esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem and Methods
### Sensitive Ant Colony Optimization Method
The local pheromone matrix update is made after each ant moves within each construction step [22]. \[\text{Local update:}\quad\quad\tau_{ij}^{(n)}=\tau_{ij}^{n-1}\cdot(1-\rho)+\rho \cdot\Delta_{ij}. \tag{9}\]  
Notations: \(\rho\) is the pheromone evaporation rate, and \(\Delta_{ij}\) is the artificial pheromone laid on edge \((ij)\). _Globally update the best_ tour's \(PSL\) vector and pheromone matrix \(\tau\). The global update occurs after all ants finish all construction steps. Now, the \(PSL\) vector recording the pheromone sensitivity level for each ant is also updated according to a specified linear formula based on [24; 25]; for this particular problem, the Equation (10) PSL update influenced by the image's intensity values is used. \[PSL=((1-\rho)*PSL+\rho*\Delta_{ij}*\
u(I_{ij}))*\Delta_{ij}+PSL*|1-\Delta_{ij}|. \tag{10}\]  
Furthermore, the best tour is a user defined criterion; it can be the best tour found in the current construction step, or the best tour from the start of the ACO algorithm, or a combination of these two. For ACO, global update of the pheromone matrix [1] is performed as in Equation (11). \[\text{Global update ACO:}\quad\quad\tau^{(n)}=(1-\psi)\cdot\tau^{(n-1)}+\psi \cdot\tau^{(0)}, \tag{11}\]  
where \(\psi\) is the pheromone decay rate. For \(SACO\), the global update is based on its sensitivity feature (Equation (12)). \[\text{Global update SACO:}\quad\quad\quad\tau^{(n)}=\max_{k=1:K}PSL(k)\cdot \tau^{(n-1)}. \tag{12}\]  
The problem solution is obtained after reaching a stopping criteria, such as, for example, a maximal number of iterations.
## 4 Experimental Verification and Analysis
### Experimental Dataset  
After the completion of the data acquisition, a multi-channel dataset was constructed. There were 600 samples for each health state in each view, and 3000 samples for five health states, each sample consisted of 1024 points. The samples of nine views were divided into three datasets according to the motor speed. Datasets A, B and C were vibration signals obtained at constant rotational speeds of 1200 rpm, 1500 rpm and 600 rpm, respectively. Each dataset had nine channels, and the size of each channel was 1 \(\times\) 1024. Then, 20% samples of each health state were randomly selected from the datasets A, B, and C to form the dataset D, which was used to evaluate the stability of the model at the mixing speed. In dataset D, data with the same health status from different rotational speeds were considered as the same type, so the dataset contains five different health conditions at three  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Measuring Point Position** & **Model** & **Direction** & **Sensitivity** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Outer side of planetary gearbox body} & \multirow{3}{*}{CA-YD-3152} & X & 2.01 \\  & & & Y & 1.99 \\  & & & Z & 1.92 \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Inner side of planetary gearbox body} & \multirow{3}{*}{CA-YD-3152} & X & 2.03 \\  & & & Y & 2.09 \\  & & & Z & 2.01 \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Inner bearing seat of planetary wheel output shaft} & \multirow{3}{*}{YD-36D} & Z & 1.88 \\ \cline{1-1}  & & & Y & 1.98 \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{Inner bearing seat of planetary wheel input shaft} & \multirow{3}{*}{YD-36D} & Z & 1.92 \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The acceleration sensor information used in the experiment.  
different motor speeds. Each dataset was divided into a training set and a test set according to the ratio of 8:2, as shown in Table 2.
## 3 Experiment and Results
### Results of Ablation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Algorithm** & **Slim** & **ShareSepHead** & **SPDDEConv** & **C2f_SAC** & **mAP\({}_{50}\)** & **mAP\({}_{50-95}\)** & **Recall** & **Parameters** & **Time** \\ \hline A & - & - & - & - & 0.804 & 0.575 & 0.684 & 15.9 M & 3.15 ms \\ B & \(\surd\) & - & - & - & 0.756 & 0.529 & 0.690 & **9.8 M** & 2.04 ms \\ C & \(\surd\) & \(\surd\) & - & - & 0.828 & 0.562 & 0.779 & **9.8 M** & **1.73 ms** \\ D &  
A. On the other hand, Algorithm G achieved the best detection performance for shaving defects, with an mAP\({}_{50}\) value of 0.905. To more specifically and intuitively evaluate the effectiveness of the improved modules proposed in this study, Eigen-CAM [37] was used to visualize the features extracted from the same layer of each algorithm in the ablation experiment. The visualization results are depicted in Figure 13. The more profound the red part of the heatmap, the higher the degree of focus on that part.
