esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an On-Line Defect Detection System for EDM Process
## 3 Case Study
### Model Judgment Result  
The summary of exponential results for interval 1, shown in Table 1.  
Figure 10: OCR feature calculation and analysis results.  
Figure 9: AIDT feature calculation and analysis results.  
In this section, abnormal processing is used as the verification, and the models trained with the features in Section 3.1 are input, respectively. In order to avoid misjudgment caused by a single method, three independent models are trained in three methods (respectively, NN, RF, and XGB-RF), and the voting rules are used for reducing the problem of excessive weight for a single model.  
The experimental results significantly indicated that the CV values of these seven features (e.g., ASF, ADE, etc.) were obviously separated into two clusters between the normal and abnormal machining processes. The EDM abnormal monitoring model was trained well by picking up the key features of the raw data. In order to avoid misjudgment caused by a single method, three independent models were adopted to be trained in three independent methods (NN, RF, and XGB-RF, respectively). The voting rules reduce the problem of excessive weight for a single model. In summary, three models indicated either a normal or abnormal case forward equal response. Only single model voting would lead to pre-warning (and not a warning state of the EDM machine).  
In the model judgment result, as shown in Figure 11, after the 16 th minute of the NN model, the judgment result begins to stabilize, and the judgment only misses for 2 min in the subsequent 55 min. After 8 min of the RF model, the judgment result begins to stabilize, and within 63 min, only 1 min is missed. The XGB-RF model starts to stabilize after 14 min, and within 57 min, only 1 min is missed. With the voting mechanism added, as a whole, the sensitivity can reach 0.974 and the Fpr is 0.07. Compared with the previous two seconds, it is judged formally. The sensitivity reaches 0.93 and the Fpr is 0.002.
## 3 Mechanically Tunable PhC Cavity Sensors
### Sensing Applications of Mechanically Tunable PhC Cavities
#### 3.2.1 Deformable Cavity-Based PhC Sensors
The bending of cantilever modulated the effective RI, and hence induced resonance shift of cavity. A theoretical minimum of surface stress of about 0.8 mN/m was solved by mechanical tuning the defect of PhC slab. Another analogous tunable PhC cavity integrated cantilever was proposed by directly inserting a cantilever into the middle of a PhC nanobeam [78], as illustrated in Figure 7b. The top 200 nm silicon layer served as an electrode while the thick silicon layer acted as a ground, and these two layers were electrically isolated by a 1-\(\upmu\)m silicon dioxide layer. The cantilever bent out of PhC slab and affected the band-edge resonance mode. Besides, a nonlinear behavior was observed in the impact of the cantilever's deflection on resonance shift. A maximum resonant wavelength shift of 13.5 nm was obtained under the applied voltage of 97 V, however, the quality factor was relatively low because of the cavity loss caused by inserting the cantilever into the nanobeam cavity.
## 5 Analysis of CDSAs  
In the CDSAs, a new SM of a node is achieved after a round of DD and SU stage, which is illustrated in Figure 8. The SM determines the scheduling probability of the data packets transmission as well as the probability of Sleep state, and determines the work features of a node at the next DD stage. The scheduling parameters (SP), such as \(v\), \(m\), \(g\) and \(\beta\), are the fundamental elements of the SM. So it  
\begin{table}
\begin{tabular}{l l l} \hline S\({}_{\text{i}}\) & S\({}_{\text{i+1}}\) & P(S\({}_{\text{i+1}}\)|S\({}_{\text{i}}\)) \\ \hline S & I\({}_{0}\) & 1 \\ S & I\({}_{\text{x}}\) & 1 \\ I\({}_{0}\) & S & \(1-v\) \\ R & T & \(m\times\beta\) \\ Ix & T & \((1-v)\times\beta\) \\ T & T & \((1-v)\times\beta\) \\ F & S & \(g_{p}\times(1-v)\) \\ \hline \end{tabular}
\end{table}
Table 2: Scheduling Probabilities of T-CDSA.  
can be used to represent the SM. The sequence of SP decides the work process of a node. Since the next SP depends on the current SP only, the sequences of SP is a Markov process.  
In this paper, the WSN is a reliable and loseless network. The remark below will demonstrate that the SP process will be adapted to the workload and the communication channel status of a node.
