esa-sceva/satcom-chunk-collection huggingface dataset:
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 4 Biochar as an Efficient Adsorbent for Organic and Inorganic Pollutants
### Parameters Affecting the Adsorption of Pollutants on Biochar
#### 4.4.4 Temperature  
The adsorption capacity of biochar has been reported to also be affected by the temperature of the medium in which it is applied. Most studies reported that the adsorption efficiency increased when the temperature raised showing an endothermic nature of the adsorption process. In the study performed by [PERSON] et al. [28], the authors reported that the sorption of indigo carmine onto potassium hydroxide activated biochar increases with the increase in the temperature due to the endothermicity of the sorption process. An increase in temperature produces an increase in the mobility of dye molecule and a possible increase in the porosity of the adsorbent. This may be due, also, to the swelling effect on the internal structure of adsorbent with increasing temperature, which allows more dye to penetrate further [162]. In another study, [PERSON] et al. [157] showed that the increase in the experimental temperature for the adsorption of NH\({}_{4}\)\({}^{+}\)-N from 15 \({}^{\circ}\)C to 45 \({}^{\circ}\)C allowed for an increase in the adsorption efficiency. They claimed that the raise of temperature above 30 \({}^{\circ}\)C to a maximum of 45 \({}^{\circ}\)C is beneficial for a maximum removal efficiency.
# Evaluation of Low-Temperature Performance of Recycled
## 2 Materials and Methods
### Low-Temperature Performance Test
#### 2.8.3 Bending Beam Creep Test (BBCT)  
The bending beam creep test was conducted based on the trabecular bending test following the T 0728-2000 method specified in JTG E 20-2011. The test temperatures were \(-20\)\({}^{\circ}\)C, \(-10\)\({}^{\circ}\)C, and \(0\)\({}^{\circ}\)C. At least 5 specimens were tested at each temperature, and the average value was calculated. Before the experiment, specimens were conditioned at the test temperature for over 12 h. The loading stress was set at 10% of the failure load, with a loading time of 5400 s. The bending creep for each asphalt mixture was determined using the calculation formulas presented in Equations (5)-(7).  
\[\sigma_{0}=\frac{3\mathrm{P}_{0}\mathrm{L}_{0}}{2\mathrm{b}h^{2}} \tag{5}\]  
\[\varepsilon(\mathrm{t})=\frac{6\mathrm{h}d(\mathrm{t})}{\mathrm{L}_{0}^{2}} \tag{6}\]  
\[\varepsilon_{\mathrm{s}}=\frac{\varepsilon_{2}-\varepsilon_{1}}{(\mathrm{t}_{ 2}-\mathrm{t}_{1})\sigma_{0}} \tag{7}\]  
where \(\sigma_{0}\) is the creep bending and tensile stress of the specimen (MPa); \(\varepsilon(\mathrm{t})\) is the bending and tensile strain at the bottom of the specimen beam; \(\varepsilon_{\mathrm{s}}\) is the bending creep rate (MPa\({}^{-1}\)\(\times\) s\({}^{-1}\)); b is the width of the span-interrupted test piece (mm); h is the height of the span-interrupted test piece (mm); \(\mathrm{L}_{0}\) is the span of a simply supported beam, generally 200 (mm); \(\mathrm{P}_{0}\) is the constant concentrated load exerted on the mid-span during the test loading process (N); and d(t) is the mid-span deflection that changes with time t (mm).
## 2 Basic Structure of the VC-SOT MTJ Device  
Spintronic devices achieve the injection, transport, and detection of spin current by regulating the spin properties of electrons, thereby achieving the processing, transmission, and representation of electronic signals. Magnetic tunnel junction is a way to control electron spin in spintronics. By adjusting the magnetization direction of the ferromagnetic layer in the magnetic tunnel junction, it exhibits different macroscopic electrical characteristics, such as resistance, performed to achieve data storage or calculation. The methods of regulating magnetic tunnel junctions generally include applying magnetic fields, currents, and voltages.  
