esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Removal of Pharmaceuticals during Wastewater Treatment
### Conventional Treatments
From the results of the above-cited studies, it can be concluded that conventional WWTP settings, which have been performed to reduce the contamination of water, did not lead to a desirable quality of water. Indeed, pharmaceutical residues were not totally removed by these processes, and they are increasingly dangerous for humans and the environment. It is of interest to explore the technological and scientific gaps in wastewater treatment by the conventional techniques with the specific study. The review paper published by [PERSON] et al. [55] reported that micropollutant removal appears to be compound- and process-dependent in all investigated processes. This paper also reported that even though some hybrid processes show promising micropollutant removal, further studies are needed to optimize these water treatment processes, in particular in terms of technical and economic competitiveness. Conventional drinking water plants feature multiphase treatments, including clarification, filtration, and coagulation/flocculation steps [1], Not one of these operations have shown an efficient removal of pharmaceutical products among the sixteen different medicines considered in this review [56]. When considering sewage water, [PERSON] et al. [56] demonstrated the ineffectiveness of the coagulation-flocculation process for the elimination of pharmaceutical products. However, the use of iron (III) chloride as a flocculant product showed no significant removal for diclofenac, carbamazepine, bezafibrate, and clofibric acid from water [53]. [PERSON] et al. [57] observed low removal of sulfamethoxazole and acetaminophen up to 25%. According to [PERSON] et al. [58], coagulation was shown to be effective for the removal of seven tetracycline classes of antibiotics (TAs), and the addition of poly-aluminium chloride (PACI) allowed authors to remove 43-94% of TAs at the optimum conditions from the synthetic water. Overall conventional water and wastewater treatment processes are unable to reliably remove some recalcitrant pharmaceuticals, and it is necessary to introduce additional advanced treatment technologies prior to discharge into the environment. Among the emerging treatment options, combined adsorption and AOPs are the most promising methods for the efficient removal of pharmaceutical residues. The combined adsorption--AOPs systems are discussed, which hold great promise to provide international alternatives for the manifold WWT process to mitigate pharmaceutical residues and meet acceptable limitations.
# Experimental Study on the Features of Infrasonic Waves of Sandstone under Shear Load
## 3 Signal Processing and Analysis Method
### Analysis and Processing of Background Noise
#### 3.1.2 Analysis of Background Noise  
After noise preprocessing, the background noises in the experiments were mainly the sounds produced by mechanical vibration of the mechanical system during force loading. Therefore, before the experiment, the background noises produced by the mechanical system were collected by the infrasound sensor, and continuous wavelet transform and time-frequency analysis of the background noises were conducted (Figure 4). As shown in Figure 4, the background noise in the laboratory had relatively stable frequency, no outstanding frequency components, and low intensity (\(\leq\)0.022 dB).
# A Novel Intermittent Jumping Coupled Map Lattice Based on Multiple Chaotic Maps
## 3 Analysis of Dynamic Behaviors
### Equilibrium Degree Analysis  
As the name implies, the equilibrium degree analysis is to evaluate whether the number of ones is close to the number of zeros in a binary sequence. Naturally, its definition can be written in the following form:  
\[ED(l)=1-|P(l)-Q(l)|/N, \tag{14}\]  
where \(l=1\), \(2,\cdots\), \(L\). The notations \(P(l)\) and \(Q(l)\) record the number of ones and zeros, respectively, in the \(l\)th binary sequence, and we have \(N=P(l)+Q(l)\). The value of equilibrium degree lies in the interval [0, 1], and the higher the value, the better the equilibrium degree. For some bit-level image cryptosystems, the equilibrium degree analysis is helpful to check whether a CML-based system can generate pseudo-random binary sequences.  
Figure 5 shows results for the equilibrium degree analysis under the setting \(2.8\leq\mu<4\) and \(0\leq e\leq 1\). In this simulation study, the sequence \(x_{n}(l)\) generated by the \(l\)th lattice is first binarized through the operation \(mod(\ \lfloor x_{n}(l)\cdot 10^{7}\ \rfloor\,\ 2)\), where \(n=1,\ 2,\cdots,\ N\). We calculate the average value of \(ED(l)\) over all lattices, where \(l=1,\ 2,\cdots,\ L\).  
Figure 5: Equilibrium degree analysis for (**a**) the CML system, (**b**) the NCML system, (**c**) the DCML system, (**d**) the NDCML system, and (**e**) the proposed IJCML system, respectively.  
