esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Active Control  
While FWTs are passive devices that only require proper modeling, the actively controlled GLA requires the synthesis of a controller. In this work, a Static Output Feedback (SOF) controller [40] was designed. This approach was chosen for its simplicity and reliability, as demonstrated in previous numerical and experimental studies [41; 42]. The SOF architecture is straightforward: it generates a control input, \(\mathbf{u}\), which is a linear combination of certain measurements, \(\mathbf{y}\), as described in Equation (4).  
\[\mathbf{u}=-\mathbf{Gy} \tag{4}\]  
Aside from time integration, the controller is purely static, meaning there is no filtering of the measured signals or incorporation of controller dynamics. This simplifies the design to determining the gain matrix used to compute the control input, specifically the \(\mathbf{G}\) term in Equation (4). The measurements used for control are the pitch rate, the accelerations at the wing tips, and the center of gravity, which are integrated to obtain the structural  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mass Configuration** & **Payload** & **Fuel** \\ \hline CONF1 & 100\% & 80\% \\ CONF2 & 80\% & 100\% \\ CONF3 & 100\% & 0\% \\ CONF4 & 50\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass configurations considered.  
Figure 3: Gust time histories for z = 8000 m and \(V_{EAS}=150\) m/s.  
velocity. The control surfaces commanded by the SOF controller are the elevator and the ailerons, which are actuated symmetrically. As the elevator primarily influences the aircraft's rigid body dynamics, its deflection is set to be proportional only to the pitch rate to suppress low-frequency gust content. The resulting gain matrix is structured as shown in Equation (5), with six independent \(g_{i}\) gain values to be defined.  
\[\begin{Bmatrix}\delta_{\text{aileron}\ RH}\\ \delta_{\text{aileron}\ LH}\\ \delta_{\text{elevator}}\end{Bmatrix}=-\begin{bmatrix}g_{1}&g_{2}&g_{1}&g_{2}&g_ {3}&g_{4}&g_{5}\\ g_{1}&g_{2}&g_{1}&S_{2}&g_{3}&g_{4}&g_{5}\\ 0&&&\cdots&&0&g_{6}\\ \end{bmatrix}\begin{Bmatrix}\ddot{x}_{tip}\ RH\\ \dot{x}_{tip}\ LH\\ \dot{x}_{tip}\ LH\\ \dot{x}_{CG}\\ \dot{q}_{CG}\end{Bmatrix} \tag{5}\]  
The \(g_{i}\) values were determined using the procedure outlined in [41]. An optimization problem was formulated, with the objective of minimizing the weighted sum of the ratios between the open-loop (OL) and closed-loop (CL) structural responses. Specifically, these ratios were calculated for the bending moments (BMs) and torsional moments (TMs) at the wing root (WR) and engine section (ES), as shown in Equation (6).  
\[obj=\frac{WRBM_{CL}}{WRBM_{OL}}+\frac{WRTM_{CL}}{WRTM_{OL}}+\frac{ESBM_{CL}}{ESBM _{OL}}+\frac{ESTM_{CL}}{ESTM_{OL}} \tag{6}\]  
The optimization was performed using Matlab's fmincon, ensuring the stability of the closed-loop aero-servo-elastic system across the flight envelope by imposing \(\Re(\lambda)<0\). Additionally, a constraint was applied to limit the control surface rotation rate to \(\delta\leq 80^{\circ}_{\text{s}}\).
## 2 Magnet Anomaly Motion Target Feature Detection
### Magnetic Dipole Motion Detection Model
Figure 2 shows the relationship between the magnetic field generated by the magnetic target at the detection platform and the geomagnetic field. The magnetic field generated by the magnetic target at the detection platform is \(\mathbf{B}\), the geomagnetic field at the detection platform is \(\mathbf{B}_{\text{E}}\), and the geomagnetic field can be considered to be uniformly distribution in a narrow range. The total magnetic field measured using a magnetometer on the detection platform is \(\mathbf{B}_{\text{S}}\), which is synthesized with the constant geomagnetic field part \(\mathbf{B}_{\text{E}}\), and the magnetic anomaly field \(\mathbf{B}\), as shown in Equation (3). \[\mathbf{B}_{\text{S}}=\mathbf{B}+\mathbf{B}_{\text{E}} \tag{3}\]  
where \(\varphi\) is the vector angle between the magnetic anomaly field \(\mathbf{B}\) and the geomagnetic field \(\mathbf{B}_{\text{E}}\). In general, the value of the magnetic field \(|\mathbf{B}|\) of the magnetic target at the detection platform is about a few hundred nT, while the value of the geomagnetic field \(|\mathbf{B}_{\text{E}}|\) is about 50,000 nT. When \(|\mathbf{B}|\ll\mid\mathbf{B}_{\text{E}}\mid\), the relationship between the total magnetic field, the anomalous magnetic field, and the geomagnetic field can be approximated as  
\[|\mathbf{B}_{\text{S}}|\approx|\mathbf{B}_{\text{E}}|\times\left[1+\frac{1}{2} \Big{(}|\mathbf{B}|^{2}+2|\mathbf{B}_{\text{E}}|\times|\mathbf{B}|\cos\varphi \Big{)}/|\mathbf{B}_{\text{E}}|^{2}\right] \tag{4}\]  
According to (3) and (4), the magnetic anomaly signal \(S\) at the detection platform obtained by the measurement is:  
\[S=|\mathbf{B}|-|\mathbf{B}_{\text{E}}|\approx\frac{\mathbf{B}\cdot\mathbf{B}_ {\text{E}}}{|\mathbf{B}_{\text{E}}|}=|\mathbf{B}|\cos\varphi=\mathbf{B}\cdot \mathbf{i}_{e} \tag{5}\]  
where \(\mathbf{i}_{e}\) is the unit vector in the direction of the geomagnetic field. It can be seen from Equation (5) that the measured magnetic anomaly signal of the magnetic target at the detection platform is the projection of the magnetic anomaly field generated by the magnetic target at the detection platform in the direction of the geomagnetic field.
