esa-sceva/satcom-chunk-collection huggingface dataset:
# A C-BiLSTM Approach to Classify Construction Accident Reports
## 2 Related Works
### Existing Studies on Accident Narrative Classification  
There are some existing studies in the field of accident classification using machine learning approaches. [PERSON] et al. [35] used an NB-based model to classify the reasons for insurance claims on work-related injuries. The overall accuracy of the model is approximately 90%, but the accuracy of the category of claim on minor injury is somehow decreased. [PERSON] et al. [36] evaluated the aviation safety reports by an SVM-based model and obtained an accuracy rate of 60-96%. [PERSON] et al. [37] proposed a fuzzy Bayesian model to classify the injury reports obtained from the National Health Interview Survey (NHIS) and achieved an accuracy rate of 87%. [PERSON] et al. [38] extracted the scenes of high recurrence Occupational Accident with Movement Disturbance (OAMD) from the narrative texts using a Bayesian network. However, this method requires expert knowledge to pre-process data and it is time-consuming. [PERSON] et al. [29] classified building quality complaint reports using a CNN model, and the weighted average of F1 values is 0.73, which is superior to the results of traditional machine learning algorithms such as NB and SVM.  
In the application field of construction accident classification, related studies are very limited. [PERSON] et al. [39] proposed a rule-based automatic content analysis system that automatically extracts attributes and safety outcomes from unstructured injury reports. This system achieved an accuracy rate of 95%, but it showed poor performance when dealing with unexpected situations. Moreover, this approach requires an external dictionary for professional terminologies. [PERSON] [40] used the data obtained from OSHA website to compare the performance of several machine learning algorithms, including SVM, NB, decision trees (DT), linear regression (LR), random forest (RF) and k-nearest neighbor (KNN), in the classification of construction accident reports. The results showed that the SVM-based classifier generated a better F1 value than other classifiers. [PERSON] et al. [16] further proposed sequential quadratic programming (SQP)-based integrated algorithm based on [PERSON] et al.'s work. This combined method achieved a weighted result of 0.68, which is better than the result of a single machine learning algorithm.  
It is not difficult to find that although there are some studies on the classification of construction accidents by traditional machine learning algorithms, there is still a lack of research on the application of DL algorithms in this field. Therefore, this study is aimed to evaluate the performance of the DL algorithm in the automatic classification of construction accident narratives.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 5 Experiment Results
### Comparative Algorithm and Performance Metrics
In addition, our evaluation criteria also include EQ (task execution quality, where higher values indicate better completion), UQ (execution uncertainty, where lower values indicate smaller errors or delays), and SR (success rate, measured as the ratio of executed tasks to total tasks, expressed as a percentage).
# Lifting Activity Assessment Using Kinematic Features and Neural Networks
## 1 Introduction
Recently, besides sEMG parameters, kinematic data have been used to calculate indices dealing with mechanical lifting energy consumption; those indices have been proven to be proportional to the LI and also correlated with forces that determine injuries at the L5-S1 joint, so showing an ability in discriminating all the risk condition pairs [34]. Supported by this last study, here we hypothesize that kinematic features as ANN inputs could predict the risk levels while performing controlled lifting tasks with different LI values identified by the NIOSH equation [7; 8] in a controlled environment. The aims of this study were to verify the sensitivity of kinematic data to the risk level and to test the ability of machine-learning techniques (ANNs) to map kinematic features on LI levels so leading to a reliable biomechanical risk estimation.
## 5 Understanding Decentralised Identity Systems
### Sovrin
#### 5.5.2 Limitations  
Despite its robust architecture and capabilities, the Sovrin Network has some limits. One of the key issues is its reliance on established organizations, such as stewards, who serve as the intermediary between consumers and the distributed ledger. While this strategy improves security and governance, it adds a layer of complexity and dependency, potentially undermining the network's claim to be truly decentralised. Furthermore, Sovrin's scalability, portability, and connection with other identity systems are still in early phases of development, raising worries about broad acceptance. In addition, certain users may struggle to handle the Sovrin Network's complicated design and governance mechanism [65; 66].