## 3 Existing MSRS Model  
The existing MSRS models developed by [11; 25] were improvements to the conventional single-skilled resource scheduling where activities are delayed when the daily resource requirement exceeds the available resources. The algorithm of [PERSON] et al. [11] was adopted in this study due to the simplicity and easy scheduling procedure. The MSRS model uses resource substitution and heuristic priority rules to resolve resource conflicts thereby minimizing the project delay. Additionally, resource substitution was based on the assumption that workers have been cross-trained and certified to carry out multiple tasks. The substitution rules are formed based on project size, resource skill, and the available resources while the effectiveness of the rules depends on the ability of the project planner/manager to match the less utilized resources with the over-allocated ones. For example, the substitution rule 2R3 = 1R1 means two of resource R3 can replace resource R1 if there is a shortage of one resource R1 so that activity can either start or continue depending on the progress of the project.  
Furthermore, in the development of the schedule using the MSRS model, first, the start and finish times of each activity and project duration are determined using the forward and backward pass method. Next, activities are listed for scheduling based on the desired priority rule (PR) to be adopted. Then, activities are scheduled using the adopted PR. In the process of scheduling, if resource conflict occurs, the multi-skill algorithm is applied based on the resource substitution rule to resolve the resource conflict so that activities are not delayed. However, if the substitution rule does not cover for the shortage of resources, then the activity is delayed until the next scheduling cycle. After scheduling all the activities, an optimal resource schedule with feasible project duration considering the resource constraints is developed while minimizing project delay.  
Moreover, in the existing MSRS model, resource conflicts are resolved based on optimistic and deterministic activity durations without consideration for the risk and uncertainty that can impact scheduled activities. This limits the application of the model considering construction project realities. Hence, in the proposed stochastic MSRS (SMSRS) model, risk and uncertainty were incorporated into the model to generate a realistic schedule.
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 4 Results
### Validation of the Parameter Estimation Method for Indoor Temperature Model  
To ensure the applicability of the parameter estimation method for the indoor temperature model, it was tested with the data of five different buildings presented in [26] where measured indoor temperatures were used for the parameter estimation. Parameters of the indoor temperature model (Equation (1)) were estimated using the method described in Section 3.2 utilizing four 100-h identification data sets and predicting indoor temperatures for four 28-h validation data sets presented in [26]. This amounted to four different parameter sets for all five buildings including the physical constants \(C\) and \(U\). Heating power was then decreased by 30% for the validation periods and indoor temperature was predicted again. The change in indoor temperature was then compared between the two models and results are presented in Figure 3.  
Figure 2: Predictive optimization of the heat demand with two different optimization strategies.  
As in this study, the change in modelled indoor temperature was restricted between \(-0.5\) and \(+1\)\({}^{\circ}\)C. The results for up to 1 \({}^{\circ}\)C absolute change are of interest and are shown in Figure 3. The model whose parameters are estimated using the method described in Section 3.2 overestimates the change in indoor temperature when compared with the indoor temperature model identified and validated in [26]. Therefore, also taking the error of the indoor temperature model into account, it can be concluded that up to 1 \({}^{\circ}\)C changes in the simulated indoor temperature are realistic and safe to make and are not going to compromise the indoor temperature in the buildings.
## 4 Analysis, Results and Discussion
### Incorporating the Noise Data
#### 4.2.1 Data Pre-Processing  
Unlike the mobile phone dataset, the noise data contained missing values: there were in 14,532 observations with the value \(-99\). It was necessary to impute these values.  
Within this analysis, the focus was on the noise sensors placed in nightlife areas. These correspond to RULAEQ0003, RULAEQ0005, RULAEQ0012, RULAEQ0014, RULAEQ0017, RULAEQ0076, RULAEQ0077, which can be found in Figure 13.  
