esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk Assessment of Lift-Jacking Accidents Using FFTA-FMEA
## 2 Literature Review
### FftA, FMEA Theory Review
After years of cooperation, the AIAG/VDA (Automotive Industry Action Group/Verband der Automobilindustrie) FMEA Handbook recommends using AP (Action Priority) fields in place of the RPN in FMEA in 2019 [21]. The edition adopts AP to classify failure modes into H (High-risk), M (Medium-risk), and L (Low-risk). The AP method focuses on the S-value and respective impact of S, O, and D. It considers S, O, and D in order and subdivides all the corresponding situations. [PERSON] found that the AP method is reasonable and appropriate by performing a differential analysis of the old RPN and the new AP in the case [22]. It also proves that AP is beneficial to error-proof. Overall, the AP method is an improvement on the traditional RPN method. A recent development in risk assessment in the industry is the refinement and extension of existing classical methods through mathematical methods. [PERSON] proposed a new FMEA classification method, which avoids the interactive effects of simultaneous analysis of risk factors by combining them in pairs for risk evaluation [23]. [PERSON] retained the original applicability of the conventional FMEA and the three conventional risk variables and added a fourth variable to consider risk mitigation capability [24]. [PERSON] added a consistency check for expert judgements in the application of FFTA [25]. Another typical development is the combination of a risk assessment method with another method (or even more). [PERSON] proposed the Partial Risk Map Methodology, which combines FMEA and the Risk Matrix risk assessment method to perform proactive assessments and give predictions [26]. [PERSON] combined the MCMD method based on hesitant fuzzy sets with the FMEA method to effectively address expert hesitation in assessment [27]. [PERSON] used FFTA and improved AHP to make an important analysis of fire and explosion accidents in steel oil storage tanks [28]. Its purpose is to leverage the strengths of their respective risk assessment methods and achieve complementarity. In fact, its reliability, validity, practicality, and applicability have increased significantly over the decades. The FFTA-FMEA method proposed in this study is an effective combination of qualitative and quantitative methods, which integrates the FFTA and FMEA methods. The FFTA allows for the precise identification of incident factors in lift-jacking incidents and effectively removes the ambiguity and uncertainty of the risk factors. The FFTA establishes a numerical link to the FMEA through the results of the quantitative analysis, and the FMEA using the AP method enables in-depth risk analysis and risk quantification from multiple perspectives. The specific integration aims to perform a more comprehensive and unbiased analysis and assessment of lift-jacking accidents. In addition, the weighting method incorporated in FFTA effectively eliminates uncertainty and subjective influences between experts due to various factors such as experience and education, thus standardizing expert opinion.
## 4 Results and Discussion
### Component Criticality  
Figure 14 shows the component criticality depending on the design stages. The component criticality shows the important component of the availability, and it is the ratio of the component's failure time to the system failure time. The most crucial component at Stage I was the heater, which accounted for 90.3% criticality. The heater and ball valve (condensate) were critical in Stage II and Stage III. The heater and ball valve (condensate) had 50.7% and 20.0% criticality at the design Stage III, respectively. The preventive maintenance occupied about 10% on the criticality at Stage III. The most critical component was the heater regardless of the design stages. The availability of the system can be significantly increased as installing redundant heaters. The results of the component  
Figure 12: Conversion method to transfer random number to value of time at cumulative distribution.  
criticality analysis guide a designer or a decision maker to select additional components to installed to increase availability.
## 4 Discussion
### Proposed Approach for LoRaWAN Network Deployment  
When we can configure a LoRaWAN network of thousands of devices over a range of several km with the least power consumption, we can define some different application-oriented demands--namely data acquisition assurance, data acquisition frequency, and data size--which all are different aspects of Quality of service (QoS), as long as a hybrid solution for serving applications based on data-level requirements such as the distinction of data and so in EDs level QoS. The level of data acquisition assurance is not directly connected with ACK and retransmissions, as it could be, but to the PDR cut-off which is acceptable for our needs. For example, this cut-off could be 0.50 if our application is not precise centric and we do not care if we lose one out of two messages.  
By following the below application-wide clustering approach method, we have gathered some great insights. We have achieved greater than 0.90 PDR on a large network (6000 EDs) on a wide coverage (10 km) with a data acquisition frequency range from 10 min to 12 h and medium to very high data acquisition assurance.  