###### Abstract  
The integrated multi-satellite retrievals for the global precipitation measurement (IMERG) data, which is the latest generation of multi-satellite fusion inversion precipitation product provided by the Global Precipitation Measurement (GPM) mission, has been widely applied in hydrological research and applications. However, the quality of IMERG data needs to be validated, as this technology is essentially an indirect way to obtain precipitation information. This study evaluated the performance of IMERG final run (version 6.0) products from 2001 to 2020, using three sets of gauge-derived precipitation data obtained from the Integrated Surface Database, China Meteorological Administration, and U.S. Climate Reference Network. The results showed a basic consistency in the spatial pattern of annual precipitation total between IMERG data and gauge observations. The highest and lowest correlations between IMERG data and gauge observations were obtained in North Asia (0.373, \(p<0.05\)) and Europe (0.308, \(p<0.05\)), respectively. IMERG data could capture the bimodal structure of diurnal precipitation in South Asia but overestimates a small variation in North Asia. The disparity was attributed to the frequency overestimation but intensity underestimation in satellite inversion, since small raindrops may evaporate before arriving at the ground but can be identified by remote sensors. IMERG data also showed similar patterns of interannual precipitation variability to gauge observation, while overestimating the proportion of annual precipitation hours by 2.5% in North America, and 2.0% in North Asia. These findings deepen our understanding of the capabilities of the IMERG product to estimate precipitation at the hourly scale, and can be further applied to improve satellite precipitation retrieval.  
IMERG; spatial pattern; error analysis; diurnal variation; interannual variability 2024 IEEEexample:BSTcontrol  
1  
P.: [PERSON] The Performance of GPM IMERG Product Validated on Hourly Observations over Land Areas of Northern Hemisphere. _Remote Sens._**2024**, _16_, 4334. [[https://doi.org/10.3390/rs16224334](https://doi.org/10.3390/rs16224334)]([https://doi.org/10.3390/rs16224334](https://doi.org/10.3390/rs16224334))  
Academic Editor: [PERSON]  
Received: 29 September 2024  
Revised: 5 November 2024  
Accepted: 16 November 2024  
Published: 20 November 2024
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 5 Adversarial Ant Colony Optimization Algorithm for Solving Non-TT Routing Problem
### Definition of the Optimization Problem in Non-TT Routing
#### 5.3.3 Pheromone Update Rules  
In each time window, the pheromone \(\tau_{ij}^{k}(t)\) was volatilized in a fixed ratio. At the same time, the pheromone concentration was increased on the edges, according to the route traveled. For each ant in the \(k\)th colony:  
\[\tau_{ij}^{k}(t+1)=(1-\rho)\tau_{ij}^{k}(t)+\Delta\tau_{ij}^{k}(t), \tag{16}\]  
where \(\rho\) is the volatility coefficient (preset between 0 and 1), \(\Delta\tau_{ij}^{k}(t)\) is the sum of the pheromone increments in the \(k\)th population, which is accumulated by the pheromone increment of each ant \(\Delta\tau_{ij}^{kl}(t)\), and \(Q\) is a preset pheromone increment constant. \(\Delta\tau_{ij}^{kl}(t)\) was calculated by:  
\[\Delta\tau_{ij}^{kl}(t)=\begin{cases}Q/c_{src_{k},dst},&\text{$ tour(i,j)\in tour$ $\omega_{l}(v_{src_{k}},\dots,v_{dst})$}\\ 0,&\text{$otherwise$}\end{cases}. \tag{17}\]  