A typical VC-SOT MTJ structure (Figure 2a) contains an anti-ferromagnetic (AFM) layer (e.g., IrMn) with an MTJ positioned on top of it. The MTJ consists of three layers: the ferromagnetic (FM, e.g., CoFeB) pinned layer, the oxide layer (e.g., MgO), and the FM-free layer. The magnetization direction of the pinned layer remains fixed, while the free layer's magnetization direction can be freely altered. When the magnetization direction of the free layer is anti-parallel (AP) to the pinned layer, the MTJ is in a high resistance state. Conversely, when the magnetization direction of the free layer is parallel (P) to the pinned layer, the MTJ is in a low resistance state. These two resistive states of the MTJ can represent data \"1\" and \"0\" (AP for \"1\", P for \"0\"). A recent discovery involves a novel magnetic field-free switching mechanism [19], which utilizes an IrMn/CoFeB/MgO structure and achieves field-free SOT switching aided by an in-plane EB. As shown in Figure 2a, the new mechanism also incorporates the voltage control of the magnetic anisotropy (VCMA) effect, significantly reducing the SOT switching current by applying a bias gate voltage (V\({}_{\text{bia}}\)). The magnetization of the free layer in the MTJ device can be pulled towards the in-plane direction by both the SOT and voltage torques. In addition, in-plane magnetic symmetry is disrupted by the exchange bias between the AFM/FM (IrMn/CoFeB) layers of the MTJ. In sub-nanoseconds, the deterministic and complete reversal of the magnetization in the free layer of the MTJ device can be achieved.  
Figure 2b shows a typical VC-SOT MTJ bit-cell structure. It consists of a VC-SOT MTJ and two NMOS transistors. During the write operation, for V\({}_{0}\) = \"1\", a write voltage is applied between VT\({}_{1}\) and VT\({}_{2}\) to generate a write current (Iwrite). The magnetization direction of the free layer of MTJ is then switched based on the direction of Iwrite. Simultaneously, V\({}_{1}\) = \"1\", and a bias voltage (V\({}_{\text{bia}}\)) is connected to the MTJ, reducing the energy barrier required for the magnetization direction switch. The read operation follows  
a different path. Specifically, during the read operation, the read current (Iread) flows through the MTJ, with the MTJ state determining the amplitude of Iread.
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 4 Results and Discussion
### Characterization of the Power Spectrum
#### 4.2.1 Characterization of the Power Spectra at Different Magnitudes  
Based on the magnitude groupings outlined in Section 4.1.1, the power spectra for all strong-motion components (EW, NS, and UD) were calculated, and their mean spectra were derived for all groups. Figure 9 illustrates the mean power spectral curves. The maximum values of the mean curves (\(P_{max}\)) and their associated predominant periods (\(T_{p}\)) are summarized in Table 3.  
As shown in Figure 9 and Table 3, the power spectra of the three components are significantly influenced by the earthquake magnitude. On average, as the magnitude increases, both \(P_{max}\) and \(T_{p}\) display a clear upward trend. Within the same magnitude range, there is generally not a significant difference between \(P_{max}\) and \(T_{p}\) for the horizontal and vertical directions, except in the 7.1-8.0 magnitude subgroup.
## 2 EMAT Technology
However, NdFeB magnets cannot be used at temperatures higher than 200 \({}^{\circ}\)C.  
A cooling system may also be required so that both the magnets and the coil will operate efficiently. [PERSON] _et al._ have designed and tested a water-cooled EMAT that can obtain signals up to 1000 \({}^{\circ}\)C for thickness measurements. The EMAT was exposed to the heat source for as much time as it needed for the signal to be recorded and then it was removed [33]. Oil- and air-cooled EMATs have also been designed. Generally, the reported high-temperature EMATs seem to operate at high temperatures for short periods of time, which are suitable for inspection, but they cannot be used in long-term condition monitoring. Consequently, an EMAT operating at high temperatures (500 \({}^{\circ}\)C) for long periods of time is required and could be used for the structural health monitoring of high-temperature structures such as the absorber tubes.