## 2 Numerical Methods
### Motion of Object  
The particles movement is described by the equations of motion for xyz-transportations and the angular momenta of a rigid body as follows:  
\[\frac{d\left(m_{\text{P}}\mathbf{u}_{\text{p}}\right)}{dt}=\int_{S}fdS+m_{\text{ P}}\mathbf{g}, \tag{5}\]  
\[\frac{dX_{\text{P}}}{dt}=\mathbf{u}_{\text{P}}, \tag{6}\]  
\[I_{\text{P}}\frac{d\mathbf{\omega}_{\text{P}}}{dt}=\mathbf{T}, \tag{7}\]  
\[\frac{d\theta_{\text{P}}}{dt}=\mathbf{\omega}_{\text{P}}, \tag{8}\]  
where \(m_{\text{P}}\) and \(I_{\text{P}}\) denote the particles mass and inertial moment, respectively. \(\mathbf{u}_{\text{P}}=(u_{\text{P}},\,v_{\text{P}},\,w_{\text{P}})\) and \(X_{\text{P}}=(x_{\text{P}},\,y_{\text{P}},\,z_{\text{P}})\) represent the velocity and position of the particle's center of mass, \(\mathbf{\omega}_{\text{P}}=(\omega_{\text{px}},\,\omega_{\text{py}},\,\omega_ {\text{pz}})\) and \(\theta_{\text{P}}=(\theta_{\text{px}},\,\theta_{\text{py}},\,\theta_{\text{ pz}})\) are the angular velocity and orientation of the particle. \(f\) and \(\mathbf{T}\) express the aerodynamics forces and torque acting on the particle by the fluid. \(S\) is the cell face between the fluid and ghost cells, and \(\mathbf{g}\) denotes the acceleration due to gravity. The distribution of the level set function changes by the position of particle's center moving. The velocities of two particles (labeled 1 and 2) after their collision are respectively given by:  
\[\begin{array}{l}\mathbf{u}^{\prime}_{\text{P,1}}=\frac{m_{\text{P,2}}}{m_{ \text{P,1}}+m_{\text{P,2}}}(1+e)\big{[}\big{(}\mathbf{u}_{\text{P,2}}-\mathbf{ u}_{\text{P,1}}\big{)}\cdot\mathbf{c}\big{]}\mathbf{c}+\mathbf{u}_{\text{P,1}},\\ \mathbf{u}^{\prime}_{\text{P,2}}=\frac{m_{\text{P,1}}}{m_{\text{P,1}}+m_{ \text{P,2}}}(1+e)\big{[}\big{(}\mathbf{u}_{\text{P,1}}-\mathbf{u}_{\text{P,2} }\big{)}\cdot\mathbf{c}\big{]}\mathbf{c}+\mathbf{u}_{\text{P,2}},\end{array} \tag{9}\]  
where \(e\) is the coefficient of restitution, and \(\mathbf{c}\) is a standardization vector [19; 20]. In this solver, the collision is applied to the perfectly elastic collision; \(e=1\). The collision detection of particles 1 and 2 was determined using an inequality involving Pythagoras formula as follows (see Figure 2),  
\[\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}+(z_{2}-z_{1})^{2}}\leq\sqrt{(r_{1}+ r_{2})^{2}}, \tag{10}\]  
where \(r\) is the particle radius.  
After a particle-wall collisions, the particle velocity becomes:  
\[\mathbf{u}^{\prime}_{\text{P}}=(1+e)\big{[}\big{(}-\mathbf{u}_{\text{P}} \big{)}\cdot\mathbf{c}\big{]}\mathbf{c}+\mathbf{u}_{\text{P}}. \tag{11}\]  
Figure 2: Collision detection.  
To detect a collision between a particle and the wall, the central position of the particle and the normal vector were obtained from the level set function. When the image point appeared in the ghost cell of the other object, the velocity and the pressure components of the ghost cell were not updated. The velocity and the pressure of the object cell were constantly defined as the initial conditions.