Compared to existing ACO algorithms, the main improvement of the ACO in this paper was the calculation of pheromone increment \(\Delta\tau_{ij}^{k}(t)\). The pheromone increment of each ant colony was calculated in decreasing order of priority. When calculating the \(k\)th pheromone increment, the pheromone increments of the previous \(k-1\) ant colonies were taken into account. Ant colonies with higher priorities had a stronger suppression effect on the current colony. Therefore, when we calculated \(\Delta\tau_{ij}^{k}(t)\), the sum of the pheromone increments of stronger ant colonies on the path to \(v_{j}\) had a proportional negative impact on the current pheromone increment. \(\Delta\tau_{ij}^{k}(t)\) was calculated by:  
\[\Delta\tau_{ij}^{k}(t)=\begin{cases}\sum\limits_{l=1}^{m}\Delta\tau_{ij}^{kl}( t)-\gamma\sum\limits_{s=p_{k}+1}^{max(p)}\Delta\tau_{sj}^{s}\frac{s\cdot pck_{k}}{ \sum\limits_{k=1}^{max(p)}h\cdot pck_{k}},&\text{$if $>0$}\\ 0,&\text{$otherwise$}\end{cases}, \tag{18}\]  
where \(\gamma\) is a preset constant whose purpose is to adjust the influence of stronger colonies on the current colony, \(\Delta\tau_{sj}^{s}\) is the sum of pheromone increments on the edges of all paths whose priority is \(s\) and whose destination is \(v_{j}\), and \(max(p)\) is the maximum value of priority \(p\). The colonies with higher priorities than the \(k\)th colony were \((p_{k}+1,\dots,max(p))\).
# Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
## 4 Modeling Procedure
### Dt  
The learning results with the DT algorithm depend on the algorithm's parameters chosen to optimize the learning process. DT was developed using the SciKit library in Python, and the Graphviz library was used for visualization. There were three splitting criteria available to measure the quality of splits (i) mean square error (MSE), which is the same as variance reduction as a criterion for feature selection, (ii) Friedman-MSE, to assess potential splits, [PERSON]'s improvement score is used with the mean squared error [90; 91], and (iii) Poisson, a process that finds splits by reducing Poisson deviance [92]. Because there is no specific rule to choose between the splitting criteria, a trial and error procedure is needed for every problem to select the best one. Hence, after a trial and error process, the MSE criterion was chosen as the best splitting criterion.  
In terms of splitting internal nodes, the minimum sample split represents the minimum number of samples required to break an internal node. Because of the importance of this parameter, we specified it as equal to two and allowed the tree to grow and reach the highest accuracy. There must be a minimum number of samples at the terminal node, as specified in the minimum sample leaf. To allow the tree to grow as accurately as possible, this parameter was set to one. To prevent a tree from overgrowing and overtraining, tree depth (TD) plays an important role. In most studies, this parameter is chosen as one of the main parameters to select the best tree among developed trees [93]. By increasing the TD, the danger of the algorithm's memorization of data will increase; therefore, it would be better to use a lower depth. Using the mentioned parameters, a DT was developed for flyrock prediction. The optimal values of DT parameters for the generation of flyrock induced by blasting are shown in Table 5. In addition, Figure 7 shows the visualization of the trained DT for flyrock prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Hole Depth** & \multirow{2}{*}{**Spacing (m)**} & \multirow{2}{*}{**Burden (m)**} & \multirow{2}{*}{**Stemming (m)**} & **Powder Factor** & \multirow{2}{*}{**Flyrock (m)**} \\  & & & & & (kg/m\({}^{3}\)) & \\ \hline
1 & 13.5 & 5.5 & 5 & 4.5 & 0.23 & 15 \\
2 & 13 & 5 & 4 & 4.1 & 0.33 & 36 \\
3 & 12 & 4 & 3 & 3.6 & 0.24 & 48 \\
4 & 10  
Using an ad-hoc ranking, DT can evaluate the input variables based on their importance. Using this ranking system, more important variables become closer to the root node and play an important role in tree splitting and predicting target values.
