esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Variations in NH4+-N, NO3-N, TN, and TOC during the Composting  
The accumulation of NH4+-N is mainly due to the mineralization and ammonification of organic nitrogen. As shown in Figure 3a, NH4+-N content showed a trend of increasing first (from day 0 to 2) and then decreasing in three treatments. The NH4+-N content in C1, I1, and I2 increased from initial 1.82 g/kg DM to the peak 5.53 g/kg DM, 4.54 g/kg DM and 4.18 g/kg DM on day 2, respectively. Afterward, NH4+-N content kept going down. NH4+-N content was decreased to 1.14 g/kg DM, 0.94 g/kg DM and 0.91 g/kg DM on day 20. By the end of composting, the final NH4+-N content of C1, I1, and I2 treatments decreased by 37.4%, 48.4% and 49.9%, respectively.  
The initial NO3-N concentrations of the three treatments were low and the growth rates were slight at the initial phase of the composting process (Figure 3b). The NO3-N concentrations in C1, I1, I2 increased from 0.20, 0.17, 0.19 g/kg DM on day 8 to the 0.45, 0.48, 0.51 g/kg DM on day 20, respectively. The NO3-N content of C1 treatment was significantly lower than that of I1 and I2 treatments.  
The TOC content in all the treatments decreased gradually during the composting process (Figure 3c). The rates of TOC degradation in the thermophilic phase were slightly higher than those in the curing period. At the end of composting, the TOC contents of C1,  
Figure 3: Evolutions of NH4+-N content (**a**), NO3-N content (**b**), TOC content (**c**) and TN content (**d**) during the composting. The error bars are standard deviation (\(n=3\)).  
1, and I2 were 295.10 g/kg, 317.56 g/kg, 304.78 g/kg, and the TOC losses were 51.26%, 47.55%, 49.66%, respectively. Higher TOC loss occurred with the C1 treatment compared with I1 and I2.  
As shown in Figure 3d, TN content showed a trend of slightly decreasing first and then increasing in the three treatments during the whole composting process. The minimum TN content value was detected in the C1 treatment on day 4 and I1 and I2 treatments on day 2. The TN content of C1, I1, and I2 peaked at the end of composting with 15.91 g/kg DM, 16.35 g/kg DM and 16.21 g/kg DM, respectively. During the whole composting process, the TN content of C1 treatment was lower than that of I1 and I2.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 4 Discussion
### Sensitivity to Noise
#### 4.2.3 Harmonic Noise  
In this case, Noise() is a cosine function with a given frequency (for this analysis, a noise frequency of 400 Hz has been considered, a frequency one decade larger than the measured signals). Figure 15 represents the functions \(P_{E}(\mathrm{t})\) and \(P_{\mathrm{C}}(\mathrm{t})\) obtained using Equations (32) and (33) with different noise levels.  
Table 5 shows how adding a gaussian noise to \(P_{E}(\mathrm{t})\) and \(P_{\mathrm{C}}(\mathrm{t})\) impacts into the calculated power depending on the method.  
The results of this analysis show a degradation of the accuracy of the result when noise increases. When the noise level is 15%, the average power obtained is 118.155 mW (which corresponds to an error of \(-\)0.08%) and 125.137 mW (which corresponds to an error of \(-\)6%) using methods 1 and 2, respectively. As observed with uniformly distributed noise, method 1 has shown better robustness when noise increases.  
Figure 15: AC component of \(P_{E}(k)\) and \(P_{\mathrm{C}}(k)\), filtered and non-filtered, with different noise levels (harmonic noise, 400 Hz).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Average Power (mW) for Different Levels of Noise**} \\  & \multicolumn{6}{c}{**(Harmonic Noise, 400 Hz)**} \\ \cline{2-7}  & **0\%** & **5\%** & **10\%** & **15\%** & **20\%** & **25\%** \\ \hline Reference method & 118.05 & 118.052 & 118.053 & 118.054 & 118.055 & 118.056 \\ Instantaneous power (method 1) & 115.983 & 116.015 & 116.281 & 116.727 & 117.351 & 118.155 \\ One-period average (method 2) & 116.691 & 117.751 & 119.426 & 121.266 & 123.170 & 125.137 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the results obtained with different levels of noise (harmonic noise, 400 Hz).
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 4 Methods for Measuring the SUHI  
Various methods have been developed to measure the SUHI and they, in general, can be grouped into three broad classes: LST as a proxy of SUHI, LST difference between the urban and reference areas, and non-parametric models. These three different types of methods are briefly reviewed below.