###### Abstract
Based on the above analysis, it can be concluded that the current two methods for coverage enhancement have problems such as high sensor density requirements, high deployment costs, and low coverage efficiency. To address these problems, we studied the coverage enhancement problem of the IoT sensing layer composed of a mixture of stationary and mobile directional sensors. Then, we proposed a node deployment optimization method to achieve coverage enhancement. The method proposed in this article fully utilizes directional sensors' rotation and movement performance. The proposed method mainly includes two stages. First, the sensing direction is adjusted by rotating the stationary sensor to expand the effective coverage area and improve the coverage rate. In the second stage, the first step is to search for the MEP as the target path to repair the coverage hole. Then, a redeployment strategy is designed for mobile sensors, repairing coverage holes and reducing redundant coverage by scheduling mobile nodes. The proposed method significantly improves the network coverage through two-stage coverage enhancement. In addition, our approach takes full advantage of the coverage area of stationary sensors, reducing the use of mobile sensors and deployment costs. Compared with other algorithms, our method has significant advantages regarding coverage rate, redundancy rate, and energy consumption. The main contributions of this paper are summarized as follows. 1. We propose a two-stage node deployment optimization method to achieve coverage enhancement. This method fully utilizes the rotation and movement capabilities of directional sensors, improving the coverage rate and reducing deployment costs by adjusting the sensing direction and redeploying mobile nodes. 2. In the stage of adjusting the sensing direction, an improved particle swarm optimization algorithm is proposed with the goal of maximizing the effective coverage area and minimizing the redundancy rate to solve the optimization model and obtain the optimal working direction of the stationary sensor. 3. In the redeployment phase of mobile nodes, we first use an improved sparrow search algorithm to obtain the MEP. Using the obtained MEP as the target path to repair the coverage hole, a mobile node redeployment strategy is designed to repair the hole. Our method can improve coverage performance with lower mobile energy consumption while meeting the conditions of energy balance. 4. The performance of the proposed method was simulated and compared with other advanced methods in terms of coverage rate and energy consumption. We also analyzed the reasons for the performance differences. This article consists of six parts, and the rest are arranged as follows. We provide an introduction to the relevant research on coverage enhancement in Section 2. Section 3 describes the mathematical expression of the system model and problem. In Section 4, we provide a detailed description of our algorithm. Section 5 presents the experiments and performance evaluation. In Section 6, the contribution of this article is summarized and future work is planned.
## 2 Materials and Methods
### Volatile Compound Analysis
#### 2.3.2 GC-MS Analysis  
GC-MS analysis was performed using a Shimadzu QP2020 GC-MS model (Shimadzu Corporation, Kyoto, Japan) equipped with an HP-FFAP capillary column (50 m \(\times\) 0.32 mm i.d., 0.65 \(\upmu\)m film thickness; Agilent Technologies, Wilmington, DE). The mass detector was operated in electron impact mode at an ionization energy of 70 eV with a scanning mass range of 30-500 m/z. The GC oven temperature program was started at 50 \({}^{\circ}\)C for 2 min, 50-80 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, then 80-250 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, and finally held at 250 \({}^{\circ}\)C for 5 min. Helium was used as the carrier gas at a constant flow rate of 1.0 mL/min. The transfer line temperature was maintained at 230 \({}^{\circ}\)C. A series of n-alkane (C7-C30) standards were employed to determine the linear retention index (RI) of each compound. The volatile components of the sample were identified by matching the mass spectra from the NIST library (ver.14) and the RIs of authentic standards. When authenticated standards were unavailable, tentative identifications were based on the NIST library (ver.14) and a comparison of RIs from the literature. The relative content of each volatile compound in the samples was obtained by calculating peak area ratio between the analyte and internal standard (2-methylbutyl 2-methylbutyrate) in each sample.
## 3 ML-Based Routing Algorithms in WSNs
### Ensemble-Learning-Based Routing Methods  
Ensemble learning is a powerful tool in ML that builds and combines multiple machine learners to accomplish learning tasks, which generates a set of individual learners and then combines them following a specific strategy. Individual learners are generally common ML algorithms, such as decision trees, neural networks, etc. There are two types of integration: homogeneity and heterogeneity. Homogeneity means that individual learners are all of the same type, and the individual learners in this homogeneous integration are also called base learners. Heterogeneity means that individual learners contain different types of learning algorithms, such as decision trees and neural networks, at the same time.  
In order to predict the best routing protocol for WSN, [PERSON] et al. [73] proposed a scheme for the order of preferences by similarity to ideal solution (TOPSIS), which is a multi-criteria assessment algorithm used to optimize ensemble learners. Then, a multi-criteria TOPSIS-based ensemble framework (MCTOPE) was presented. MCTOPE considers two types of routing protocols: ad hoc on demand distance vector (AODV) and dynamic source routing (DSR). The principle of prediction is to correctly determine the appropriate routing protocol for the data collected by the WSN. Selective training samples are first constructed to train base classifiers from a pool of ML models. The action of routing for training is represented as a set \(P(SP,RP,DP,RA,RO,PDR,APL,TP,DFR,NN)\), where \(SP\) represents the sent packet, \(RP\) represents the received packet, \(DP\) denotes the dropped packet, \(RA\) is the routing agent, \(RO\) represents the routing overhead, \(PDR\) represents the packet delivery ratio, \(APL\) denotes the average path length, \(TP\) represents the throughput, \(DFR\) represents the dataflow rate, and \(NN\) represents the node number.  