The proposed clustering approach is as follows:  
1. Count the overall EDs that are needed;
2. Deploy as many GW as are needed to avoid as much as possible the DR0 allocation that could be necessary for distantly deployed nodes. Do not exceed 900 nodes per GW channel [31].
3. Separate the EDs into several clusters based on the distance from the closest gateway. The clusters should be in donut shape with the inner cycle being the shortest distance away and the outer being the furthest;
4. If there is not an application-wide QoS level, further distribute the EDs in each distance cluster to several new clusters based on QoS aspects (data acquisition assurance, data acquisition frequency and data size);
5. Distribute the EDs for each of the previous clusters to DR-TX groups based on the following criteria: (a) The larger the distance and data acquisition frequency, the larger the DR that should be allocated, while being proportional to the acknowledgment and amount of retransmissions, (b) The higher the DR, the higher the TX power needed to simultaneously achieve the best trade-off between coverage and power consumption, (c) The more EDs in the same cluster, the higher the DR allocation should be, (d) Exhaust the distance limits of each DR cluster in order to use as many DRs as possible, while the DR5 cluster should be configured to around 64% of the total EDs [32];
6. Limit the distance-based clusters to equalize as much as possible the maintenance intervals, or distribute the final clusters according to power needs to determine which power capacity configuration should be used in each case (if this is a possible capability for the deployment).
# An IoT-Based System for Efficient Detection of Cotton Pest
## 3 Results and Discussion
### Simulation Results
#### 3.2.2 Two-Hop Scenario  
As single-hop scenes were applied to the simulations, the creation of a multi-hop environment was also attempted. The single-hop field was small and thus it took less time to reach the mobile node bug-infested area and less time to take action. Nevertheless, because the field of the multi-hop environment covered a relatively large area and one hop had been added to the communication design, the response time inevitably increased.  
These two parameters were directly proportional to the fact that when the size of the field was increased, the hop was also increased. With the increase in the hop, the response time would also increase. In the single-hop, the field was to be kept small while the size of the field in the multi-hop was increased, but the response time of the drones was also increased by a few milliseconds. The snapshots in Figure 13A,B show scenes in which the cluster had increased and the coverage area of the drones from a multi-hop environment also increased.  
The results show that every time a pest entered a sensing range of a sensor node, it was detected, and a detected message was sent to the sink node as shown in Figure 14. Initial results from the simulation study indicated the suitability of the proposed pest detection system to be deployed in the agriculture field.  
Figure 11: Whenever a pest entered the sensing range of a sensing node, the simulator highlighted it with yellow color.
## 5 Q-Leaning Based Data Caching
Then, the reward function is determined as  
\[\psi(m_{st},a_{st})=v_{st}^{f^{+}}-v_{st}^{f^{-}}. \tag{17}\]  
Then, the relationship between value function \(V_{s}\) and Q-value is as follows:  
\[V_{s}=\max_{a_{st}\in\mathcal{A}}Q_{s}(m_{st},a_{st}). \tag{18}\]  
If for every state-action pair an optimal Q-value is known, then an optimal policy is determined as  
\[V=\sum_{t=0}^{\infty}\alpha^{t}\psi^{t}, \tag{12}\]0  
\(Q(m,a)\) is determined by the Q-learning algorithm in a recursive manner by utilizing the update rule for SBS [34]. \[V=\sum_{t=0}^{\infty}\alpha^{t}\psi^{t}, \tag{12}\]1 \[V=\sum_{t=0}^{\infty}\alpha^{t}\psi^{t}, \tag{12}\]2  