## 6 Projection Methods (PM)  
Characteristic cases of projection methods are collocation and Galerkin methods. By way of illustration, we consider here the collocation method wherein the unknown function \(u(x)\) in (1) is approximated through the whole of \([a,b]\) by the interpolating polynomial of degree \(\leq n\),  
\[\tilde{u}_{n}(x)=\sum_{j=1}^{n+1}\ell_{j}(x)u(x_{j}), \tag{38}\]  
where \(\ell_{j}(x),\ j=1,\ldots,n+1\) are the Lagrange basis functions defined in (23), which interpolates \(u(x)\) at \(n+1\) distinct ordered points \(a=x_{1}<x_{2}<\ldots<x_{n}<x_{n+1}=b\). By using (38), Equation (1) degenerates to  
\[\tilde{u}_{n}(x)-\lambda\sum_{j=1}^{n+1}\tilde{u}_{n}(x_{j})\int_{a}^{b}K(x,t )\ell_{j}(t)dt=f(x),\quad x\in[a,b]. \tag{39}\]  
Setting up the vectors  
\[G=\lambda\big{(}\begin{array}{ccccc}g_{1}&g_{2}&\cdots&g_{n+1}\end{array} \big{)},\quad g_{j}(x)=\int_{a}^{b}K(x,t)\ell_{j}(t)dt,\quad j=1,2,\ldots,n+1,\]  
and  
\[\Psi(\tilde{u}_{n})=\left(\begin{array}{c}\Psi_{1}(\tilde{u}_{n})\\ \Psi_{2}(\tilde{u}_{n})\\ \vdots\\ \Psi_{n+1}(\tilde{u}_{n})\end{array}\right),\quad\Psi_{j}(\tilde{u}_{n})= \tilde{u}_{n}(x_{j}),\quad j=1,2,\ldots,n+1,\]  
Equation (39) may be written in the symbolic form  
\[T_{c}\tilde{u}_{n}=\tilde{u}_{n}-G\Psi(\tilde{u}_{n})=f, \tag{40}\]  
where the operator \(T_{c}:X\to X\) and \(X=\mathbb{C}[a,b]\).  
Furthermore, approximating \(\{g_{j}(x)\}\) and \(f(x)\) by polynomials of degree \(\leq n\) as above, viz.  
\[g_{jn}(x)=\sum_{l=1}^{n+1}\ell_{l}(x)\int_{a}^{b}K(x_{l},t)\ell_{j}(t)dt,\quad j =1,2,\ldots,n+1,\]  
and  
\[f_{n}(x)=\sum_{l=1}^{n+1}\ell_{l}(x)f(x_{l}),\]  
and after introducing the vector  
\[G_{n}=\lambda\big{(}\begin{array}{ccccc}g_{1n}&g_{2n}&\cdots&g_{(n+1)n} \end{array}\big{)},\]  
Equation (40) is carried to  
\[T_{cc}\tilde{u}_{n}=\tilde{u}_{n}-G_{n}\Psi(\tilde{u}_{n})=f_{n}, \tag{41}\]  
where the operator \(T_{cc}:X\to X\) and \(X=\mathbb{C}[a,b]\).  
Equations (40) and (41) are of the type (3), and so their unique solutions may obtained by Theorem 1.
# Numerical Simulation of Flow Field of Submerged Angular Cavitation Nozzle
## 3 Results and Discussion
### Influence of Expansion Section on Cavitation  
#### 3.4.1 Influence of Expansion Angle on Cavitation  
To investigate the influence of the expansion angle on the cavitation effect of the angular cavitation nozzle, the rest of the structural parameters were kept constant, and different expansion angles \(\beta\) (30\({}^{\circ}\), 40\({}^{\circ}\), 60\({}^{\circ}\), 80\({}^{\circ}\), and 90\({}^{\circ}\)) were set for numerical simulation analysis. The map of the axial velocity distribution of the nozzle jet and the axial vapor volume fraction distribution at \(d/2\) are shown in Figures 20 and 21, respectively.  
According to the distribution of axial velocity at different \(\beta\) (Figure 20), it can be seen that when the axial distance is less than 0 mm, the axial velocity of the jet is approximately the same for different \(\beta\). This is also in line with common sense that the change in the expansion section does not cause the axial velocity of the jet in the contraction section and  
Figure 21: Effect of \(\beta\) on the cavitation.  