The ML models in the candidate pool include decision trees (DT), AdaBoost (AB), random forest (RF), support vector machines (SVMs), probit linear models (PLM), neural networks (NNs), decision stump models (DSMs), and naive Bayesian (NB). MCTOPE continuously performs iterations of combining different models, comparing their scores and retaining the model with the highest score to perform routing protocol selection. MCTOPE ranks the features by the metric of mean decrease Gini (MDG). \(MDG\) is defined as follows:  
\[MDG=\sum_{i=1}^{c_{n}}p_{i}(1-p_{i}), \tag{10}\]  
where \(c_{n}\) is the number of classes in the target variable and \(p_{i}\) is the ratio of each class.  
In general, MCTOPE divides routing protocols into different types and predicts the best routing protocols considering various factors, such as energy efficiency, which can effectively extend the lifetime of WSNs. However, the learning phase has a high overhead due to the centralized implementation of the framework and the need to continuously select appropriate ensemble models from the sample pool.
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 4 Results  
### Experimental Details  
Our experimental environment is the PyTorch deep learning framework running on Python 3.8, Torch 2.0.1, and Cuda 11.8. The experiments were conducted on an Intel Core i7-13700F (Intel, Santa Clara, CA, USA) with an NVIDIA RTX 3050 OEM (8 G) (NVIDIA, Santa Clara, CA, USA). For image preprocessing, we first resized the image to 256 \(\times\) 256 and performed pseudo-anomaly generation at that resolution. Afterwards, it was changed to 224 \(\times\) 224 by center cropping to fit the input of our model. For the feature extractor, we chose ResNet18 pre-trained on ImageNet (v.3.0). During the training process, we set the learning rate to 0.03 and used the SGD optimizer with a momentum of 0.9 and a weight decay of 0.00003. To optimize the model parameters more efficiently and speed up the training process, we used the CosineAnnealingWarmRestarts [47] scheduler. This scheduler combines the strategies of cosine annealing and periodic learning rate restarts and is able to adaptively adjust the learning rate at different stages of training. The training epoch is set to 256, and the batch size is 64. It is worth noting that since our training goal is to enable the model to correctly distinguish normal and anomalous inputs, we only need to continuously input different normal and anomalous images for the model to learn. Therefore, an epoch in this paper refers to training a batch, where a batch contains both normal, texture-abnormal, and structure-abnormal images.
## 3. Strategical Approach
### Degradation Path and Acceleration Model
The drift \(\eta(s^{\prime}{}_{i})\) at stress level \(s^{\prime}{}_{i}\) can then be re-expressed in terms of \(s_{i}\), as follows:  
\[\eta(s_{i})=\exp(\delta_{0}+\delta_{1}s_{i}) \tag{1}\]  
where:  
\(\delta_{0}=\ln(\delta^{\prime}{}_{0})-\delta^{\prime}{}_{1}/s^{\prime}{}_{0} \text{ },\delta_{1}=\delta^{\prime}{}_{1}(1/s^{\prime}{}_{0}-1/s^{\prime}{}_{M})\) for the Arrhenius model;  
\(\delta_{0}=\ln(\delta^{\prime}{}_{0})+\delta^{\prime}{}_{1}\ln s^{\prime}{}_{0 }\text{ },\delta_{1}=\delta^{\prime}{}_{1}(\ln s^{\prime}{}_{M}-\ln s^{\prime}{}_{0})\) for the power model;  
\(\delta_{0}=\ln(\delta^{\prime}{}_{0})+\delta^{\prime}{}_{1}s^{\prime}{}_{0} \text{ },\delta_{1}=\delta^{\prime}{}_{1}(s^{\prime}{}_{M}-s^{\prime}{}_{0})\) for the exponential model. In the case of two stress variables, the generalized Eyring model, which can easily be standardized by a simple linear form, is utilized as the acceleration model. For instance, [PERSON] [12], [PERSON] and [PERSON] [13], and [PERSON] and [PERSON] [14] employed the generalized Eyring model for the relationship between a parameter and the two stress variables in analyzing ADT data. * Generalized Eyring model: \(\eta^{\prime}\left(S^{\prime}{}_{a,i},S^{\prime}{}_{b,j}\right)=\delta^{ \prime}{}_{0}\exp\left(\frac{\delta^{\prime}{}_{1}}{\delta_{a,i}}+\delta^{ \prime}{}_{2}S^{\prime}{}_{b,j}+\delta^{\prime}{}_{3}\frac{S^{\prime}{}_{b,j}} {\delta^{\prime}{}_{a,i}}\right)\)  