As we see, when \(\mu<2.99\), the average values of equilibrium degrees are all equal to 0 for the baseline systems. When \(2.99\leq\mu\leq 3.57\), the baseline systems become trapped in an unstable transition zone, over which the average values are drastically varying. When \(\mu>3.57\), the DCML system reaches a flat plateau with the average value close to 1 as shown in Figure 5c. For the CML system, however, there exists an obvious sunken area embedded into the flat plateau at the range \(3.57<\mu<3.99\) and \(0<e<0.18\). For the NDCML system, two straight ravines occur at \(\mu=3.74\) and \(3.84\), respectively, and directly go through the flat plateau. The NCML system has a wavelike plateau, which involves multiple sunken areas and ravines. This means that these baseline systems fail to achieve a stable equilibrium degree even when the nonlinear mapping function \(f(x)\) has entered into the chaotic regime (i.e., \(\mu>3.57\)). In contrast, due to the mixture of multiple chaotic maps, the proposed IJCML system possesses a more stable and larger flat plateau as shown in Figure 5e. If \(e\) is set to 0, the IJCML system's equilibrium degree degrades considerably, which validates that the intermittent jumping mechanism is helpful to enhance the chaotic behaviors. Based on these observations, we state that the IJCML system outperforms the baseline ones in terms of the equilibrium degree.
## 2 Results
### In Vitro and In Vivo Identification of CsTPSs  
The enzymatic activities of the CsTPSs were tested with an in vitro assay, using GPP, FPP, GGPP, and neryl pyrophosphate (NPP) as substrates. CsTPS3 GT was found to react only with FPP to form sesquiterpene (Z)-\(\gamma\)-bisabolene, while CsTPS4 WS reacted exclusively with GPP to form linalool, a monoterpene alcohol, as reported for CsTPS29 (Figure 7a,b; [29]). As there are two natural enantiomers for linalool (R and S) [34,35], CsTPS4 WS was further confirmed to be (R)-linalool synthase via chiral gas chromatography (Figure 7c). On the other hand, CsTPS5 TH produced the sesquiterpene (E)-nerolidol with only FPP, thus confirming it as an (E)-nerolidol synthase (Figure 7d). CsTPS activity was further evaluated in vivo by transiently expressing them in the leaves of _N. benthamiana_. Consistent with the in vitro results, the characteristic peak of (R)-linalool with CsTPS4 WS at 3 dpi (days post-infiltration) was detected. Similarly, CsTPS3 GT and CsTPS5 TH were confirmed to produce (Z)-\(\gamma\)-bisabolene and (E)-nerolidol, respectively (Figure 7e-g).  
Figure 6: Subcellular localization of CsTPSs in _N. benthamiana_ leaf cells. _A. tunefaciens_ harbouring YFP-tagged CsTPS was infiltrated into _N. benthamiana_ leaves. Then, the infiltrated leaves were visualized using a confocal microscope (2 dpi). Auto: chlorophyll autofluorescence; YFP: YFP channel image; Bright field: light microscope image; Merge: merged image between autofluorescence, YFP, and light channel. Scale bars: 20 \(\upmu\)m.
###### Abstract  
The scaling of the strength of composite parts with part size is referred to as the size effect. In the presence of notches, stress concentration affects a portion of material that increases with the notch size. Furthermore, in woven composites, the notch and tow size can be comparable, thus demanding a mesoscale approach to properly capture the stress intensification. In this paper, a probabilistic mesoscale method to model the size effect in notched woven composites is presented. First, the stress distribution is estimated with a finite element model, calibrated on experimental Digital Image Correlation data. The FE model simulates the mesoscale heterogeneity of the woven reinforced material and replicates the local stress intensification at the tow level. Then, a three-parameter Weibull-based statistical model is introduced to model the probability of failure from the calculated stress distribution and the volume of the part. An equivalent stress is used to capture the relevant fiber and matrix failure modes and the maximum value within the specimen volume is the random variable of the model. The method is applied to open-hole tension tests of a woven twill carbon fiber-epoxy composite. Two specimen widths and three width-to-diameter ratios, from 3 to 12, are considered. Specim width produced an observable size effect, whereas the variation of hole size in the range considered did not. The statistical model is found to accurately describe the experimental observations, efficiently replicating an inverse size effect, regardless of hole size, while wider specimens lead to a lower probability of failure.  
