esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Outlet Shrinkage on Hydraulics in Hyper-Concentrated Sediment-Laden Flow
## 3 Experimental Results
### Hydrodynamic Pressure
#### 3.3.1 Relative Time-Averaged Pressure  
The fluctuating pressure of the bottom plate was measured synchronously. The relative time-averaged pressure distribution along the centerline of the bottom is shown in Figure 9a-c, where the ordinate is the relative time-averaged pressure at each test point, and the abscissa is the distance between the measuring point and the beginning of the plunge pool.  
The maximum value of the relative time-averaged pressure affected the stability and safety of the bottom of the plunge pool. Observing the value of time-averaged pressure, the results indicated that the distribution of pressure for the hyper-concentrated sediment-laden flow was consistent with that of fresh water. The relative time-averaged pressure distribution in the front of the plunge pool, away from the jet impact region, was relatively stable, and its value was basically proportional to the water depth. The relative time-averaged pressure in the jet impact region increased sharply, forming a distinct peak. The position where the peak appeared was only relevant to the upstream and downstream water head, regardless of the alteration in sediment concentration; with the same upstream and downstream water head, the peak value of relative time-averaged pressure increased continuously with the increase in sediment concentration, consistent with the experimental results of [PERSON] [21]. The time-averaged pressure away from the water nappe impact region tended to be stable at first and then rose slightly, as the nappe fell into the plunge pool and formed a dammed water area downstream of the impact region. The water depth in this area was greater than the plunge pool, so the time-averaged pressure increased again.  
Figure 9: Relationship between the relative time-averaged pressure and sediment concentrations at various shapes; (**a**) shape 1; (**b**) shape 2; (**c**) shape 3; (**d**) pressure comparison for various sediment concentrations at the impact point.  
Figure 9d depicts the relative time-averaged pressure at the impact point. The result shows that the narrower outlet width caused more air entrainment of the water nappe and a lower impact pressure on the bottom. As the sediment concentration increased, the dispersion of the water nappe decreased and the effect of shape changes of the outlet was reduced. As the sediment concentration increased, the diffusion and air entrainment of the jet decreased. Higher pressure occurred due to the increase in viscosity of the liquid.
## 3 Results and Discussion  
### Lab-Scale Phase  
The dosages of each coagulation agent (FeCl3 and PAC) ranged from 270 mg\(\cdot\)L\({}^{-1}\) to 1088 mg\(\cdot\)L\({}^{-1}\). Prior to each test, the pH level was adjusted to a range close to 7 in order to establish the most favorable conditions for the coagulation process. After evaluating the test's design, it can be determined that the most effective coagulant was PAC with a dosage of 675 mg\(\cdot\)L\({}^{-1}\), leading to a 30% reduction in COD (Figure 2a). Increasing the dosage of coagulant did not contribute to enhancing the performance of the process. The use of PAC resulted in an observed rise in the content of chlorides in the treated BWW that should be further investigated in future studies.  
This same approach was also used in the subsequent laboratory tests for optimizing the dosage of the flocculation agent. Based on the findings, it can be concluded that CAF is not suitable for treating this particular type of wastewater since it does not form solid flocs and there is an undissolved substance present in the liquid above the sediment (Figure 2b).  
In the case of optimal conditions (PAC: 675 mg\(\cdot\)L\({}^{-1}\), AAF: 40 mg\(\cdot\)L\({}^{-1}\)), the lab-scale tests showed that the treatment was highly effective in removing nutrients (Ntot: 89%, Ptot: 89.9%, P-PO4\({}^{3-}\): 99.7%) and TSS (53.2%). In this study, the high removal of nitrogenous forms and phosphates reaches the values obtained in previous research, where BWWs were treated with coagulants assisted by the use of electric current (electrocoagulation), probably due to the optimal conditions of pH and reagent dosage [27]. In particular, the nearly total removal of P-PO4\({}^{3-}\) obtained in the present study can be attributed to a series of adsorption (incorporation of phosphates to the solid particles) and chemical precipitation, which involves reversible and irreversible precipitation processes [28]. The insoluble precipitate will then be removed from BWW by the final setting to end up in the sludge. This value is also confirmed in previous studies where different coagulants were used. For instance, the use of aluminum sulfate in the coagulation process removed 90% of P-PO4\({}^{3-}\) under optimal  
Figure 2: (**a**) COD removal as a function of the dosage of different coagulants, and (**b**) final turbidity of the BWW treated with PAC as a function of the dosage of different flocculants. The bars represent the 95% confidence interval of the results from diverse analyses (n = 3) made on the same sample.  
pH and dosage conditions [28], while polyacrylamide and PAC removed more than 94% of phosphorous from real phosphate mine WW [29]. However, the removal efficiency for organic substances was limited, with COD removal at 29.5% and BOD\({}_{5}\) removal at 31.9% when PAC was used.  