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
## 2 Sun Position
### Anomaly Mean \(\rightarrow\) Eccentric \(\rightarrow\) True  
Step 5 is probably the most famous step in this algorithm (see for example [24]), involving the transformation of the mean anomaly \(M\) to the eccentric anomaly \(E\) (Sun's position in a circular orbit with real changing speed around Earth) by solving the Kepler's equation (with \(M\), \(E\) in radians) [22]:  
\[M=E-e\sin E \tag{2}\]  
where \(e=0.016708\) is the eccentricity of the Earth-Sun orbit (\(e=0\) for a circular orbit). This can be achieved by successive iterations, using a simple (fixed point) convergence method:  
\[E_{i}=M+e\sin E_{i-1}\;\;\text{with}\;\;E_{0}=M \tag{3}\]  
or a Newton-Raphson method:  
\[E_{i}=E_{i-1}-\frac{E_{i-1}-e\sin E_{i-1}-M}{1-e\cos E_{i-1}}\;\;\text{with}\; \;E_{0}=M \tag{4}\]  
The second method is slightly faster, but the accuracy is similar. The convergence is very quick (4-5 iterations is usually enough) due to the very small eccentricity of the Earth-Sun orbit.  
Converting eccentric anomaly \(E\) to true anomaly \(\
u\) (Sun's position in the true elliptic orbit with real changing speed around Earth) can be achieved applying the equation (step 6) [22; 23]:  
\[\tan\frac{\
u}{2}=\sqrt{\frac{1+e}{1-e}}\tan\frac{E}{2} \tag{5}\]  
In general, the \(\tan^{-1}\) inverse function has two solutions, \(\alpha\) or \(\alpha+180^{\circ}\), but in this case, the multiplication by 2 solves the indetermination, as \(2(\alpha+180^{\circ})=2\alpha\).
## 3 Results
### Spatio-Temporal GrabCut Segmentation  
First, we test the proposed ST-GrabCut segmentation on the sequence from the public cVSG corpus. The results for the different experiments are shown in Table 1. In order to avoid the manual initialization of classical GrabCut algorithm, for all the experiments, seed initialization is performed applying the commented person HOG detection, face detection, and skin color model. First row of Table 1 shows the overlapping performance of Equation (13) applying GrabCut segmentation with \(k\)-means clustering to design the GMM models. Second row shows the overlapping performance considering the spatial extension of the algorithm introduced by using Mean Shift clustering (Equation (5)) to design the GMM models. One can see a slight improvement when using the second strategy. This is mainly because Mean Shift clustering takes into account spatial information of pixels in clustering time, which better defines contiguous pixels of image to belong to GMM models of foreground and background. Third performance in Table 1 shows the overlapping results adding the temporal extension to the spatial one, considering the morphology refinement based on previous segmentation (Equation (7)). In this case, we obtain near 10% of performance improvement respect the previous result. Finally, last result of Table 1 shows the full-automatic ST-GrabCut segmentation overlapping performance taking into account spatio-temporal coherence, and the segmentation refinement introduced in Equation (6). One can see that it achieves about 25% of performance improvement in relation with the previous best performance. Some segmentation results obtained by the GrabCut algorithm for the cVSG corpus are shown in Figure 6. Note that the ST-GrabCut segmentation is able to robustly segment convex regions. We have also applied the ST-GrabCut segmentation methodology on the image sequences of UBDataset. Some segmentations are shown in Figure 6.
# AI-Driven Waste Management in Innovating Space Exploration
## 6 International Collaboration and Policy
### Private Companies, International Policy, and Global Cooperation Need  
The increasing involvement of private companies like SpaceX and Blue Origin in space exploration introduces new dynamics and technological innovations. These companies are pioneering commercial space travel, offering fresh perspectives in operations management, including manufacturing, supply chain, and waste management. For instance, SpaceX's Starship is being developed for Mars missions and requires sophisticated waste management systems for long-duration space habitation. Figure 9 shows a line graph showing the cumulative waste generated over the expected duration of a Mars mission based on crew size and mission length.  
Figure 8: Integration and timeline of waste management systems with life support systems.  
Figure 9: Projected waste accumulation during a Mars mission.  