size effect; digital image correlation; open hole; multiscale; failure probability
## 4 Results
### Experimental Results
The total time spent on training and validation can also be seen in Table 14 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **LSTM** & **CNN** & **TCN** & **MLP** & **TREE** & **RF** & **Average** \\ \hline
**Quarantine** & 2.698 & 0.390 & 2.124 & 0.368 & **0.002** & 0.593 & 1.029 \\ \hline
**Normal** & 2.712 & 0.391 & 2.139 & 0.367 & **0.002** & 0.601 & 1.035 \\ \hline
**Fraud** & 2.716 & 0.392 & 2.147 & 0.367 & **0.002** & 0.600 & 1.037 \\ \hline
**Average** & 2.708 & 0.391 & 2.137 & 0.367 & 0.002 & 0.598 & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Time results in seconds for each period and each model. The results in time were obtained by calculating the average of each of the times used to train each of the models in the parameter optimisation process. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **LSTM** & **CNN** & **TCN** & **MLP** & **TREE** & **RF** & **Average** \\ \hline Quarantine & **12.6** & 14.8 & 13.5 & 12.7 & 13.3 & 14.8 & 13.6 \\ \hline Normal & 9.8 & 10.2 & 12.0 & 10.1 & **9.5** & 9.6 & 10.2 \\ \hline Fraud & 6.8 & 7.2 & 8.5 & 7.2 & **5.9** & 7.1 & 7.1 \\ \hline Average & 9.7 & 10.7 & 11.3 & 10.0 & 9.6 & 10.5 & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: MAPE results for each period and each model. The results have been obtained after choosing the best set of parameters for each case by carrying out a parameter optimization process. The last row and column correspond to the means for each model and dataset, respectively. The best model for each dataset is specified in bold. Finally, Figures 6-8 provide a graphical comparison between the predictions on the validation set and the actual price values. The graphs show the results obtained with the best model in terms of MAE for each of the three datasets. Looking at all the metrics presented, both those that take into account the efficiency of predictions and time, we can observe a drawback that occurs in many works, which is that the models that take the longest time are the ones that obtain the best results. In this case, it can be clearly seen that on average the model that takes the longest is LSTM, while on average it is the one that obtains the best predictions. This is why it is sometimes necessary to choose between these two fundamental aspects, computation time and prediction efficiency. Regarding which technique should be used, we have to say that since it is a very complex problem, the choice of which technique is more appropriate will depend on the input data, however, it can be corroborated that regardless of the particular Deep Learning technique we use the results are promising.
## 3 Methods  
### Preprocessing Dataset  
Facial alignment is necessary for facial identification and facial expression recognition by dividing the upper and lower facial regions.  
When face alignment is not processed, an imbalance in the face area occurs during the face division process, as shown in Figure 4. Mounting the dataset, including the imbalanced face area, onto a learning model decreases the reliability of the results. Therefore, the face feature points were determined using the multitask cascaded convolutional network (MTCNN) model [27] to perform face alignment. MTCNN is a method of calculating the loss for classification, regression, and localization, and then adding them by weighting them. The feature points of the face obtained by the MTCNN are shown in Figure 5.  
Figure 3: Brief architecture of EmoNet for facial expression recognition.  
Figure 2: A brief architecture of face identification.  
There are five facial characteristics: the center point of each eye, tip of the nose, and tip of the mouth. The face angle was adjusted using the coordinates of the center point of each eye.  
