esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Arrangement Strategy of IoT Sensors in Urban Drainage Networks: A Review
## 3 Arrangement of IoT Sensors in the Drainage Network
### Classification of Monitoring Objectives
#### 3.1.1 Monitoring of the Operational Status of the Pipeline Network  
The assessment and diagnosis of the pipeline network operation states are mainly realized via the real-time monitoring of hydraulic parameters such as flow rate, liquid level, and flow velocity [34]. By analyzing abnormal signals and sharing data across nodes, IoT sensors enable spatial continuity in monitoring, improving the detection and resolution of pipeline network anomalies. The IoT sensors are optimally arranged. Optimal IoT layouts are used to improve the coverage area [35; 36], the amount of monitored information, the time of detection [33; 37], and the reliability of the monitoring scheme [30; 38].  
In addition, the monitored data of the drainage network can be used in the calibration of numerical models. The hydraulic and water quality coupling model can be used to solve hydrodynamic problems involving flow velocity, flow rate, and water level. They can also simulate source diffusion and its inverse [39; 40]. However, they require more input parameters [41] for calibration. Both the number and spatial location of calibration points can affect the calibration accuracy of the model [42; 43]. By optimizing the arrangement of sensors to collect data at the optimal calibration points [44] of the model, the cost of data collection can be reduced while improving the accuracy.
## 4 Experimental Evaluation
### Results and Analysis
#### 4.3.3 Effect of Pseudo-Label Selection with Different Thresholds  
To obtain the best performance of the semi-supervised method, different thresholds of pseudo-label selection are utilized to explore the effect of pseudo-label selection on the CWS using uncertainty and confidence values. According to [42], we do not over-tune the hyperparameters and tune the thresholds according to the validation set.  
Thresholds of 0.5, 0.7, and 0.9 are chosen as our selection thresholds, and the effects of different selections on the method performance are shown in Figure 3. When the selection threshold for pseudo-label selection is set at 0.7, the approach performs at its best. This indicates that when the pseudo-label selection has low selection thresholds, the pseudo-labels have a low value of confidence and certainty, which leads to a large amount of noisy data being used in the training process. However, at the same time, the high confidence and certainty value results in filtering out a large amount of potential information, and the performance has a small increase or even a decrease. Therefore, we choose 0.7 as the threshold of pseudo-label selection.
## 4 Technological Advancements
### Manual to Automatic Detachable Inter-Modular Connections
However, the lateral stability offered by these stabilizing systems is limited because the upper and lower IMUs' interconnectivity is lacking in regions other than the column corners. As per evaluation, the linked wall systems made up of numerous vertical bars and sleeves could aid structures with the superior lateral response, as shown in Figure 6d [88] and Figure 6e [89]. They rigidly connect vertical diaphragms using a composite connection between the walls using bars. Recently, one of the authors patented a base isolation technology, as shown in Figure 6a [90]. This satisfies the requirements for the standard use, structural bearing capacity, and an effective resistance to the effects of earthquakes. However, they are suitable for pre-tensioned and composite types of IMCs inside the columns of PFISBs. Therefore, other energy dissipation joints are developed; for instance, the authors invented a rubber isolation system [91] and a self-locking ten-and-mortise vibration isolation rubber IMC in the IMU, as shown in Figure 5a,b [92]. The invention may reduce the adverse effects of earthquakes and improve the PFISB structure's bearing capacity. Similarly, two distinct configurations of bolted IMCs having washer and resilient rubber layers are demonstrated, as shown in Figure 5d [93]. It has been shown that the IMCs which have been bolted are rigid and cause a column fracture, as opposed to the IMC in Figure 5d, which has been strengthened with resilient layers which can withstand significant deformations without fracturing the bolt [93]. A lead viscoelastic damper in a vertical IMC dissipated more energy than the conventional bolted IMC, such as those shown in Figures 3a-f and 4a-d [94]. The damping joints in Figure 5a-d have the effect of delaying the premature failure of the structural components. They prevent a failure and collapse, increase the capacity, and guarantee security. Figure 5: Damping IMC. (**a**) rubber isolator [91]; (**b**) vibration isolation rubber [92]; (**c**) viscoelastic damper [94]; and (**d**) resilient layer rubber [93].