Having discarded all other sensors from the dataset, it was then discovered that many periods were also missing entirely, instead of being identified with an NaN value. It was therefore necessary to identify every missing period and add it to the dataset to prevent breaks in the time series (similar to the process for the mobile phone dataset).  
Lastly, it was necessary to prepare the noise data for them to be comparable with the number of mobile phones in a given grid (C1 variable). The noise data in our study were averaged hourly, while mobile phone counts were recorded every 5 min. This difference in time intervals created a mismatch between the datasets. To address this, we inserted timestamps for each 5-min interval between two consecutive hourly noise measurements. Subsequently, we imputed the missing noise values for these newly added timestamps. This step ensured that both datasets were aligned in terms of timing, facilitating a more coherent analysis.
## 2 Materials and Methods
### Spectroscopic Analysis
#### 2.4.4 Determination of Free Radical Scavenging Potential by DPPH (2,2-Diphenyl-1-Picrylhydrazyl)  
For the evaluation of free radical inhibition potential in beer samples by DPPH, a method reported in ref. [31] was employed, after minor modifications.  
Preparation of the diluted sample was carried out as follows: 13.3 mL of degassed beer was diluted with water to 100 mL at a temperature of 20 \({}^{\circ}\)C in a volumetric flask and the solution was vortexed thoroughly; then 2.5 mL of diluted beer was added to 20 mL of ethanol and the combination was left to stand for 20 min at 20 \({}^{\circ}\)C; afterwards, it was filled up to a volume of 25 mL with attemerated ethanol, the solution was homogenized rigorously, finally the mixture was transferred to a fluted filter, to be filtered, and was retained at 20 \({}^{\circ}\)C until use.  
Preparation of the diluted blank was carried out as follows: 2.5 mL of water was diluted with ethanol in a 25 mL volumetric flask and attempered for 20 min at 20 \({}^{\circ}\)C. The mixture was further filtered through a fluted filter.  
Preparation of the DPPH stock solution was carried out as follows: 0.06 mM of DPPH methanolic solution (0.0024 g DPPH/100 mL methanol) was attemerated for 20 min at 20 \({}^{\circ}\)C. The stock solution should be freshly used. Then, 1.5 mL of diluted sample was added to 1.5 mL of DPPH solution, the mixture was homogenized and kept for 30 min in the dark at room temperature and the absorbance was assessed at 517 nm against diluted blank. A total of 1.5 mL of diluted blank was added to 1.5 mL of DPPH solution and vortexed thoroughly. In addition, it was incubated in the dark for 30 min and the absorbance was measured at 517 nm versus diluted blank.  
DPPH scavenging potential was expressed as a percentage of free radical scavenging activity (FRSA) with DPPH or as percentage inhibition of the free radical with DPPH, according to the following formula:  
\[\%\text{I}\;=\frac{\text{A control}-\text{A sample}}{\text{A control}}\times 100\]  
%I: Percentage of inhibition of free radical DPPH  
A control: Absorbance of control  
A sample: Absorbance of sample
## 5 Discussion
### Research Implications  
This study is the first to be carried out in a complex footwear industry to evaluate the performance of ML methods and classify the severity of workplace accidents. [PERSON] et al. [46] highlight that using ML to extract relevant database knowledge is a viable way to learn from past incidents and improve industrial enterprises' health and safety systems. Thus, this research sheds light on this possibility in the footwear industry, one of the most common sites of workplace accidents and illnesses [19; 85].  
Neural and non-neural methods have been successful in classifying the severity of accidents. Thus, this research's findings reinforce that non-neural methods are still viable, even though neural methods may present superior performance. Methods such as FR and XGBoots required less computational effort and training time to be processed. Furthermore, they present low complexity in their interpretation.  
Neural methods such as LSTM and GRU showed poor performance in classifying the severity of accidents. These methods have temporal dependence [37] in which the context, which is the output of a previous step, is the unilateral input of the step currently being processed [29]. This characteristic may have interfered with the accuracy of the classifications. Thus, the performance of these methods is enhanced for data that present temporal dependence, such as classification based on time series or text [60], to predict the trend of future data. The Bi-LSTM method showed much better performance. Unlike GRU and LSTM, the Bi-LSTM structure considers the complete data sequence due to its ability to analyze 'forward-backward' information, improving its prediction and performance [50]. Therefore, this study expands the possibility of using Bi-LSTM to analyze databases tabulated in electronic spreadsheets directly to classify the severity of work accidents in the industry.  