Figure S3 illustrates the visible difference between the BWW prior to and following coagulation and flocculation, using the most favorable conditions. The findings have also been examined on a broader scope in industrial-scale facilities (Section 3.2).
# Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems
## 2 Construction of Milling Machine  
In this study, a horizontal milling machine was designed, as shown in Figure 1, in which the table was mounted on the machine base and was driven through the \(X\)-axis linear feeding mechanism. The vertical column was mounted on the machine base through the \(Z\)-axis feeding mechanism. The spindle ram was mounted on the vertical column, which can move along vertical direction through the \(Y\)-axis feeding mechanism. To enhance the rigidity of the feeding mechanism, the guideway systems were assembled by linear roller guides and sliding guides, in which the sliding guides were grounded, hardened, and then coated with scrapped Turcite-B liners. The use of the Turcite-B slideways can also increase the damping property of the machining system [23]. All the structural components were fabricated from gray iron. In each feeding mechanism, the driven ball screw (Hiwin-R32-10K4-FSC) has a diameter of 32 mm, a lead pitch of 10 mm, and a basic dynamic load rating C of 2.52 kN [24]. The screw shaft was slightly preloaded to a level of 0.05C so as to lessen the axial backlash. Moreover, two standardized ball-screw support units coded EK15 were used at both ends of the screw shaft to ensure its rigidity. Each feeding system was also equipped with linear roller guide modules (INA-RUS19069), which can be preloaded to different amounts by means of the adjusting the gib in spaces, normally between 10% and 20% of the basic dynamic loading capacity [25]. In addition, a motorized spindle unit was installed on the feeding ram of the spindle.  
The key factor for accurate construction of the analysis model of a machine tool structure is the modeling of the feeding mechanism, which usually consists of various linear components of rolling motion type. These interfaces mostly affect the structure characteristics in loading and damping capacities. Hence the modeling of the interface greatly determines whether the simulation results can approach the real characteristics of the system. A method for the simulation of the rolling and sliding interfaces is presented in next section as follows.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 4 Discussion
### Comparison with Previous Studies  
Our study builds upon the foundational work of Dr. [PERSON] [8], who explored iBeacon placement for indoor positioning. By improving upon their methodology, particularly through the development and application of the Positive Weighted Centroid Localization (PWCL) and HYBRID-MAPPED algorithms, we achieved a marked reduction in localization errors [7; 8; 21; 22]. The average error in our HYBRID-MAPPED method was 1.44 m, which is a significant improvement compared to traditional WCL methods that showed an average error of 2.51 m. This aligns with findings from similar studies that emphasize the importance of algorithmic enhancements and optimal hardware placement for reducing localization errors.  
Our HYBRID-MAPPED method demonstrates superior performance when compared to traditional approaches such as the standard Weighted Centroid Localization (WCL) and the recently proposed unified frameworks based on factor graphs [13]. Specifically,  
Figure 12: Bar chart of mean total error (in meters), variance and standard deviation in four different methods.  
Figure 13: Bar chart of the average total error (in meters) compared to the baseline research.  
our method achieves an average localization error of 1.44 m, a significant improvement over the WCL method with an average error of 2.51 m. Additionally, the integration of cooperative techniques as explored by [PERSON] et al. [14] has shown promise in further enhancing accuracy, but our approach refines these results by incorporating additional filtering mechanisms to address environmental noise and signal variations. These findings highlight the robustness and applicability of our proposed methods in various indoor environments.
# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 3 Proposed Method
### Feature Extractor  
The DAN-DAM model feature extractor adopts a deep convolutional neural network (DCNN) whose composition is the same as a general shallow convolutional neural network, except that the number of convolutional layers is increased. DCNN can not only mine the general features of the signal, but also fully mine the special characteristics of the signal with the increase of the layer, getting rid of the dependence of the traditional signal processing technology on the diagnosis experience. In addition, in order to make the extracted features from the source domain and the target domain distributed consistent in the high-dimensional feature space, MMD is added to the last layer of the CNN. By minimizing the MMD distance, the feature distribution of the target domain is close to that of the source domain, which avoids the degradation learning caused by adversarial and improves the diagnostic accuracy of the network at the same time.
## 2 The MCVSK and MVSK Portfolio Optimization Model  
### The Measure of Return and Risk  
As the most fundamental objective, the return to the portfolio always catches investors' attention. In the portfolio optimization problem, our aim is to invest our capital in some assets in order to obtain some desirable characteristics of the total return on investment.  
Let \(n\) denote the number of assets, which are available for investment in the beginning of a fixed period, and we assume that we have a fixed capital to be invested in them. Then we use \(x=\left(x_{1},x_{2},\ldots,x_{n}\right)^{T}\) to denote the fractions of initial capital invested in \(n\) assets. Thereinto, \(i=\frac{w_{i}}{w}\), \(i=1,\ldots,n\) where \(w_{i}\) is the capital invested in asset \(i\) and \(w\) is the total amount of capital to be invested. Let \(X\) denote a set of feasible portfolios where \(x\in X\), and it is clear that \(X\in R^{n}\) is a bound convex polyhedron. Besides, let \(R_{i}\left(\xi\right)\), \(i=1,\ldots,n\) denote the return of asset \(i\) in the case of discrete distribution, where \(\xi:\Omega\rightarrow\Xi\) is a random vector on probability space \(\left(\Omega,F,\mathbf{P}\right)\)[27]. and we assume that \(E\left[\mid R_{j}\mid\right]<\infty\) for all \(j=1,\ldots,n\). In a word, if we have a fixed capital, the return to portfolio \(g\left(x,\xi\right)\) can then be formulated as:  
\[g\left(x,\xi\right):=R_{1}\left(\xi\right)x_{1}+R_{2}\left(\xi\right)x_{2}+ \ldots+R_{n}\left(\xi\right)x_{n} \tag{1}\]  
The measure of risk has many forms, such as variance, semi-variance, mean absolute variance, VaR, CVaR and so on. Variance is the risk measure of Markowitz MV model, which has a long history and a wide range of applications. On the basic of variance, semi-variance, mean absolute variance and other risk measures are appeared. In this paper, we mainly study two kinds of risk measures: VaR and CVaR. VaR is one of the most well-known downside risk measures due to its intuitive meaning and wide spectra of applications in practice [8]. For a fixed level \(\alpha\), the value at risk \(VaR_{\alpha}\) is defined as the \(\alpha\)-quantile of the cumulative distribution function \(F_{Y}\)[28]:  
\[VaR_{\alpha}\left(Y\right)=F_{Y}^{-1}\left(\alpha\right) \tag{2}\]  
where \(Y=-X\). However, VaR still has several noticeable limitations and drawbacks. For example, it's insensitive to the magnitude of losses beyond VaR, and it's not a coherent risk measure [9]. Meanwhile, CVaR measures the conditional expectation of losses beyond VaR, which is a coherent risk measure. Therefore, CVaR is theoretically more attractive and partly resolve the shortcomings of VaR [29]. The CVaR is defined as the solution to an optimization problem:  
\[CVaR_{\alpha}\left(Y\right):=inf\left\{a+\frac{1}{1-\alpha}E\left[Y-a\right]^ {+}:a\in\mathbb{R}\right\} \tag{3}\]  
where \(\left[z\right]^{+}=max\left(z,0\right)\). Besides, for smooth \(F_{Y}\), CVaR equals the conditional expectation of \(Y\)[30]:  
\[CVaR_{\alpha}\left(Y\right)=E\left(Y\mid Y>VaR_{\alpha}\left(Y\right)\right) \tag{4}\]
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
## 4 Suggested Swarm-Inspired Metaheuristics
### Firefly Algorithm (FA)  
The FA [28] is a swarm-inspired metaphor conceived for global optimization problems. FA models the impact of interactive attractiveness between fireflies based on their generated light intensity which is reflected by its brightness level. The latter controls their movement respecting that the less brightening fireflies moves toward the brighter ones. Fireflies use light attraction to grab other entities like prey, which is mandatory to its life activities.  