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 3 EALC Methodology
### Node Movement and Network Communication  
After the clustering process, nodes start communicating and send their data to their CH. It is the responsibility of the CH to deliver the data to the intended destination, either to a node within a cluster, across the cluster or to the base station. EALC follows the reference point group mobility model RPGM [26; 27]. In RPGM, all nodes follow some reference point for their movement. EALC considers the CHs as reference points and all CMs update their position according to the movement of their CHs. The process in the grey shaded area in (i.e., calculating fitness, clustering through K-Means Sorted Fitness) in Figure 1 will be repeated until the flight operation or simulation time is ended.
###### Abstract  
Previous research has predominantly focused on the biomechanical effects of anterior-posterior foot motion during running, with comparatively less attention given to medial-lateral foot motion and its impact on lower limb biomechanical characteristics. We recruited 18 healthy runners who wore five different types of running shoes: regular shoes (NS), those with a 6 mm and 9 mm medial-lateral height difference in the forefoot (M6, M9), and those with a 6 mm and 9 mm lateral-medial height difference (L6, L9). Biomedical parameters of lower limb joints during the stance phase of running, including range of motion, peak angular velocity, peak moment, power, and work, were analyzed. We used paired-sample \(t\)-tests and one-dimensional statistical parametric mapping (SPM1D) to compare joint biomechanics between shoes with varying height differences and NS. Under the L6 condition, notable differences occurred in the hip and knee flexion-extension moments during landing and push-off, accompanied by a significant increase in ankle dorsiflexion work and a significant decrease in inversion-eversion work. In contrast, the M9 condition resulted in decreased hip flexion-extension peak moment, power, and work in the sagittal plane. These findings indicate that varying forefoot medial-lateral height differences in running shoes significantly impact lower limb joint dynamics during the stance phase, particularly the L6 condition, potentially reducing knee injury risk and aiding gait improvement for overpronators. The findings offer valuable insights for sports injury prevention and athletic footwear design. However, further research is needed to understand the underlying mechanisms and practical implications for sports injury prevention and performance enhancement.  
forefoot height discrepancy; running injuries; running shoes; shoe design; lower limbs; biomechanics +
Footnote †: journal: Medical and Medical and Medical and Medical Research  
0000-0001-7999-2277-3000]J.S.S.Baker
## 2 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**NO.**} & \multirow{2}{*}{**Compounds**} & \multicolumn{2}{c}{**Percentage of Total Monoterpenses +/– SD**} & \multicolumn{2}{c}{**Fold Change**} \\ \cline{3-5}  & & **Control Light** & **Low Light** & **Low Light** & **Low Light/Control Light** \\ \hline
1 & (+)a-Pinene & 4.07 \(\pm\) 0.61 & 3.52 \(\pm\) 0.59 & 0.86 \\
2 & Sabinene & 0.24 \(\pm\) 0.03 & 0.12 \(\pm\) 0.03 & 0.50 \\
3 & \(\beta\)-Pinene & 1.05 \(\pm\) 0.15 & 0.93 \(\pm\) (0.08) & 0.88 \\
4 & \(\beta\)-Myrone & 2.35 \(\pm\) 0.14 & 1.90 \(\pm\) (0.35) & 0.81 \\
5 & Limonene & 10.49 \(\pm\) 1.29 & 11.07 \(\pm\) (1.84) & 1.05 \\
6 & 1,8-Cineole & 0.17 \(\pm\) 0.02 & 0.17 \(\pm\) 0.01 & 1.00 \\
7 & Menthone & 25.18 \(\pm\) 0.06 & 17.97 \(\pm\) 0.28 & 0.71 * \\
8 & Isomenthone & 7.52 \(\pm\) 0.49 & 3.12 \(\pm\) 0.05 & 0.41 * \\
9 & Menthol & 32.02 \(\pm\) 4.69 & 12.85 \(\pm\) 0.41 & 0.40 * \\
10 & Isopleugone & 1.91 \(\pm\) 0.15 & 0.15 \(\pm\) 0.02 & 0.08 * \\
12 & Pulegone & 0.95 \(\pm\) 0.22 & 8.70 \(\pm\) 1.15 & 9.15 ** \\
13 & Piperitone & 1.23 \(\pm\) 0.63 & 28.31 \(\pm\) 1.14 & 23.02 * \\
14 & Caryophyllene & 0.50 \(\pm\) 0.06 & 0.16 \(\pm\) 0.03 & 0.32 * \\
15 & Germanence D & 2.9 \(\pm\) 0.38 & 0.15 \(\pm\) 0.02 & 0.16 * \\
16 & \(\gamma\)-Cadinene & 0.26 \(\pm\) 0.02 & 0.26 \(\pm\) 0.02 & 1.00 \\ others & & 14.38 \(\pm\) 2.05 & 18.52 \(\pm\) 0.27 & 1.29 * \\ Monoterpene hydrocarbons & 17.71 \(\pm\) 2.31 & 16.66 \(\pm\) 2.26 & 0.94 \\ Oxygenated monoterpens & 67.67 \(\pm\) 5.22 & 64.87 \(\pm\) 2.24 & 0.95 \\ Sesquiterpenes & 10.07 \(\pm\) 1.38 & 5.20 \(\pm\) 0.73 & 0.51 * \\ Total essential compound percentage & 99.87 \(\pm\) 0.08 & 97.13 \(\pm\) 0.27 & 0.97 \\ \hline \end{tabular} The percentages of total monoterpenes are given as mean values \(\pm\) SD. The fold change was calculated by dividing the percentages of compounds in darkness-treated leaves by the percentages of compounds in control leaves. The level of significance was obtained with Student’s \(t\)-test, and is marked as follows: * \(p\) \(<\) 0.05; **_p_ \(<\) 0.01.  