As space exploration expands to include more nations and commercial entities, the need for an international regulatory framework for waste management becomes increasingly urgent. Based on treaties like the Outer Space Treaty of 1967, current space law provides a basic structure but lacks detailed waste management and debris mitigation regulations. Efforts are underway to develop comprehensive guidelines, such as the United Nations Committee on the Peaceful Uses of Outer Space's Space Debris Mitigation Guidelines, which suggest the best practices to limit debris but do not address waste management beyond low-Earth orbit. Significant international collaboration is essential to create policies encompassing waste reduction, recycling, and handling hazardous materials. The space community can ensure sustainable waste management by enhancing cooperation among space agencies, private companies, and regulatory bodies. As the number of missions increases, the coordinated management of space resources is vital to minimise environmental impacts and establish a sustainable human presence in space for future generations.
# Field Evaluation and Application of Intelligent Quality Control Systems
## 3 Results
### Analysis  
To analyze the correlation between the testing methods, the median (i.e., a commonly used data analysis measure) was chosen. The median represents the value that lies exactly in the middle of a data set when arranged in order. Unlike the mean, it is less affected by outliers, making it useful for indicating the central tendency. This study conducted a correlation analysis using the median values from the intelligent QC test (CMV) and the traditional QC tests (PLT, LWDT, and DCPT). The intelligent QC tests and PLT were conducted at compaction counts of two, four, six, and seven. Apart from the seventh compaction, the correlation with CMV was high, as shown in Figure 8. However, the R-squared value was relatively low compared to the other test values, likely due to the variability of the point-test method in PLT, while CMV evaluated the entire section. The correlation between the intelligent QC method and LWDT was very high, with a correlation coefficient of approximately 0.91 or higher, as shown in Figure 9. Additionally, the correlation analysis between the intelligent QC method and DCPT showed an exceptionally high correlation, exceeding 0.95, as shown in Figure 10.  
The correlation analysis of the four testing methods showed that each method was statistically significant at the 0.01 level, as shown in Table 3. This outcome indicated that the correlation between the variables was simultaneously very strong and statistically significant. Additionally, the Pearson correlation coefficient was also very high, suggesting that the intelligent QC methods were feasible for field application.  
Figure 8: Correlation analysis results between CMV and PLT.  
Figure 7: Normal distribution box plot of CMV values for each compaction pass across all sections.  
Figure 10: Correlation analysis results between CMV and DCPT.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Correlation**} & & \\  & **CMV** & **DPI** & **E** & **PLT** \\ \hline \multirow{4}{*}{CMV} & Pearson correlation & 1 & \(-\)0.977 ** & 0.956 ** & 0.829 \\  & Significance probability & & 0.000 & 0.001 & 0.171 \\  & (two-tailed) & & 7 & 7 & 4 \\  & Pearson correlation & \(-\)0.977 ** & 1 & \(-\)0.895 ** & \(-\)0.749 \\  & (two-tailed) & 0.000 & & 0.006 & 0.251 \\  & N & 7 & 7 & 7 & 4 \\  & Pearson correlation & 0.956 ** & \(-\)0.895** & 1 & 0.746 \\ E & Significance probability & 0.001 & 0.006 & & 0.254 \\  & (two-tailed) & & 7 & 7 & 4 \\  & N & 7 & 7 & 7 & 4 \\  & Pearson correlation & 0.829 & \(-\)0.749 & 0.746 & 1 \\ PLT & Significance probability & 0.171 & 0.251 & 0.254 & \\  & (two-tailed) & N & 4 & 4 & 4 \\ \hline \hline \end{tabular}  
* The correlation is statistically significant at the 0.01 level (two-tailed).  
\end{table}
Table 3: Correlation between intelligent quality control methods and conventional quality control.  
Figure 9: Correlation analysis results between CMV and LWDT.
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Simulations  
Ultrasound RF element data of blood and tissue were simulated, which are represented by a set of random point scatterers. About 300 scatterers per mm\({}^{3}\) where used and each physical transducer element was subdivided into 10 by 10 mathematical elements to increase computational accuracy. A sampling frequency of 180 MHz was used, the resulting RF element data were down sampled to 36 MHz to mimic experimental settings. The speed of sound was assumed to be 1540 m/s.