The light intensity of any given firefly \(\mathbf{i}\) is proportional to its distance to any other firefly \(\mathbf{j}\) and it is calculated using Equation (3):  
\[I=\frac{I_{S}}{|r^{2}|} \tag{3}\]  
Considering:  
* \(I_{S}\) the initial light intensity.  
* \(r\) represents the distance between fireflies \(\mathbf{i}\) and \(\mathbf{j}\).  
The brightness value (\(\beta\)) of a firefly agent reflects the intensity value (\(I\)) and is calculated based on the initial brightness (\(\beta_{0}\)) as mentioned in Equation (4):  
\[\beta=\beta_{0}\times\exp^{-\gamma r} \tag{4}\]  
The attractiveness value (\(\alpha\)) is extracted from \(\beta\) value as calculated in Equation (5):  
\[\alpha=\alpha_{0}\times\exp^{-\gamma r^{2}} \tag{5}\]  
Consequently, the movement of a firefly \(\mathbf{i}\) toward a better firefly entity \(\mathbf{j}\) is a stochastic process calculated using the Equation (6):  
\[x_{i}^{t+1}=x_{i}^{t}+\alpha_{ij}(x_{j}^{t}-x_{i}^{t})+\mathit{Rand} \tag{6}\]  
Considering:  
* \(x_{i}^{t}\): the position of firefly \(\mathbf{i}\) at instant \(\mathbf{t}\).
* \(x_{i}^{t+1}\): the position of firefly \(\mathbf{i}\) at instant \(\mathbf{t+1}\).
* \(x_{j}^{t}\): the position of firefly \(\mathbf{j}\) at instant \(\mathbf{t}\).
* \(\mathit{Rand}\): a randomization parameter between 0 and 1.  
The pseudo-code of the FA in Algorithm 1.  
```
1:Initialize fitness function \(f(\mathit{element})\) for each firefly agent \(\mathit{element}\).
2:Initialize positions of fireflies.
3:Calculate initial Brightness \(FA_{Brightness}\) intensity for all fireflies.
4:Check \(N_{LIST}\) for \(\mathit{Bdle}\);
5:while\(Max_{Iteration}\) not reached do
6:for each \(Firefly(i)\) in \(Firefly_{Population}\)do
7:for each \(Firefly(j)\) in \(Firefly_{Population}\)do
8:\(Firefly(i)_{BRIGHTNESS}\) = Calculate_brightness (\(i\));
9:\(Firefly(j)_{BRIGHTNESS}\) = Calculate_brightness (\(j\));
10:if\(Firefly(j)_{BRIGHTNESS}>Firefly(i)_{BRIGHTNESS}\)then
11: Move \(Firefly(i)\) toward \(Firefly(j)=Cand_{SCF}\);
12:endif
13:endfor
14:endfor
15: Evaluate new solutions
16: Rank fireflies and deduce best local best firefly \(firefly_{GBest}\)