Figure 20: Effect of \(\beta\) on the fluid velocity.  
the cylindrical section. While the axial distance is greater than 0 mm, the maximum axial velocity of the jet decreases with the increase in \(\beta\). It is due to the fact that the smaller the \(\beta\), the more concentrated the jet energy, the smaller the energy loss, and the higher the jet velocity.  
The distribution of the axial steam volume fraction at \(d/2\) (Figure 21) shows that when the axial distance is less than 0 mm, the steam volume fraction at \(d/2\) is basically the same for different \(\beta\). While the axial distance is greater than 0 mm, the steam volume fraction at \(d/2\) shows an ascending and then descending trend with the increase in \(\beta\) and reaches the maximum steam volume fraction when \(\beta\) is 60\({}^{\circ}\). Therefore, a too-large or too-small \(\beta\) is not conducive to the generation of cavitation bubbles, and the nozzle with \(\beta\) = 60\({}^{\circ}\) has a better cavitation effect [33].
## 5 Conclusions  
When approaching small scales, the validity of continuum-based LEFM methods is questioned. The present work sheds light on the applicability of the FFM approach by using experimental data available in the literature related to blunt V-notched nano-cantilevers, made of single crystal silicon. The work, therefore, focuses on ideal brittle fracture and mode I loading condition. The main conclusions can be summarised as follows:  
* The comparison with experimental tests shows that the FFM is able to predict the failure load of notched nano-cantilevers at the considered small scale (Figure 5). The agreement between experimental and theoretical apparent fracture toughness was excellent for all the opening angles and notch root radii considered, with a maximum discrepancy of 4.7%.
* The validity of the FFM at the considered scale is due to the fact that the low limit of continuum-based LEFM is not yet reached. Indeed, the crack advancements obtained are larger than the fracture process zone of 0.4-0.5 nm evaluated by other authors (Figure 4 and Table 3).
* The critical length \(\tilde{l}_{c}\approx 1.65\) nm agrees well with the critical distance and the singular stress field length available in the literature and at which continuum-based LEFM breaks down. The crack advancements obtained here are all well far from this value (Figure 4), further justifying the validity of the method.
* Moreover, for very small notch root radii, the apparent generalized fracture toughness deviates from the sharp case (Figure 3). The deviation is larger for small notch opening angles. When dealing with cracks, if attention is not paid in the realization of an ideally sharp crack-tip, an apparent size dependent fracture toughness may result from experimental tests.
* Based on the evidence presented in the current work, the FFM can be an extremely useful method to predict the static failure of micro and nanodevices made of ideal brittle materials. Further investigations should be done when dealing with semi-brittle materials.
* As further development, the extension of the method to the atomic scale is possible by substituting continuum stress and energy formulations with the virial stress and interatomic potential energy, respectively.  
Conceptualization, P.G.; investigation, P.G.; formal analyses, P.G. and A.S.; writing-original draft preparation, P.G. and A.S.; writing-review and editing, P.G. and A.S.; funding acquisition, P.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Academy of Finland grant number 321244.  
**Acknowledgments:** [PERSON] is grateful to [PERSON], [PERSON] (Kyoto University) and [PERSON] (East China University of Science and Technology) for their support during the realization of the experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Test Sites  
Three sites (A, G, and S), where the weathered granite layer was thickly developed, in the mid-western part of South Korea were selected based on the geological map of South Korea provided by the Korea Institute of Geoscience and Mineral Resources (KIGAM) and a review of many site investigations was performed previously near the area (Figure 1).  
Borehole investigations to determine the composition of the subsurface were performed with SPT at all the test sites, and once SPT was refused, rock cores were retrieved with a triple-core barrel; it is regarded as SPT is not acceptable when further penetration was not observed in spite of 10 successive blows [17]. The boundary between grade VI (residual soil) and grade V granite was determined based on the SPT-N value (50 blows/10 cm) [18], grade III and IV granite layers were distinguished based on the core sample conditions comparing with the criterion of weathering grade [1].  
Figure 2 presents the results of borehole investigations, including subsurface layer compositions and SPT-N values. Grade IV and V granite layers (A site: 19 m, G site: 13 m, S site: 18 m), which is the target geo-material, were developed at all the test sites. Therefore, many PMTs were performed, and the samples obtained from the depth, where PMTs were to be conducted, can be analyzed by XRF. The results of PMTs and XRF are used to investigate the relationship between shear strength characteristics and chemical weathering indices of grade IV and V granite.  