# Design of a 120 W Electromagnetic Shock Absorber for Motorcycle Applications
## 1 Introduction
Under 20 mm/1.5 Hz settings, the efficiency is 59.07%. Third, some EHSAs accomplish motion conversion using the ball screw method. These are known as BS-EHSA (ball screw). The studies [33; 34] describe approaches for developing these systems with power outputs of up to 35 W and damping coefficients up to 3000 Ns/m. In [35], a high-efficiency shock absorber for automobiles with a 10,580 Ns/m damping coefficient and recovery of close to 7 W. Movement rectification for BS-EHSA shock absorbers is recommended in [36; 37]. To replace traditional oil dampers in car suspensions, a study [36] proposes a novel form of mechanical motion rectifier-based EHSA using a ball-screw mechanism and two one-way clutches. In [38], the development of a BHSA system with separate generators to improve damping force and energy recovery. Finally, works such as [39] discuss the effect of friction factors on dynamic behavior and suspension recover ability. Finally, the cable drive EHSA (CD-EHSA) is presented in [40]. The EHSA (CD-EHSA) could generate 25 W at speeds ranging from 20 to 30 km/h, with a conversion efficiency of up to 60%. In addition, there has been research on the control of EHSA systems for energy recovery. A multiobjective H\(\infty\) active control design technique for EHSA systems is provided in [41; 42]. In [43], a generic control design technique for EHSA systems applied to civil engineering applications is described. The use of an EHSA system in motorcycles instead of traditional oil shock absorbers for energy conversion recovery is investigated in this study. The motorcycle used for this study is a light, electrically driven vehicle. There are two basic technologies for energy recovery, as outlined in the state of the art: piezoelectric EHSAs and electromagnetic EHSAs. The first is discarded because of its low energy recovery capacity and zero damping capacity. Linear and rotational electromagnetic dampers, on the other hand, are included within electromagnetic dampers. In this study, a linear electromagnetic shock absorber is investigated. The design targets include average power and power density of 120 W and 104 W/cm\({}^{3}\), respectively. To maximize the output power, a one-to-one slot-pole ratio combination and a hybrid permanent magnet structure were used. Additionally, the volume of a permanent magnet, its magnet ratio, and coil volume were parametrically studied to enhance the power. Therefore, the detailed model could provide average outpower and power density of 124 W and 107.2 W/cm\({}^{3}\), respectively. However, the 30% larger permanent magnet structure is considered. magnet used, which resulted in a larger cogging force, also led to rider discomfort during the operation. The rest of the article is organized as follows. Section 2 presents design specifications such as geometry, operating conditions, and an external circuit. Section 3 shows a detailed design, which was obtained by using parametric studies of various geometry variables. Section 4 concludes this article.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 5 High-Speed Scenario
### Summary  
The traffic of private networks deployed for high-speed railways dynamically varies due to the running trains. In this section, we propose several refinement policies, including frequency band policy, idle detection, and location division, which can effectively improve the resource allocation and service quantity.
# A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments
## 2 Related Work
### Time Series Analysis in Network Security  
Recognizing network traffic as time series data is crucial. While DL models like LSTMs implicitly handle temporal aspects, specific statistical time series analysis techniques offer complementary capabilities. Traditional methods like ARIMA have been used for traffic prediction, and while these methods can indirectly support anomaly detection [30], they often lack the scalability for real-time, high-dimensional IDS. STL [31] is a versatile technique for separating a time series into trend, seasonal, and remainder components. Its robustness to outliers makes it suitable for analyzing noisy network data [10]. While STL is applied in various anomaly detection contexts [32; 33], its potential as a dedicated pre-filtering mechanism to specifically enhance the FNR/FPR performance of subsequent deep learning classifiers in large-scale IoT IDS frameworks has not been extensively investigated. Most research either uses DL alone or combines different DL/ML models, rather than leveraging statistical decomposition to preprocess input for DL classifiers. Additionally, recent work [34] in this field shows a promising model for deep learning intrusion detection systems in time series data, and a comparative study of time series anomaly detection models for industrial control systems is also showing promising results in this area [35]. Leveraging STL's ability to isolate residual components, often indicative of anomalies, as a pre-filter for deep learning classifiers, which excel at learning complex patterns, represents a promising hybrid approach specifically suited for the noisy and time-sensitive nature of IoT network data.