In this acceleration model, \(S^{\prime}{}_{a,i}\) and \(S^{\prime}{}_{b,j}\) represent the different stress variables. To simplify the acceleration model without the loss of generality, the stress levels of each variable, \(S^{\prime}{}_{a,i}\) and \(S^{\prime}{}_{b,j}\), were standardized as \(S_{a,i}\) and \(S_{b,j}\), respectively. The drift \(\eta^{\prime}\left(S^{\prime}{}_{a,i},S^{\prime}{}_{b,j}\right)\) can be rewritten in terms of \(S_{a,i}\) and \(S_{b,j}\), as follows:  
\[S_{a,i}=\frac{1/S^{\prime}{}_{a,0}-1/S^{\prime}{}_{a,i}}{1/S^{\prime}{}_{a,0} -1/S^{\prime}{}_{a,M}},\text{ }S_{b,j}=\frac{S^{\prime}{}_{b,0}-S^{\prime}{}_{b,j}}{S^{\prime}{}_{b,M}-S^{ \prime}{}_{b,0}}\]  
\[\eta\left(S_{a,i},S_{b,j}\right)=\exp\left(\delta_{0}+\delta_{1}S_{a,i}+\delta_ {2}S_{b,j}+\delta_{3}S_{a,i}S_{b,j}\right)=\eta_{ij} \tag{2}\]  
where \(\delta_{0}=\ln\delta^{\prime}{}_{0}+\frac{\delta^{\prime}{}_{1}}{\delta^{ \prime}{}_{a,0}}+\delta^{\prime}{}_{2}S^{\prime}{}_{a,0}+\delta^{\prime}{}_{3} \frac{S^{\prime}{}_{b,0}}{\delta^{\prime}{}_{a,0}}\), \(\delta_{1}=\left(\delta^{\prime}{}_{1}+\delta^{\prime}{}_{3}S^{\prime}{}_{
## 4 Monte Carlo Simulations
### Latin Hypercube Sampling (LHS)  
Stratified sampling techniques can enhance the efficiency of Monte Carlo simulation. Latin Hypercube Sampling [55] has emerged as an appealing alternative to simple random sampling in computer experiments.  
The name \"Latin Hypercube\" is derived from the combination of \"Latin\" (from Latin square) and \"Hypercube\", which refers to the multidimensional equivalent of a cube. A Latin square is an arrangement of symbols in a grid such that no symbol appears more than once in each row and each column. LHS extends this concept to higher dimensions, creating a more sophisticated sampling strategy [56]. The technique is used in experimental design and sampling for simulation studies to ensure a more even and representative sampling of the input space of a model.  
LHS possesses a distinctive characteristic; that is, unlike simple random sampling, it concurrently stratifies across all dimensions of input variables [55; 56; 57]. LHS partitions the distribution of the random variable into n intervals and selects a random sample from each interval, ensuring an unbiased representation of all regions within the distribution [58].  
LHS has been applied in various domains, such as probabilistic-based analysis of mechanically stabilized earth walls [59] and stochastic finite element analysis [60].
## 2 Materials and Methods
### Irrigation Scheduling Management
#### 2.2.2 Irrigation Scheduling Based on Irriframe Model (IrrIMan)  
The approach used for irrigation scheduling was based on the \"Irriframe\" platform developed by the Water Boards Italian Association [11]. This model takes into account the soil, plant, and atmosphere continuum, and calculates the crop water requirement using evaporimetric data. The crop coefficients (Kc) [12; 13] are adjusted based on local information. The input data include the type of crop, geographic location, meteorological and soil data, and the characteristics of the irrigation system used. The system provides information on expected crop evapotranspiration, the next irrigation date, and the amount of water to be distributed. It offers real-time irrigation scheduling, giving daily guidance on how much and when to irrigate crops. Users can fully interact with the system through the web interface [14].
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
###### Abstract  
Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.  
data fusion; sensors; variability; precision agriculture; artificial intelligence +
Footnote †: journal: Sensors