Figure 1: Locations and geological maps of test sites.
## 5 Numerical Method and Verification
### Vortex State in Outlet Conduit Based on Q Criterion  
The main purpose of the outlet flow conduit is to make the water flow divert and diffuse better during the process of flowing into the outlet pool and to maximize the kinetic energy recovery without the occurrence of shedding or swirling. During the actual operation of the pump station, the flow pattern in the outlet conduit was disordered, and the existence of unstable vortexes easily caused irregular vibrations of the internal flow field, which caused vibrations of the pump shaft and system and, also, led to eccentric wear of the bearing and packing seals, resulting in serious consequences, affecting the safe and stable operations of the pump unit. Therefore, the extraction of vortex information in the outlet conduit is very important for the analysis of the flow structure in the outlet conduit. In this paper, the \(Q\) criterion method was used to characterize the vortex structure.  
The \(Q\) criterion was proposed by [PERSON] et al. (2019) in 1988. This method defines the area of the second matrix invariant \(Q>0\) of the velocity gradient tensor \(\Delta\)V in the flow field as a vortex, the pressure in the vortex area is less than the ambient pressure and, in the incompressible three-dimensional flow, the definition of \(Q\) is as follows ([PERSON], 2019):  
\[Q=\frac{1}{2}\big{|}\overline{\Omega}\big{|}^{2}-\big{|}\big{|}\overline{S} \big{|}\big{|}^{2} \tag{21}\]  
where \(\overline{S}\) is the symmetrical part of the velocity gradient tensor, i.e., the deformation of a point in the flow field, and \(\overline{\Omega}\) is the asymmetric part of the velocity gradient tensor, i.e., the rotation of a point in the flow field.  
When the threshold Q = 300 s\({}^{-2}\) is taken, the isosurface diagram of the \(Q\) criterion of the siphon outlet conduit, varying with the speed under the three characteristic conditions, is shown in Figure 17. Under the same threshold value, there are eddy structures with different ranges and shapes at the inlet of the siphon outlet conduit, the high-speed zone at the bottom of the hump section and the outlet of the flow conduit under the three characteristic conditions. When water flows in the high-speed downward section of the hump section, under strong inertia, the main flow will deviate to the outside of the flow conduit, which will cause different degrees of outflow inside the flow conduit and make the flow conduit free. The outlet section produces a large-volume vortex structure. Under the small flow condition, the vortex structure is mainly distributed at the inlet of the flow conduit in a lamellar shape, while, under the other two characteristic conditions, the vortex structure mostly presents a striped shape.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks  
Malicious individuals or groups often conduct smart grid attacks with criminal or terrorist intent to disrupt, interrupt, manipulate, and compromise the smart grid's operational systems and critical infrastructure [130]. These attacks differ depending on their type, impact, and the components they target in the smart grids, including control systems, smart meters, connected devices, communication networks, channels, and many others [131]. The goal of each attack differs from the others as their motivation is to either disrupt the service, gain unauthorized access, manipulate or inject data, conduct espionage, or sabotage and is mainly driven by personal gain. They can often be carried out using various means, such as cyberattacks, physical intrusions, social/reverse engineering, and phishing techniques. Still, they can also be mitigated by implementing resilient security protocols, such as intrusion detection and prevention systems, access restrictions, honeypots, anti-malware solutions, encryption, multi-factor authentication mechanisms, and regular security assessments and updates [32].  
This section categorizes the specific attack methods used to achieve these threats by directly exploiting vulnerabilities in smart grid systems. These attacks can be classified based on their nature, coordination, and complexity as seen in Table 13. Each attack type is directly linked back to one or more of the threats outlined in Section 4, providing a structured perspective on how smart grid systems can be compromised in practice.
## 3 Test Results
### Discussion  
As a result of measuring the shielding efficiency for each frequency band as electromagnetic waves passed through the slit of the amorphous soft magnetic sheet, we determined that the waves could pass more easily through the slit in the horizontal direction than in the vertical direction. Therefore, the electromagnetic wave passed through the space between the fibers in the fabric of the amorphous soft magnetic fiber in which the space between the fabrics had not been properly shielded.  
In addition, the shielding effect increased as the area of the multi-layered conductive tape attached to the slit of the amorphous soft magnetic sheet increased and was overlapped. Furthermore, it was important that the shielding efficiency increased when the amorphous soft magnetic sheets were stacked alternately at a 90-degree angle rather than when stacked in parallel.  