## 2 Literature Review
### Reliability of Delivery  
The reliability of delivery is addressed in greater detail by a survey of the related works regarding the d-MP, transport optimization, and reliability of delivery:  
1. The reliability of delivery regarding the d-MP There are numerous works focusing on the reliability of delivery regarding the d-MP that consider different conditions, such as a two-terminal network [4], the delivery time [12], the turning angle [19], and various algorithms, including a method based on the Monte Carlo simulation method [5], a recursive algorithm [7], a new direct algorithm [9], etc.
2. The reliability of delivery regarding transport optimization Several works on the reliability of delivery regarding transport optimization are solved by numerous approaches, such as simplified swarm optimization (SSO) [13], real-time management [31], the path-based method [32], social engineering optimization (SEO), a combination of firefly and the simulated annealing (SA) approach [33], and production functions [34].
3. The reliability of delivery Moreover, many related works on the reliability of delivery were studied, such as the production network [14], allocation design [16], vehicle routing planning [35], task scheduling [36], power systems [37], social networks [38], the binary-state network [39], and the supply chain [40].
## 3 LSTM and FSST-Based Fault Diagnosis Algorithm
### Data Preprocessing
Higher frequencies correspond to shorter time periods, while lower frequencies correspond to longer periods. Time-frequency relationships in time-series data explain the dynamic nature of the data; analyzing these data as a whole provides a more detailed interpretation of the data characteristics. The FSST is a mathematical tool for analyzing the dynamic characteristics of frequency modes in the frequency-time domain of time-series data. Equation (3) shows the mathematical expression of the FSST. In this expression, \(\omega\) represents the frequency, ranging continuously from \(-\infty\) to \(\infty\). \(t\) represents time, which indicates the time domain of the given time-series data. \(\phi(t,\omega)\) is the time-frequency distribution, also known as the time-frequency kernel or window, obtained from the FSST. One of the main features of FSST is that the kernel is used to reconstruct the signal in the frequency-time domain while preserving the frequency mode information of the original signal. \(S(\omega,t)\) represents the result of the FSST, which captures the frequency modes of the time-series data in the frequency-time domain. Figure 3 provides a visualization of the time-series data after normalization, illustrating how the FSST enables the extraction of dynamic frequency content. These data were collected over a one-month period from a remote agricultural site, focusing on soil conditions. The mathematical normalization process adjusts the data scale to ensure consistency and reliability across a variety of environmental conditions. This enhances the tracking and analysis of temporal variations in frequency modes, enabling precise monitoring of changes within the time-series data. By standardizing the data, the model gains the improved accuracy and generalization essential for effective real-time smart agriculture applications. Figure 3: Data normalization of time-series data.
# Two-Stage Configuration of User-Side Hybrid Energy Storage Based on Fuzzy Optimization
## 3 Two-Stage Optimization Algorithm Based on Fuzzy Optimization
### Two-Stage Optimization Algorithm Based on Gurobi  
In summary, the specific steps of the two-stage algorithm for optimizing the configuration in this paper are as follows.  
1. Input photovoltaic output data and load data
2. Determine whether the fluctuation of output data meets the grid-connected requirements, and if it meets the requirements, move to step 5. If not, adjust the fuzzy parameters, increase the investment of the supercapacitor, and return to step 2 to recalculate the filtering effect.
3. If the requirement is not met, calculate the fuzzy parameters from the viewpoint of users and power grid.
4. Establish a fuzzy optimization model to optimize the configuration of supercapacitors, verify whether the photovoltaic output after capacitance configuration meets the requirements, and return to step 2 if it does not meet the requirements.
5. Calculate user loads with the superimposed photovoltaic output if the requirements are met.
6. According to the overlapped load, establish the optimal battery allocation model under a two-part tariff.
7. Use _GUROBI_ to solve the model and obtain the optimal configuration scheme of hybrid energy storage in the photovoltaic scenario.  