where \(\alpha\) represents learning rate, \(m_{s}+a_{s}\) is the state transition from state \(m_{s}\) as a result of an action \(a_{s}\) at time \(t\), where \([V_{s}^{t}(m_{s}+a_{s})=max_{a_{s}}Q_{s}^{t}(m_{s},a_{s})]\). The details of pseudo code of the proposed Q-learning based caching algorithm is provided in Algorithm 1. First, state-action pairs are initialized for the Q-learning caching mechanism. Then, actions \(a_{s}\) are selected by iterative evaluation of state-action pairs to maximize the Q-function. As a result of the selection of better actions, the system rewards are enhanced according to Equation (18). Thus, SBS \(s\) can cache contents that can potentially improve long term reward. In steps 9 and 10, the reward is achieved and system transition to a new state occurs. Finally, the Q-table is updated based on the rewards achieved. The proposed scheme evaluation has a complexity \(\mathcal{O}(|\mathcal{M}|^{3})\). Moreover, Q-values are updated in each iteration; therefore, the proposed scheme update has a complexity \(\mathcal{O}(|\mathcal{A}||\mathcal{M}|^{2})\). ```
1:Initialize Q-value \(Q_{s}(m_{st},a_{st})\) for every state-action pair. 2:for\(\mathcal{T}=1,2,\ldots,t,\ldots,T\)do
3: Choose a random probability \(p\). 4:if\(p\geq\epsilon\)
5:\(a_{st}=\operatorname*{arg\,max}_{a_{st}\in\mathcal{A}}Q_{s}(m_{st},a_{st})\),
6: otherwise,
7: randomly select an action \(a_{st}\). 8: Execute action \(a_{st}\) in the system. 9: Obtain reward \(\psi_{st}\). 10:\(m_{st}\) transits to the next state \(m_{s(t+1)}\) and enter the next interval. 11: update, \(Q_{s}^{l+1}(m_{st},a_{st})=(1-\psi)Q_{s}^{t}(m_{st},a_{st})+\) \(\psi(\psi_{st}(m_{st},a_{st})+\alpha V_{s}(m_{st}+a_{st}))\),
12:endfor
```  
**Algorithm 1** Q-Learning based Data Caching at SBS level.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 2 Sensing Methods
### Sparse Dictionary Sensing  
An important topic for sparse-representation research is signal sparse representation under a redundant dictionary. The current sparse representation of signals under redundant dictionaries focuses on the construction of sparse dictionaries, and the design of fast and efficient sparse-decomposition methods. Conventionally, sparsifying dictionary learning aims to construct a proper dictionary \(\Psi\) and a matrix \(s\) to minimize sparse-representation errors. On the basis of Equation (2), the sparse-representation error is defined as follows:  
\[E\triangleq x-\Psi s\ \ \ subject\ to\ \ \parallel s(:,k)\ \parallel_{0}\leq K, \forall k, \tag{6}\]  
where \(K\) is the sparsity of \(s\). In the design of CS systems, besides dictionary \(\Psi\), another important aspect is to choose a suitable sensing matrix \(\Phi\) that can accurately construct original signal \(x\) by measurement \(y\). \(\Theta=\Psi\Phi\) in Equation (3) shows that the sparsity of measurement \(y\) is also an important aspect influencing the reconstruction accuracy of \(x\).  
[PERSON] et al. embedded a sensing matrix into the problem of sparse dictionary learning, and proposed an alternative optimization strategy [9]. Previously, [PERSON] et al. had proposed a similar  
Figure 2: Process of simplified compressed sensing (CS). Note: \(s\), sparse vector of \(x;y\), measurement vector; \(\Phi\Psi\), sensing or measurement matrix; and \(M<N\).  
framework [10]. In [9], [PERSON] et al. optimized the problem of sparse dictionary learning by embedding a measurement matrix. The optimization process is as follows:  
\[\left\{\begin{array}{l}min_{\Phi,\Psi,s}\parallel A-B\Psi s\parallel_{F}^{2} \triangleq\digamma(\Phi,\Psi,s)\\ \parallel s(:,k)\parallel_{0}\leq K,\forall k,\Phi=f(\Psi)\end{array}\right., \tag{7}\]  
where  
\[A\triangleq[\sqrt{1-\alpha}\Phi X,\sqrt{\alpha}X]^{T},B\triangleq[\sqrt{1- \alpha}\Phi,\sqrt{\alpha}I_{N}]^{T}. \tag{8}\]  
Both \(A\) and \(B\) are independent of \(s\). Sensing matrix \(\Phi\) was denoted as \(\Phi=f(\Psi)\) because it was decided by a given sparsity matrix \(\Psi\), which is also called dictionary \(\Psi\). The authors further proposed an optimized measurement matrix and a new algorithm to solve the corresponding optimization problem.  
Another novel dictionary-based approach was proposed and applied in diffusion-tensor imaging (DTI) [11]. It combined adaptive dictionaries and \(T_{2}\)-weighting correction to form a compressed-sensing framework to reconstruct undersampled DTI data. This method could improve spatial resolution, the flexibility of the diffusion protocol, and application feasibility.