Finally, when manufacturing an amorphous soft magnetic composite material, the gap (slit) must be filled with a conductive material, such as conductive tape, so that there is no space for electromagnetic waves to pass through.  
It was difficult to find similar existing research data, so direct comparisons of the results of this study with existing studies were limited. Moreover, this research pioneered a new direction in the field of EMP protection technology by showing the potential of light, flexible electromagnetic shielding materials.  
In future research, we will examine the effect of the Fe content of soft magnetic material on its electromagnetic wave-shielding properties in relation to applications in low-frequency regions where magnetic properties are critical.
## 4 Numerical Simulation Verification and Analysis  
The coupling of the beam material, beam size parameters, and soil parameters, as well as the boundary conditions on the beam, make it difficult to determine the measurement range and accuracy of the OFEB. Thus, a numerical simulation was performed to investigate the coupling and coordinated deformation capacity of the beam and the soil. The optimal material and size parameters were determined to increase the coupling deformation capacity of the beam and the soil, and the applicability of the OFEB for measuring large-scale settlement and deformation of a subgrade was evaluated. A finite element analysis of the subgrade similarity model was carried out using ABAQUS. An analysis was performed to determine the influence of key design parameters for the shape and material of the pipe and the interfacial contact (as represented by the pipe diameter \(D\), the elastic moduli \(E\) of the pipe and the soil, and the friction coefficient \(\mu\) between the pipe and the soil) on the OFEB measurement range.
# Indoor Acoustic Requirements for Autism-Friendly Spaces
## 2 Materials and Methods
### Literature Results on Acoustic Devices Requirements and Their Uses
For the acoustic qualification of a room, it is also essential to study the distribution of sound energy during decay (i.e., whether this energy is concentrated near the source or, conversely, a significant portion of it arrives with a significant delay compared to the direct sound) [35]. The ratio between early and late sound reflections is represented by the parameter clarity index (C\({}_{50}\)). This is an objective qualification of the clarity of speech. Indeed, late reflections are unfavorable for speech comprehension; anyway, if this delay does not exceed a certain time limit (50 ms), the reflections will contribute positively to the speech clarity. In addition, the percentage ratio between early sound reflections (between 0 and 50 ms) and overall sound energy of the impulse response is defined in terms of definition (D\({}_{50}\)). Few studies have focused on the determination of the optimal sound field conditions related to the use of this sound device within the built environment. [PERSON] et al. [27] reported that for reverberation time, the range comprised from 0.5 s to 0.7 s represents the optimal conditions for the use of sound sensors in small environments; [PERSON] et al. [36] demonstrated that strong disturbances are caused by reflections in most typical indoor living area for sounds at frequencies below 250 Hz and above 2500 Hz. The identified optimal reverberation time represents the combination of the requirements of good intelligibility and sufficient intensity level, depending on the listening conditions. A value of 0.5 s at medium-high frequencies is characteristic of a normally furnished residential environment [37]. Among all the other parameters, C\({}_{50}\) and D\({}_{50}\) represent further paramount room acoustic indicators used to determine the quality of environments suitable for sensor installation [38; 39]. [PERSON] et al. [40] showed that the full frequency-band of C\({}_{50}\) is the most relevant measurement of sound energy to predict phenomena recognition in terms of correlation and mutual information. [PERSON] et al. [41] explained the high correlation between the word accuracy and the definition D\({}_{50}\), pointing out that if a value of 0.7 is verified in a room, the sound intelligibility will be very good. These indexes represent the ratio between the energy associated with direct sound and the one associated with the first and subsequent reflections [42]. Therefore, C\({}_{50}\) and D\({}_{50}\) are certainly useful for the qualification of the indoor sound field. They permit correctly location of the acoustic recognition sensors inside a room because they are influenced by room position. However, these parameters are not commonly used as the RT, which, on the other hand, does not vary significantly because of room location. In conclusion, some ranges have been found, but no studies on indoor sound field distribution are available as well as information on device position selection.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Benchmark Methods
#### 4.5.3 Fc-Nn  
The deep learning methods used in UWA channel estimation are based on the backpropagation neural network [29]. It capture complex nonlinear relationships by mapping the CIR with received signal and the transmitted pilots. The FC-NN used in this paper comprises five dense layers. This configuration allows the NN to effectively learn and represent the underlying mapping between input features and the CIR in the UWA channel.