**Remark 1**.: _To achieve the two goals of reducing charging and discharging cycles and improving the user's income, rewrite the battery optimization target as follows._  
\[Max:\frac{C_{\text{invest2}}}{N} \tag{35}\]  
**Remark 2**.: _To successfully solve the problem with GUROBI, as many nonlinear terms in the objective function need to be removed as possible. Therefore, the inflation term in objective functions (14), (15), and (18) is removed to simplify the optimization process. After the optimization, the results are added for analysis._
# Investigation and Design of the Transonic Laminar Flow Characteristics in a Laminar Aircraft
## 4 Results and Discussion of Airfoil Design
### Design Results  
The calculated states used by \(OLD\) and \(NEW\) are listed in Table 7. The results of the \(C_{p}\) curve in the central experimental wing are displayed in Figure 25. These results show that a weak shock wave appears at 50% chord length of the \(NEW\) airfoil at \(\alpha=2^{\circ}\). The \(C_{p}\) shape in front of the shock wave of the \(NEW\) does not change significantly compared with the \(C_{p}\) curve of \(OLD\). However, the value of \(C_{p}\) decreases, which benefits the increase in the \(C_{L}\). The position of the negative pressure peak is moved from 60% to 50% of the chord direction, which further increases the aerodynamic load on the rear part of the airfoil. The aerodynamic load after a 50% chordwise position on the lower surface of the \(NEW\) airfoil is obvious. Therefore, the whole area enclosed by the \(C_{p}\) curve is significantly increased. In short, the shape of the \(C_{p}\) curve matches well with the design intent. In addition, the favorable pressure gradient segment from the leading edge of the airfoil to 50% of the chord length on the \(OLD\) upper surface is well inherited by \(NEW\). This determines the laminar flow characteristics of the \(NEW\) on the upper surface. It can be found in Figure 26 that the length of the laminar flow zone of \(NEW\) does not change significantly in comparison with the \(OLD\). It is worth noting that there is no difference at all in the transition positions of the \(OLD\) and \(NEW\) airfoil types at \(\alpha=0^{\circ}\). When \(\alpha\) is \(1^{\circ}\) or \(2^{\circ}\), a slight delay in the transition position of \(NEW\) is produced in comparison with \(OLD\). Because the weak shock wave design slightly extends the length of the favorable pressure gradient segment of the airfoil, increasing the flow stability in front of the shock wave. Therefore, the laminar flow characteristics are slightly improved when the shock wave appears.  
Through the analysis above, the prerequisite of the design of this chapter inheriting the laminar flow characteristics is well achieved. The transition position is not advanced by airfoil design. From the \(C_{L}\) curve shown in Figure 27a, the \(C_{L}\) has a more obvious increase after the aerodynamic load on the rear part of the airfoil increases. The \(C_{L}\) increases by about 0.28 in the range of 0\({}^{\circ}\)to 3\({}^{\circ}\)of \(\alpha\), which greatly reduces the load burden on the outboard wing. This will provide a reference for laminar aircraft design. The \(C_{D}\) pole curve intuitively shows the improvement of the lift-drag characteristics near the cruise \(\alpha\) = 1\({}^{\circ}\)after increasing the aerodynamic load on the rear part of the airfoil. Although the \(NEW\) has the larger nose-down pitch moment compared to the \(OLD\), about 0.08, this increment has little effect on the flat tail trim of the aircraft.
# Non Data-Aided SNR Estimation for UAV OFDM Systems
## 2 Signals Model of UAV OFDM
### The OFDM Model over UAV Channel  
In an OFDM system, the \(m\)th transmitted signal sample [25] is given by  
\[x_{m}(n)=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}X_{m}(k)e^{j\frac{2\pi k}{N}(n-N_{c} )},\qquad n=0,1,\ldots,N+N_{c}-1, \tag{2}\]  
where \(X_{m}(k),k=0,1,\ldots,N-1\), is modulated data of the \(m\)-th OFDM symbol, \(N\) and \(N_{c}\) are the total number of sub-carriers and the size of cyclic prefix (CP), respectively. Suppose \(X_{m}(0),\ldots,X_{m}(N-1)\) are independent and identically distributed random variables.  