This is also the first article to test the 1D-COV method for classifying accidents in the footwear industry. [PERSON] et al. [70] had already observed good performance of convolutional methods in predicting the severity of traffic accidents. Therefore, this study expands the possibility of using 1D-COV directly on accident data in the footwear industry, as the accuracy of the classifications was greater than 99%.  
From a practical point of view, new simulated scenarios or new accurate data can feed the trained neural networks, generating the probable severity of the accident. Therefore, occupational safety managers can use algorithms to build prevention strategies and scenarios for possible workplace accidents.
## 4 Results and Analysis
### Fault Detection and Fault Identification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & **Proposed Method** & **Alfpido [2]** & **Falfeto [8]** & **Alfepio [10]** & **Fpiflo [29]** & **Alepio [30]** \\ \hline Normal State & 100\% & 94.8\% & 97.4\% & 96.1\% & 91\% & 88\% \\ Actuator Fault & 97.4\% & 91.1\% & 94\% & 92\% & 89.2\% & 86\% \\ Sensor Fault & 99.1\% & 73.8\% & 91.6\% & 86.7\% & 74.2\% & 70.2\% \\ Actuator-sensor Fault & 98.3\% & 82.4\% & 89.8\% & 89\% & 88\% & 88.2\% \\ \hline Average & 98.7\% & 85.5\% & 93.2\% & 90.9\% & 85.6\% & 83.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fault diagnosis results for the robot manipulator based on various state-of-art techniques. Figure 4: Torque and position residual signals of robot manipulator’s joints in normal and abnormal conditions based on the neural high-order variable structure observer (NHVSO) algorithm. Figure 5: Torque and position residual signals of robot manipulator’s joints in normal and abnormal conditions based on the adaptive neural high order variable structure observer (ANHVSO) algorithm. Figure 6: Energy of the torque and position residual signals of robot manipulator’s joints considering four different conditions for fault classification.
## 2 Literature Review
Accuracy is an important performance metric; however, for multiclass classification problems, accuracy is less significant as compared to other performance metrics, especially when there is an imbalance in the dataset. For instance, we would like to emphasize that precision and recall rate are important performance measures for life-critical applications. Most of the presented works have reasonable accuracy, but they suffer from lower precision and recall rate, and require improvement. Review of the existing work also highlights that most of the work on GI tract diseases has been conducted on datasets that are not publicly available. This makes it hard to generalize the results and compare the performance. Furthermore, researchers mostly focused on single disease detection and binary classification [14; 15; 16]. The focus of our work is to conduct experiments on publicly available datasets and target the multiclass classification of GI tract diseases like polyps, ulcers, ulcerative colitis, and esophagitis. Also, the suggested strategy has significantly improved the performance across practically all indicators.