## 4 Case Studies
### Probabilistic Forecasting of ALSC
#### 4.2.4 The Impact of Power Forecasting on Probabilistic ALSC  
In addition to the fact that two types of power imbalance allocations affect the results of probabilistic ALSC, the forecasting accuracy of wind power, photovoltaic power and load power are influential as well. In order to study the impact, eight different forecasting models mentioned in Table 6 (LGBM, XGB, RR, SVR, KNN, LR, LSTM and Stacking ensemble learning model) are used to predict the wind power, photovoltaic power and load power at the same time point, respectively. The probabilistic forecasting results are used to calculate the probabilistic ALSC. Correspondingly, the probability density functions (PDFs) of ALSC obtained by different forecasting models are shown in Figure 12.  
From Figure 12, it can be seen that the PDFs of ALSC with different forecasting models are evidently different, thus demonstrating that the different prediction accuracy of forecasting models have significant influence on the results of probabilistic ALSC. More specifically, in Figure 12a, the PDF of XGB is most similar to the PDF of Stacking. Compared with the PDF of stacking, the PDFs of LGBM and RR have lower sharpness, and the tail of the PDF of RR is thicker. In Figure 12b, the PDF of LSTM is most similar to the PDF of Stacking. The sharpness of the PDFs of SVR, LR is apparently lower and the PDF of KNN is more right-skewed. In addition, according to Table 6, it can be found that the two models with the highest accuracy closest to the stacking ensemble learning model in the wind power, photovoltaic power and load power forecasting: XGB and LSTM can also obtain the PDFs closest to stacking ensemble learning model.  
Figure 11: The power variation of generators during load growth based on modified IEEE 39-bus systems: (**a**) Generator at bus 31 in Method 3; (**b**) Generator at bus 31 in Method 4; (**c**) Generator at bus 35 in Method 3; (**d**) Generator at bus 35 in Method 4; (**e**) Generator at bus 36 in Method 3; (**f**) Generator at bus 36 in Method 4; (**g**) Generator at bus 37 in Method 3; (**h**) Generator at bus 37 in Method 4; (**i**) Generator at bus 38 in Method 3; (**j**) Generator at bus 38 in Method 4; (**k**) Generator at bus 39 in Method 3; (**l**) Generator at bus 39 in Method 4.
## 1 Introduction
### Research Questions  
Despite the rapid development of image generation technology in recent years, mainstream generative AI models still face significant difficulties in processing traditional patterns with deep cultural semantics [5]. The difficulties in generating phoenix pattern images include chaotic pixel structure, distorted symbolic relationships, ambiguous colour semantics, unstable generation control, etc. The root cause of these problems is the lack of a structural expression mechanism for cultural logic; the current AIGC system lacks the ability to understand and call on the cultural \"semantic gene\". Therefore, to promote the transformation of phoenix pattern images from \"visual inventory\" to \"semantic generation\", there is an urgent need to build a cultural ontology model with structured, semantic, and controllable characteristics and verify its adaptability and expressiveness in actual generation tasks. This study focuses on the relationship between \"cultural semantics-generation logic-platform adaptation\" and proposes the following three research questions:  
_RQ1: How can a multidimensional cultural gene model for phoenix pattern imagery be constructed to represent both its visual features and symbolic meanings?_  
_RQ2: How can ontologically encoded cultural semantics be transformed into effective prompts for AI-based image generation systems?_  
_RQ3: How do different generative platforms compare in their ability to express culturally encoded imagery, and how well does the proposed model integrate with them?_  
These three major questions correspond to the three core paths of this study: theoretical construction, prompt design, and experimental verification, which constitute the logical main line of this study. Through these three explorations, it aims to open up the application path of \"cultural knowledge structure \(\rightarrow\) AI semantic interface \(\rightarrow\) controllable generation practice\" and provide a set of generalizable methodological frameworks and data foundations for the visual regeneration of intangible cultural heritage, cultural intelligent expression, and cross-platform image generation in the AIGC era. It is worth noting that, in this study, the term \"cross-platform\" refers to parallel semantic-controlled image generation experiments conducted on two independent AI platforms\(-\) Midjourney and Dreamina AI. It does not imply an integrated model architecture or data transfer between systems.
# Attention Block Based on Binary Pooling
## 3 System Design
### Example  
As a plug-and-play attention mechanism module, it is highly flexible. One example based on ResNet is shown in Figure 2. The attention module is the non-identity branch of the residual module, and it operates before the summation with the identity branch. This approach allows the module to be integrated into other advanced backbone networks, enhancing the effectiveness of the model.  