Figure 1: Unmanned Aerial Vehicle (UAV) Orthogonal frequency division multiplexing (OFDM) system model.  
The OFDM model under UAV channel conditions is shown in Figure 2. To avoid the ISI, \(L\) is assumed to be less than \(N_{c}\). Now supposing that the symbol timing and frequency offset have been synchronized, then the received sampling signal transmitted through multipath channel can be given by  
\[\begin{split} y_{m}(n)&=\sum_{l=1}^{L}h_{l}x_{m-1}(N_{ c}+N+n-l)U(l-n-1)+\sum_{l=0}^{L}h_{l}x_{m}(n-l)U(n-l)+v_{m}(n),\\ n&=0,1,\ldots,N+N_{c}-1,\end{split} \tag{3}\]  
where \(U(\cdot)\) is the step function, \(v_{m}(n)\)\(\sim\)\(N(0,\sigma^{2})\) represents additive white Gaussian noise (AWGN) with mean 0 and variance \(\sigma^{2}\) and \(E\left[v_{m}(n)v_{m}^{*}(n+\tau)\right]=\sigma_{v}^{2}\delta(\tau)\).
## 4 Performance Analysis  
### Throughput  
The throughput of the proposed protocol is defined as the total number of packets successfully transmitted in each slot, expressed as \(T\). When the system is in a steady state, the number of newly arrived packets is equal to the number of packets successfully sent. Therefore, the throughput of the system in a steady state can be expressed as  
\[T=p_{f}\cdot(M-N_{a}), \tag{12}\]  
where \(N_{a}\) represents the average number of backlogged users when the system reaches a steady state, that is  
\[N_{a}=\sum_{N=0}^{M}\pi_{N}(p_{f},p_{r})\cdot N, \tag{13}\]  
where \(\pi_{N}(p_{f},p_{r})\) is the steady-state probability \(\pi_{N}(0\leq N\leq M)\).  
Take Equation (13) into (12) to obtain the first expression of throughput. Moreover, in a steady state, the number of ACK replies is equal to the average of the packets successfully sent by the sender. The receiver sends an ACK response if the system is a true success (TS) or a pseudo success (PS). When the system has \(N\) backlogged users, the probability of receiver feedback and ACK responses is  
\[\begin{array}{l}P_{ACK}(N)=P_{TS}(N)+P_{PS}(N)\\ =P_{f}(1,N)P_{r}(0,N)+P_{f}(0,N)P_{r}(1,N)+P_{f}(3,N)P_{r}(0,N)\\ \quad\quad+P_{f}(0,N)P_{r}(3,N)+P_{f}(1,N)P_{r}(2,N)+P_{f}(2,N)P_{r}(1,N)\\ \quad\quad+P_{f}(2,N)P_{r}(0,N)+P_{f}(0,N)P_{r}(2,N)+P_{f}(1,N)P_{r}(1,N).\end{array} \tag{14}\]  
Based on the above analysis and Equations (12)-(14), the throughput of the system in a steady state is  
\[T=\sum_{N=0}^{M}\pi_{N}(p_{f},p_{r})P_{ACK}(N)=\sum_{N=0}^{M}\pi_{N}(p_{f},p_{ r})[P_{TS}(N)+P_{PS}(N)]. \tag{15}\]  
As shown in [11], we let the length of a packet be \(L_{p}=800\)_bits_. Considering the length of the signature code, the actual throughput of the system can be expressed as  
\[T_{act}=\frac{L_{p}-L}{L_{p}}T, \tag{16}\]  
where \(L\) represents the length of the signature code and \(T\) is the total throughput of the system.