As shown in Figure 6, the coordinates of the center points of the left and right eyes are defined as (x\({}_{1}\), y\({}_{1}\)) and (x\({}_{2}\), y\({}_{2}\)), respectively. The vector calculated using Equation (2) is defined as V\({}_{1}\). We also define a vector parallel to the x-axis, calculated using Equation (3), as V\({}_{2}\). Finally, the angle between V\({}_{1}\) and V\({}_{2}\) is calculated using Equation (4): Face alignment is performed using the angle obtained from the following calculation:  
\[\mathrm{V}_{1}=(|\mathrm{x}_{2}-\mathrm{x}_{1}|,|\mathrm{y}_{2}-\mathrm{y}_{1 }|) \tag{2}\]  
\[\mathrm{V}_{2}=\begin{cases}(\mathrm{x}_{1},0),\ \ (\mathrm{y}_{2}>\mathrm{y}_{1 })\\ (\mathrm{x}_{2},0),\ \ (\mathrm{y}_{2}<\mathrm{y}_{1})\end{cases} \tag{3}\]  
\[\begin{array}{c}\mathrm{a}=|V_{2}|\\ \mathrm{b}=|V_{1}|\\ \mathrm{c}=|y_{2}-y_{1}|\\ \cos a=\left(b^{2}+c^{2}-a^{2}\right)/(2*b*c)\\ \theta=\begin{cases}90-\arccos(\cos a),\ \ (\mathrm{y}_{2}>\mathrm{y}_{1 })\\ \arccos(\cos a),\ \ (\mathrm{y}_{2}<\mathrm{y}_{1})\end{cases}\end{array} \tag{4}\]  
Figure 4: Example of cropped upper and lower facial regions without face alignment.  
Figure 5: Facial feature points found by the MTCNN model (central point of each eye, tip of nose, end of mouth).  
The face angle aligned result is shown in Figure 7.  
Finally, after the face-area-cropping operation, each image is saved using only the upper and lower facial regions. Example images are shown in Figure 8.  
The above process was applied to the Labeled Faces in the Wild (LFW) [28] and AffectNet datasets used for facial recognition and facial expression recognition.
# Theoretical Study of the Wear of a Reduced-Diameter Wheel for Freight Wagons, Based on Its Diameter
## 2 Methods and Materials
### Calculation Model
#### 2.4.10 Prediction of RCF  
Under high axle loads, stress distribution around the contact patch can lead to fatigue cracks on the wheel surface or within it. To predict the occurrence of rolling contact fatigue (RCF), the fatigue index model developed by Ref. [45], as presented by Ref. [23], proves effective. The fatigue index (\(Fl_{surf}\)) is calculated as the utilized friction term (\(\mu_{u}\)) minus the shakedown limit (\(L_{RCF}\)). By comparing this value to zero, three scenarios can be identified:  
* If \(Fl_{suf}<0\), RCF alone is insufficient to initiate cracks because the tangential force is controlled by moderated (utilized) friction.
* If \(Fl_{suf}=0\), this represents the limiting condition. Cracks do not form because the shear stress at yield (\(\tau_{lim}\)) has not yet been exceeded.
* If \(Fl_{suf}>0\), RCF triggers surface cracks due to the increased tangential force resulting from higher (utilized) friction.  
The formation of an RCF prediction is presented next. The maximum force above which RCF appears (\(F_{max,RCF}\)) results from equating \(Fl_{suf}\) to zero (that is, in the limit situation):  
\[FI_{surf}=\mu_{u}-L_{RCF} \tag{54}\]  
\[FI_{surf}=\frac{\sqrt{F_{x}^{2}+F_{y}^{2}}}{N}-\frac{\tau_{lim}}{p_{z_{o}}} \tag{55}\]  
\[F_{max,RCF}=\frac{2}{3}\tau_{lim}\pi ab \tag{56}\]
## 2 Related Works
### General Object Detection
YOLOv8 [25] introduces an attention mechanism and dynamic convolution and specifically improves on small-object detection to address the challenges highlighted in YOLOv7. For the problem that current detection methods lose a large amount of feature information when performing layer-by-layer feature extraction and spatial transformation operations on the input image data, YOLOv9 [26] proposes the concept of programmable gradient information (PGI), which computes the objective function by providing complete input information for the target task, so as to obtain reliable gradient information to update the model weights. Moreover, the effectiveness of PGI on lightweight models was confirmed by designing a lightweight generalized effective layer aggregation network (GELAN) based on gradient path planning. Since the detection speed and accuracy of YOLO are affected by Non-Maximum Suppression (NMS) to a greater extent and the Transformer-based target detection algorithm DETR provides an alternative to NMS, RT-DETR [27] was designed as an efficient hybrid encoder for the fast processing of multi-scale features by decoupling intra-scale interaction and inter-scale fusion to improve the detection speed. In addition, an uncertainty-minimum query selection algorithm was proposed to provide the decoder with high-quality initial queries to improve the detection accuracy. Since the current YOLOs algorithm has achieved an effective balance between the computational cost and the detection performance, performing NMS processing during the deployment of the model will affect the inference process of the model. Therefore, YOLOv10 [28] proposes a consistent dual allocation strategy for NMS-free training, which simultaneously ensures high model performance and low inference latency. Moreover, v10 fully optimizes each component of YOLO from both efficiency and accuracy perspectives to reduce the computational overhead of the model while enhancing the detection performance. Although these current detection models have demonstrated impressive performances across diverse scene datasets, their efficacy in detecting objects within UAV aerial datasets remains limited, indicating ample room for improvement. Compounding this challenge is the existence of numerous small objects and significant variations in object sizes within UAV aerial images. The distribution of instance samples and features is imbalanced across objects of different scales, and this imbalance is exacerbated by subsequent convolution and pooling operations, resulting in the loss of crucial features.