\end{table} 
Table 1: The essential oil compositions of mint leaves (_Menthá canadensis_ L.) under the control and low light conditions. Figure 2: Introduction of changes in volatile exudates of leaves under the control and the low light conditions. (**A**) Gas chromatography–mass spectrometry (GC-MS) analysis of plants under the control (up) and low light (bottom) conditions. (**B**) MS/MS spectra of significantly changed compounds between the two light conditions, i.e., menthol, pulegone, piperitone, and the internal standard substance bornanone. (**C**) Relative contents of volatile compounds: menthol, pulegone, and piperitone.
## 2 Methodologies
### Stacked Denoising Auto-Encoder  
The SDAE consists of multiple denoising auto-encoders (DAE) through stacking [37]. The SDAE can provide extensive and vigorous fault-feature extraction from raw-input vibration data. The structure of the SDAE is composed of neural networks, as shown in Figure 7.  
The auto-encoder includes an encoding network and a decoding network. The encoding network maps the input vector \(\mathbf{X}=\{x_{1},\dots,x_{i}\}\) to the encoded vector, which is subsequently remapped to the output vector \(\mathbf{Y}\) by the decoding network. The encoded vector is the characteristic representation of the input vector, whereas the output vector is the reconstruction representation of the input vector. The dimension of the output vector is equal to the input vector. The output of the encoding network is expressed as  
\[Y=f_{\theta}(X)=S(W\cdot X+B)\]  
Figure 7: Neural networks of the stacked denoising auto-encoder (SDAE).  
where \(W=\{w_{1}\), \(w_{2}\),..., \(w_{n}\}\) and \(B=\{b_{1}\), \(b_{2}\),..., \(b_{n}\}\) are the weight matrix and the bias matrix, respectively, from the input layer to the hidden layer; and \(W\) and \(B\) constitute the parameter \(\theta=\{W,\,B\}\). Generally, the activation function \(S\) is a sigmoid function. The function of \(W\) and \(B\) is able to be summarized as the encoding of original data. SDAE decodes the output of the hidden layer using the following equation:  
\[Z=g_{\theta^{T}}(Y)=S\Big{(}W^{T}Y+B^{T}\Big{)} \tag{10}\]  
Constructing the SDAE model mainly determines the model parameters. The equation to solve the optimal parameters \(\theta\) and \(\theta^{T}\) is as follows:  
\[\Big{\{}\theta,\theta^{T}\Big{\}}=\text{argmin}\frac{1}{n}\sum_{i=1}^{n}L(x_{ i},g_{\theta^{T}}(f_{\theta}(x_{i}))) \tag{11}\]  
To obtain the features of the input data, the loss function of SDAE must be reduced as much as possible. Like the traditional auto-encoder, the SGD algorithm is used to minimize the model error of SDAE. The loss function L(\(X,Z\)) is expressed by  
\[L(X,Z)=-\sum_{k=1}^{d}[X_{k}\text{lg}Z_{k}+(1-X_{k})\text{lg}(1-Z_{k})] \tag{12}\]  
The principle of SDAE is shown in Figure 8. As a typical deep learning method, SDAE has its own shortcomings. When the training vibration-fault samples are inadequate, the training on the SDAE model is also insufficient and the model cannot accurately obtain the parameter \(\theta\). This means that the SDAE-based fault diagnosis is inaccurate. However, as mentioned above, the GAN can generate many training samples after the data augmentation, and this can compensate for the shortcomings of the SDAE. A joint model combining GAN and SDAE--a GAN-SDAE system--is the best solution to improve the accuracy of the fault diagnosis.