17:endwhile
```  
**Algorithm 1** Pseudo-code of FA metaheuristic.
## 4 Rapid Prognostic Model Validation and Analysis  
For the planar configuration, cylindrical configuration, and circular truncated conic configuration, five groups of zonal parameters are randomly generated, and the angular coefficients are calculated using the PSO-SVR model and Monte Carlo method, as shown in Table 2. It can be seen that the angular coefficients obtained by the rapid prognostic model are very similar to those obtained by Monte Carlo simulation, and the maximum error is 0.003, which meets the application requirements. The angular coefficient obtained by the rapid prognostic model is 0.915, and the time taken is 0.002 s, improving efficiency by 29,750 times. The Monte Carlo method takes an average of 59.5 s for each calculation, and the rapid prognostic model takes an average of 0.002 s, which is significantly more efficient than the Monte Carlo simulation method. Figure 5: Monte Carlo-based simulation of base models. (**a**) Planar model (orthogonal); (**b**) planar model (top and bottom offset); (**c**) cylindrical model (orthogonal); (**d**) cylindrical models (top and bottom offset); (**e**) round table model (orthogonal); (**f**) round table model (top and bottom offset). To further verify that the change rule of the angular coefficients obtained from the rapid prognostic model is consistent with the Monte Carlo method and conforms to the thermal law when the parameters of the infrared cage are changed, the change analysis of the key parameters is carried out by using the control-variable method. In the process of infrared cage design, since the curvatures _K1_ and _K2_ are determined by the spacecraft structure, the thermal design of the infrared heating cage is mainly concerned with the correspondence between the spacing between the infrared cage and the spacecraft, the partition height/arc length, and the angular coefficient. The spacecraft partitions are numbered as shown in Figure 6, where the spacecraft partition directly opposite to the infrared cage partition has a bias of (0,0), and the peripheral spacecraft partitions are numbered according to the transverse bias and longitudinal bias in turn. The change rule of the angular coefficients with spacing can be obtained by using the rapid prognostic model, as shown in Figure 7, where the black circles are the angular coefficients corresponding to the bias (0,0), (0,1), (1,0) partitions obtained using Monte Carlo simulation. The results of the rapid prognostic model are very close to the Monte Carlo method. In addition, data comparison reveals that (1) the angular coefficients of the
# Tire Bubble Defect Detection Using Incremental Learning
## 2 Proposed Method  
Since the tire factory cannot provide enough defect images for model training, in practice, it often happens that the recognition rate of the model trained in the laboratory is greatly reduced when it is actually imported into the field. In order to shorten the time for importing the model into the field, this paper proposes an incremental learning method for bubble detection on tire tread and sidewalls. We test the lab-trained model with images from the tire production site and collect misidentified samples, incrementally train by freezing the Yolo Backbone, and only re-train the Neck and Prediction network layers to improve defect detection rates [11]. The main steps are described as follows:  
1. Manually mark the position and type of the bubble and classify it into one of the six types;
2. Perform image enhancement and increase the number of training samples;
3. After resizing, use the YOLO architecture proposed in this paper for model training;
4. Overlay the detected defects on the original image for visualization.  
We apply incremental learning to the inspection process to account for potentially overlooked defects. Incremental learning allows small models to be trained on the missed defects and improves the model's accuracy. The process is as shown in Figure 1.  
Figure 1: The bubble detection process.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 3 Countermeasures against Web Tracking
### Tracking Measurement
#### 3.1.2 Measurement of User Browsers on a Large Scale  
To determine how unique each browser is in tracking, researchers use webpages with tracking and fingerprinting scripts to assess the degree of uniqueness of each browser in tracking by collecting changes in the entropy of browser environments and fingerprint elements. It can help identify the tracker code based on the collected data. We introduce Panopticlick, AmiUnique, and Hiding in the Crowd, which are representative studies.  
Panopticlick is a project launched by the Electronic Frontier Foundation (EFF) that provides information about the user's browser and basic knowledge of tracking and fingerprinting [49]. [PERSON] et al. [50], based on that site, collected fingerprints from 470,161 browser samples and found that 1 out of 286,777 browsers had the same fingerprints as others.  
AmIUnique can measure the AdBlock filter-list in the user's browser, including HTTP Header, WebGL, and canvas elements, and provides fingerprinting statistics for users and visualizes how unique the user is for each element as a percentage [51]. [PERSON] et al. [52], based on that site, analyzed 118,934 fingerprints and confirmed that 89.4% of fingerprints were unique.  
[PERSON] et al. [53] conducted a study where they inserted tracking scripts into a popular website, collected 2,067,942 browser fingerprints over six months, and compared the results with previous studies using Panopticlick and AmIUnique. In analysis, 62% of mobile device fingerprints had unique canvas values in the analysis, while 30% of desktop fingerprints had unique plugin combinations. The results of the study indicated that the mobile device's own features significantly impacted the fingerprint, while user customization on desktops had a major influence on the fingerprint. Studies utilizing Panopticlick and AmIUnique may gather user fingerprints based on their website access, which could bias their results. That study identified 33.6% of unique fingerprints, while previous studies showed that collecting large-scale fingerprints, excluding user intent, could identify more than 80% of unique fingerprints. The findings show that browser fingerprinting may not be effective on a very large scale, but it is an important factor in identifying users on asmall scale. In future research, we need to prepare for new fingerprinting techniques using extension fingerprinting and newly introduced APIs, and develop methods to minimize user bias.