Figure 2: The design of the original residual module (**left**) and the ResNet module with attention mechanism (**right**).
## 5 Discussion
### Practicability of the Proposed Estimation Framework  
In the \"Simulation example\" section, we verify the practicality of the proposed estimation framework through an engineering example. In fact, both cyber-attacks and MMs may occur in actual networked control systems, such as unmanned aerial vehicle group control systems and intelligent vehicle networking systems [63; 64]. The estimation framework proposed in this paper is practical as long as the cyber-attacks obey the Bernoulli distribution.
# Numerical Analysis on Spanwise Correlation of Vortex-Induced Force of Split Double-Box Beam
## 4 Results and Analysis
### Three-Dimensional Bypass of the Bridge in Static State
#### 4.2.2 Spanwise Correlation of Surface Pressure Coefficient When the Bridge Is in a Static State  
Two pressure points near the wake area are selected in this section to study the correlation of the pressure coefficient when the reduced wind velocity is 5.65 and 6.21. The measuring points are shown in Figure 8. The variation of the spanwise correlation coefficient of the pressure coefficient with the spanwise spacing at each measuring point is shown in Figure 9.  
Figure 8: The layout of pressure measuring points in the wake area.  
Figure 7: Spanwise correlation of aerodynamic coefficients in static state (_δ/D_: dimensionless spacing). (**a**) Spanwise correlation of lift coefficient; (**b**) Spanwise correlation of torque coefficient; (**c**) Spanwise correlation of resistance coefficient.  
It can be seen in Figure 9:  
* The spanwise correlation coefficient of the pressure coefficient decreases gradually with the increase of spanwise spacing.
* The spanwise correlation of the pressure coefficient of the upstream panel is higher than that of the downstream panel under the same wind speed. The possible reason is that the upstream panel is located on the windward side and the intermediate groove has certain limitations that make the vortex separation and reattachment of the fluid in the wake area after passing through the upstream panel stronger than that of the downstream panel, and as a result, the spanwise correlation of the pressure coefficient of the upstream panel is also stronger.
## 2 Materials and Methods
### Model Calibration  
#### 2.5.1 Pilot Point Placement Design  
Pilot Point Placement is the method of technically defining the emplacement of each pilot point in a groundwater flow model calibration. A pilot point can be placed on top of observations or between them. Wherever possible, increasing the density of the pilot point would be advantageous for the model calibration. Specific rules, such as density, dictate the placement of the pilot point. It should be more significant where there are more observations or high hydraulic head fluctuation, and the gap in the placement is not tolerated [41]. The groundwater flow model calibration was conducted using PEST numerical code based on the inverse modeling with pilot point mode. Inverse modeling could be used to characterize aquifer and aquitard [11]. The spatial distribution of the hydraulic head was resulted by running the model simulation using the parameters generated by the PEST. In this study, The pilot points method combines the zonation that separates the upgradient from the downgradient [42]. We adopt kriging interpolation to interpolate the assigned value of the pilot points to each of the model cells as it gives a better trend in subsurface data [43]. An exponential variogram was used for the entire simulation, parallel to the Gaussian. The simulation provides proper hydraulic conductivity distribution by minimizing the residual over 10 iterations. The simulation could give an unrealistic value to approximate the observation; however, the modeler makes the balance. The PEST calibration allowsregularization opportunities that concede the model to produce a unique solution. The SVD regularization and Tikhonov regularization are available in the PEST suite or PEST++.
## 3 Methods  
Our study focuses on integrating DWD and BBCH phenological observations, crop type data, and geometries with Sentinel-2 ARD time series. We begin by generating high-quality \(NDVI\) time series using a robust filtering approach, termed UE-WS, which combines upper envelope detection (UE) and Whittaker smoothing (WS) to filter out and replace erroneous data points before any phenological analysis. Next, we calibrate and determine optimal thresholds for the detection of different phenological stages. We then assessed the accuracy of the detection results using these chosen thresholds against independent ground observation data. Finally, we mapped the phenological detection outcomes at a regional scale. This flow chart shown in Figure 2 gives a general picture of the methodological approach used in this study.  
Figure 2: Flowchart illustrating the overall methodological workflow of this study.