## 3 Results and Discussion
### Effect of Composite Micronized Powder on the Performance of Cementitious Materials  
Integrating the above orthogonal experimental results and giving priority to the influence of each factor on the 28 d activity index of the composite micronized powder, the best ratio for the preparation of composite micronized powder is A4B4C4D2, i.e., 35% BFS, 30% MTS, 0.3% GA, 5% DG and 30% WQMS. The specific surface area of the composite micronized powder is 460 m\({}^{2}\)/kg. Figure 4 shows the particle size distribution of OPC and composite micronized powder, and it can be seen that the particle size range of composite micronized powder is smaller. Figure 5 shows the XRD pattern of the composite micronized powder, which shows an obvious dispersion peak, indicating that the main component of the composite micronized powder is an amorphous high-activity substance. On this basis, the cementitious material was prepared by compounding the composite micronized powder, prepared using the best mix ratio with OPC at a 1:1 ratio. The fluidity and activity of the cementitious material are given in Table 7; compared with Table 4, it can be observed that it has the highest 28 d activity. In addition, the effect of the composite micronized powder on the hydration heat release characteristics and microstructure of the cementitious material was also tested.  
Figure 4: Particle size distribution of OPC and composite micronized powder.  
Figure 5: XRD patterns of composite micronized powder.  
2.1 Effect of Composite Micronized Powder on the Hydration Characteristics of Cementitious Materials  
Figure 6 shows the exothermic properties of hydration of cementitious materials mixed with 50% composite micronized cementitious materials and pure cementitious materials. Figure 6a shows the exothermic rate curve of hydration of different cementitious materials, and the input image provides details of the exothermic rate curve of hydration within 0-0.6 h. Figure 6b shows the cumulative exothermic curve of hydration of different cementitious materials. Compared with the maximum hydration rate of pure cement paste, the maximum hydration rate of cementitious material mixed with 50% composite micronized powder was reduced by 37.08%. Compared with the 3 d cumulative heat release of pure cement paste, the 3 d cumulative heat release of cementitious material mixed with 50% composite micronized powder was reduced by 24.69%.  
The experimental results show that the hydration rate and cumulative heat release of the cementitious material mixed with 50% composite micronized powder are much lower than those of the pure cementitious material. One of the main reasons for this is the significantly greater reduction in the heat of hydration of the cementitious material due to the halving of the cement dosage [29]; secondly, it is because there are many kinds of powder particles in the composite micronized powder, in which the unreacted various inert particles will hinder and delay the chemical reaction between Ca(OH)\({}_{2}\), the hydration product of cement and the active material in BFS, WQMS and MTS, and absorb part of the exothermic heat.
## 6 Conclusions
### Implications for Theory  
The current state of knowledge of PDCA also explores the following aspects:  
1. Integration with other quality management frameworks: This research achieves this goal by combining PDCA with other quality tools like capability analysis and control charts for a more comprehensive approach to continuous improvement. In addition, this research proposes a simplified version of the PDCA cycle, combining the \"Do\" and \"Check\" phases into a single \"Do/Check\" phase. This streamlined approach can expedite the achievement of results.
2. Application in specific contexts: While this study was applied in electric motor manufacturing, the framework's replicability allows for adaptation and application in organizations that manufacture products with wire windings. This includes generators, alternators, pumps, transformers, switchgears, and similar equipment.
## 7 Results and Discussion
### Indoor/outdoor Classification  
A study to evaluate the accuracy of a simple \(\{indoor,outdoor\}\) classifier was performed. In this task GPS signals are very relevant as the low power levels prevent satellite signal tracking in most buildings. The binary classification induces some problems in defining the boundaries. We defined driving a passenger car or travelling in a train as _outdoor_ environment, the main reason being that radio receivers are less affected by vehicles than buildings.  
The confusion matrix for the MAP classifier is presented in Table 4.  
Especially good features for differentiating between these classes were the WLAN signal strengths, which are usually higher indoors than outdoors. By using only the WLAN signal strength we could obtain classification accuracies of 78% and 82% for indoor and outdoor, respectively. Nevertheless, the results may become very different if the data would be collected only from areas where the WLAN station density is low.  