## 1 Introduction
Previous laboratory experiments and numerical simulation results studies showed that the nonlinear flow in a fractured rock mass was mainly caused by inertial force; a larger Reynolds number resulted in a significantly nonlinear flow under a high flow velocity. The critical Reynolds number marked the beginning of the flow's conditioning from linear to nonlinear [45], and a rougher fracture surface lead to a larger critical Reynolds number [46]. Some studies also showed that the critical Reynolds number increases with an increase in shear displacement [47]. Information about the Reynolds number under different boundary conditions was also obtained through experiment and numerical simulation [48; 49]. A larger aperture, coarser fracture surface, and more fracture intersections in the DFNs will lead to nonlinear flow at a lower hydraulic gradient [50]. [PERSON] et al. found that a nonlinear flow was enhanced significantly by the secondary roughness, which results in an earlier onset of nonlinearity [51]. An experiment carried out by [PERSON] et al. indicated that different intersection angles of fractures will affect the distribution of an outlet flow, thus affecting the occurrence of a nonlinear flow [52]. Investigations into the linear and nonlinear coefficients of the Forchheimer law have been conducted based on a series of flow experiments and numerical simulations for rough fractures. Experimental studies showed that a higher confining pressure and a smaller fracture aperture result in an increase in the linear and nonlinear coefficients of the Forchheimer law, and the studies established the mathematical relationship between the nonlinear coefficient and hydraulic aperture [53; 54]. [PERSON] et al. studied the nonlinear behavior of a three-dimensional fracture network by numerical simulation; the study results show that the linear and nonlinear coefficients of the Forchheimer law decrease with an increase in the percolation density of the fracture network and that they increase with an increase in the fracture surface roughness [55]. A laboratory and numerical investigation also shows that the linear and nonlinear coefficients of the Forchheimer law decrease with an increase in the shear displacement [56; 57; 58]. However, previous experimental studies have only focused on the influence of stress on the nonlinear flow characteristics of a single fracture, and numerical studies have hardly considered the influence of stress on the fracture aperture; therefore, the study of the influence of stress on the nonlinear flow characteristics of DFNs is still insufficient. The purposes of this study are: (1) to present the nonlinear flow of jointed rock masses through laboratory flow tests, theoretical models, and numerical simulations, (2) to develop the classical Forchheimer law for consideration of the influence of stress on the variation of fracture aperture; (3) to build a hydro-mechanical coupling model for fractured rock masses, programmed with a subroutine through ABAQUS; and (4) to evaluate the application of the proposed model in tunnel engineering to predict the water inflow during tunnel construction.
## 3 Case Study Analysis of Online PD Monitoring in Power Transformers
### Transformer B
#### 3.2.1 Results of Online Monitoring of Partial Discharges
The high value of the Accuracy Index (0.728) shows that both methods agreed in 72.8% of the cases, indicating a moderately high overall agreement of the performance of these methods. However, the Jaccard Index (0.177) indicates very low agreement in the case of \begin{table}
\begin{tabular}{c c c} \hline \hline  & **AE: Pulse (1)** & **AE: No Pulse (0)** \\ \hline
**UHF: Pulse (1)** & TP = 5.9\% & FP = 9.6\% \\
**UHF: No Pulse (0)** & FN = 17.6\% & TN = 66.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for pulses recorded by UHF and AE methods in ‘Transformer B’. Figure 11: PRPD pattern of UHF PD pulses recorded on 7 November 2023, at 9:31 a.m., in the tested Transformer B’. detection of the same PD pulses, considering only the cases in which at least one of the methods registered the pulses. The low Jaccard Index value suggests that the UHF and AE methods for 'Transformer B' show significant differences in detecting PD pulses.