{x}\in\partial\Omega\quad\text{( part surface)}\\ \mathbf{\varphi}(\mathbf{x})<0,&\forall\mathbf{x}\in D\backslash\Omega\end{cases} \tag{5}\]In the article, the so-called signed distance function (SDF) is used as LSF for the purpose of stabilizing the optimization. A SDF is defined as follows:  
\[\varphi(\mathbf{x})=d(\mathbf{x},\partial\Omega) \tag{6}\]  
whereby the function \(d\) computes the distance between a point \(\mathbf{x}\) and the surface \(\partial\Omega\). Hence, the property \(\|\
abla\varphi(\mathbf{x})\|=1\) holds for a SDF.  
The evolution of the LSF in a normal velocity field \(v^{n}\) is carried out using the Hamilton-Jacobi equation [30]:  
\[\frac{\partial\varphi}{\partial t}+v^{n}\|\
abla\varphi\|=0. \tag{7}\]  
The optimization method used in this article employs the VFLSM, which uses the following relationship between design variables \(\mathbf{\eta}\) and normal velocity field \(v^{n}\)[7]:  
\[v^{n}(\mathbf{x})=\sum_{i=1}^{n_{d}}b_{i}(\mathbf{x})\eta_{i}. \tag{8}\]  
In this equation, \(b_{i}(\mathbf{x})\) denotes the linear global basis function (FEM) related to node \(i\).
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 5 Experimental Results
### General (D)DoS Attack  
Although AccFlow is primarily designed to address low-rate TCP DoS attacks, it also serves as a defense mechanism against general DoS attacks. The distinction between low-rate TCP DoS attacks and general DoS attacks lies in their respective methods of operation. The former relies on the TCP retransmission timeout mechanism to launch attacks, whereas the latter disrupts legitimate users' services by inundating the network with a high volume of traffic.  
As previously mentioned, the central concept behind AccFlow is to hold attacking flows accountable for network congestion by promptly dropping their packets based on their loss rates. Consequently, any attacking flows that contribute to congestion are unable to disrupt legitimate flows by excessively utilizing network resources.  
In fact, the effectiveness of AccFlow against general DoS attacks stems from our deliberate decision not to rely on the periodic nature of low-rate TCP DoS attacking flows when designing the algorithm.  
To test the effectiveness of AccFlow when the network is faced with general DoS attacks, we use similar simulation settings listed in Table 1 except that the attackers consistently generate traffic without pause. As illustrated in Figure 13, AccFlow can effectively defend against the general DoS attacks. Furthermore, AccFlow also has a quick convergence time under such attacks, which is in the order of tens of seconds.  
Figure 12: AccFlow harmoniously coexists with the benign periodic flow and normal flows without causing performance degradation.