By using DT and SVM we could achieve higher classification rates for the _indoor_ and _outdoor_ class. Confusion matrices, where all the features from GPS, WLAN and Bluetooth were used, are presented in Tables 5 and 6 for DT and SVM, respectively. This shows that the decision tree gives the highest classification rate with these classes. However, to test our adaptation method we continued to use MAP algorithm also for these classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{{{}_{true}}^{\backslash}}\mathbf{{{}_{decision}}}\) & \(\mathbf{{In}}\) & \(\mathbf{{out}}\) \\ \hline \(\mathbf{{In}}\) & \(82\) & \(18\) \\ \(\mathbf{{Out}}\) & \(18\) & \(82\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of the classes _indoor_ and _outdoor_ using MAP classifier. The values denote percentages.
## 4 Experiments and Results
### Experimental Results  
#### 4.3.1 Baseline Modeling and Parameterization  
Using different traditional machine learning models as base classifiers introduces diversity naturally, and using multiple simpler traditional machine learning models as base classifiers can improve computational efficiency while maintaining high levels of accuracy. Through integrated learning, the strengths of these models can be combined, e.g., decision trees are easy to interpret, and support vector machines perform well on high-dimensional data. In this paper, decision trees (DT), random forests (RF), Adaboost, gradient boosting (GB), extreme trees (ET), and support vector machines (SVM) are selected as the initial set of classifiers. The training set and test set are divided in the ratio of 8:2, the validation set is divided 25% from the training set, and the validation set finally accounts for 20% of the original dataset, which is used for adjusting the model parameters, and setting up multiple sets of experiments to thoroughly investigate the effects of the model parameters.  
Table 2 provides the parameter settings containing the parameter descriptions and the range of available parameters. The parameters contain the attribute screening factor \(\beta\) that constrains the feature space when the feature space is reconstructed, the number of subsets of the feature subspace division, and the number of classifiers required for the ensemble. The number of subsets of the feature subspace division is the same as the number of classifiers required for the ensemble, as the feature spaces trained by different classifiers in the HDELC ensemble model are one-to-one and do not intersect.  
Among them, \(\beta\) controls the feature reconstruction space's degree of importance and scale. The subset of feature subspace division and the number of classifiers required for ensemble control the size of the ensemble model. All three together affect the accuracy of the ensemble learning model to some extent.
## 3 Control Strategy and Optimization Function
### Fopid Controller  
A fractional-order PID (FOPID) controller is a type of control that has several advantages. One of the main advantages is improved robustness and flexibility, meaning that FOPID controllers are more resistant to parameter variations and external disturbances, allowing for stable control of the system even when there are changes in the system dynamics. Furthermore, FOPID controllers can achieve a faster response time and better steady-state performance than traditional PID controllers. The structure of an FOPID controller is shown in Figure 3b, and the corresponding mathematical expression can be given as  
\[G_{\mathrm{c}}(s)=K_{p}+\frac{K_{i}}{s^{\lambda}}+K_{d}s^{\mu} \tag{17}\]  
where \(\mu\) is a differential order and \(\lambda\) is an integral order to provide the PID controller's settings a larger tuning range [49]. The optimization of the fitness function in this case is given by  
\[\begin{array}{c}J_{\min}(K_{p},K_{i},K_{d},\lambda,\mu)=\int\limits_{0}^{ Tsim}t|\Delta f|dt\\ \text{\emph{subject to:}}\\ K_{p}^{\min}\leq K_{p}\leq K_{p}^{\max}\\ K_{i}^{\min}\leq K_{i}\leq K_{i}^{\max}\\ K_{d}^{\min}\leq K_{d}\leq K_{d}^{\max}\\ \lambda_{\min}^{\lambda}\leq\lambda\leq\lambda_{\max}\\ \mu_{\min}\leq\mu\leq\mu_{\max}\end{array} \tag{18}\]
## 3 Experiments
### Experimental Results
#### 3.5.3 Evaluation on the BACH Dataset  
The above experiments demonstrate that HoRFNet achieves high classification accuracy on the BreakHis dataset. To further validate the model's generalization ability and robustness, we conduct additional experiments on the BACH dataset, involving both two-class and four-class classification tasks. As shown in Table 5, HoRFNet consistently outperforms the Backbone model on the BACH dataset. In the two-class classification task, HoRFNet achieves an accuracy of 88.75%, reflecting an 8.75% improvement over the Backbone's 80.00%. In the four-class classification task, HoRFNet attains an accuracy of 82.50%, significantly outperforming the Backbone's 76.25%, with a margin of 6.25%. These results indicate that HoRFNet maintains stable recognition performance across different data distributions and tissue types, further validating its generalizability and robustness in BCHI classification.