## 4 Results
### Result of Model Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No. of Accidents**} & \multicolumn{6}{c}{**Years**} \\ \cline{2-7}  & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** & **2022** \\ \cline{2-7}  & **n (\%)** & **n (\%)** & **n (\%)** & **n (\%)** & **n (\%)** & **n (\%)** \\ \hline
2 & 31 (8.5) & 37 (10.1) & 37 (10.1) & 19 (5.2) & 18 (4.9) & 30 (8.2) & 20 (11.9) \\
3 & 14 (3.8) & 7 (1.9) & 9 (2.5) & 6 (1.6) & 4 (1.1) & 18 (4.9) & 8 (4.8) \\
4 & 1 (0.3) & 5 (1.4) & 4 (1.1) & 1 (0.3) & 1 (0.3) & 2 (0.6) & 1 (0.60) \\
5 & 1 (0.3) & 0 (0.0) & 0 (0.0) & 0 (0.0) & 1 (0.3) & 0 (0.0) & 1 (0.60) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Table 7 presents the training times (in seconds) for the methods. The models required 3.48 h to be trained via 10-fold cross-validation. Thus, only about 0.20% of the time was used to evaluate the performance of non-neural methods. The Bi-LSTM neural model required 43.62% of the time to assess the models' performance. The 1D-COV model required 7.10%  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**10-Fold Cross-Validation**} \\ \cline{2-13}
**Model** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Mean** \\ \hline
**LR** & & & & & & & & & & & & \\ Accuracy & 0.970 & 0.950 & 0.960 & 0.931 & 0.931 & 0.931 & 0.921 & 0.930 & 0.910 & 0.940 & 0.937 \\ Precision & 0.887 & 0.946 & 0.969 & 0.929 & 0.903 & 0.904 & 0.936 & 0.936 & 0.944 & 0.927 & 0.928 \\ Recall & 0.908 & 0.962 & 0.986 & 0.929 & 0.903 & 0.941 & 0.945 & 0.944 & 0.962 & 0.927 & 0.941 \\ F1-Score & 0.896 & 0.953 & 0.977 & 0.929 & 0.903 & 0.919 & 0.940 & 0.940 & 0.952 & 0.927 & 0.934 \\
**DT** & & & & & & & & & & & \\ Accuracy & 0.970 & 0.931 & 0.960 & 0.921 & 0.960 & 0.950 & 0.941 & 0.930 & 0.920 & 0.960 & 0.944 \\ Precision & 0.920 & 0.896 & 0.973 & 0.961 & 0.902 & 0.912 & 0.907 & 0.951 & 0.907 & 0.936 & 0.927 \\ Recall & 0.912 & 0.888 & 0.933 & 0.969 & 0.876 & 0.920 & 0.931 & 0.951 & 0.930 & 0.944 & 0.926 \\ F1-Score & 0.916 & 0.892 & 0.951 & 0.965 & 0.888 & 0.916 & 0.918 & 0.951 & 0.917 & 0.940 & 0.925 \\
**RF** & & & & & & & & & & & \\ Accuracy & 0.990 & 0.950 & 0.970 & 0.960 & 0.950 & 0.970 & 0.970 & 0.950 & 0.920 & 0.960 & 0.959 \\ Precision & 0.980 & 0.932 & 0.984 & 0.969 & 0.911 & 0.907 & 0.919 & 0.960 & 0.930 & 0.976 & 0.947 \\ Recall & 0.95 of the time to evaluate performance, even though it was the best model with respect to accuracy, precision, recall, and F-score.
## 3 Results and Discussion
### Results and Discussion of Method Application
#### 3.1.3 Relative Comparison of Values of Individual EFs  
A comparison of the relative parameterized values of the ecosystem functions assessed (see Figure 5A-F) shows that the lowest values apply to habitat-level biodiversity (HVM), especially in the urban core and suburban area and, in the case of Decin, also near the northern boundary, where there are large stands of planted spruce forests. The other two functions, evapotranspiration and especially production, have relatively higher values and, unlike biodiversity, are also located to a limited extent in the suburban area.  
The disparate results (especially lower biodiversity compared to other EFs) are consistent with findings that certain habitat features are important for maintaining biodiversity but are not as critical for maintaining other ecosystem functions. Species diversity in urban vegetation depends on diverse vegetation structure and vertical complexity [108; 109], the naturalness of the habitat [110], and eventually, the proportion of native trees [111] and the intensity of management [29]. Some of these characteristics are not as important for the other two functions (carbon production and evapotranspiration), as these depend mainly on the vegetation biomass [112] and LAI values [54], as well as, in the case of evapotranspirationspiration, physiological characteristics such as tree-level transpiration [113]. These results suggest that certain habitats with average and sometimes rather low biodiversity values (mainly planted spruce forests but also urban parks and other urban green spaces) can, due to their high biomass, provide ecosystem services that contribute notably to cooling of the urban environment through evapotranspiration and to mitigating climate change through carbon sequestration.  
Merging of the three EFs into one value (Figure 5G,H) shows the location of area of the best EFs performance. In Liberec, they are situated mostly near the border of the administrative territory, especially where the PLA is defined; however, there are some habitats of relatively high EFs performance situated in the suburban area. In Decin, the peri-urban area has a very high EFs performance. On the contrary, the worst values of EFs were found in the urban core areas, which are almost devoid of vegetation, and in the suburban areas, where discontinuous development predominates.