## 3 Results and Discussion
### The Influence of Increasing the Number of DGs on the System Voltage Profile and Power Losses  
Table 10 presents a summary of the results obtained for the minimum voltage profile and the associated power losses, considering the placement of the DGs at various positions identified by the CF ranking method. This analysis was performed to investigate the influence of multiple DG integration in a power system on the voltage stability margin as well as the power losses. In this case, up to four DGs, whose sizes were determined using the PSO algorithm, were placed at buses 18, 33, 32 and 31 as presented in Table 10. The minimum voltage magnitudes and the system's associated power losses are presented. Based on the results presented, it is obvious that there was an enhancement of the voltage profile and a reduction in power losses as the number of DGs was increased up to the installation of three DGs. However, as the number of DGs increased to four, which were placed at buses 18, 33, 32 and 31, the voltage decreased significantly and there was an  
Figure 5: Comparison of the six scenarios for identifying optimal locations for DG placement.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & \multirow{2}{*}{**Case Type**} & \multirow{2}{*}{**No. of DGs**} & \multirow{2}{*}{**DG Size (MW)**} & \multirow{2}{*}{**DG Location**} & \multicolumn{2}{c}{**Power Loss**} & \multicolumn{1}{c}{**Minimum**} \\  & & & & & **(MW)** & **Voltage (pu)** \\ \hline
1 & Base Case & 0 & 0 & NA & 1.0268 & 0.8297 (bus 18) \\
2 & Optimal Case & 1 & 2 & 18 & 0.3066 & 0.9843 (bus 30) \\
3 & Arbitrary Case & 1 & 2 & 16 & 1.1265 & 0.9535 (bus 18) \\
4 & Optimal Case & 2 & 2, 1.064 & 18, 33 & 0.2348 & 0.9890 (bus 30) \\
5 & Arbitrary Case & 2 & 2, 1.064 & 16, 32 & 0.9815 & 0.9500 (bus 30) \\
6 & Optimal Case & 3 & 2, 1.04, 1.64 & 18, 33, 32 & 0.2045 & 0.9960 (bus 28) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of the voltage profiles and power losses with and without DG installation.  
appreciable increase in power losses. These observations can be explained as follows: When the voltage was within an acceptable range, the distribution network continued to be passive and power flow continued in one direction. But if the number of DGs was continually increased to the point that the DG point of connection with lower voltage had an increased voltage surpassing the allowable range, the network ceased to be passive and power started flowing in multiple directions. As such, the DG installed to augment power losses became an active supply center, which caused fluctuations in the system. This, therefore, diminished the system integrity, which could eventually lead to a total collapse of the distribution network if not quickly checked. This simply implies that a significant increase in the DG penetration in terms of number and size can cause voltage instability, hence the need for monitoring DG sizes and numbers for voltage enhancement and power loss minimization in radial power systems.
## 1 Introduction
Meanwhile, if link conditions, such as distance, or other parameters change, machine learning models may need to be retrained [29]. Therefore, a prior-training-free and low-complexity MFI scheme is highly desirable for practical deployment. In this paper, a prior-training-free MFI scheme, based on amplitude histogram distributions, was proposed for autonomous digital coherent receivers. Since there are different amplitude levels for QPSK, 8 QAM, 16 QAM, 32 QAM and 64 QAM, the amplitude histograms of CMA-equalized signals were applied to extract key features required for identification of different modulation formats. The proposed scheme mainly focused on identifying effective local features of amplitude histograms, rather than global features [6; 20; 34; 36]. In the proposed MFI scheme, after having normalized the signal power, the ratios of specific parts of the amplitude histograms were calculated, based on which five modulation formats could be identified. The proposed scheme did not require any prior-training or information, such as OSNR, and was also insensitive to carrier phase noise. The performance of the proposed MFI scheme was first explored by numerical simulation, using 28 GBaud PDM-QPSK/-8 QAM/-16 QAM/-32 QAM/-64 QAM signals. The simulation results showed that the proposed scheme was able to achieve 100% of the correct MFI rate for all five modulation formats when the OSNR values were higher than their corresponding theoretical 20% forward error correction (FEC) limit (BER = 2.4 \(\times\) 10\({}^{-2}\)). For 28 GBaudPDM-QPSK/-8 QAM/-16 QAM, and 21.5 GBaud PDM-32 QAM systems subject to back-to-back and long-haul fiber transmission link conditions, proof-of-concept experiments were undertaken to further verify the effectiveness of the proposed MFI scheme. The experimental results showed that the proposed scheme was robust against fiber nonlinearities and suitable for use in long-haul transmission links. Finally, the computational complexity of the proposed scheme was also discussed. It was considerably lower than relevant MFI schemes. The proposed scheme demonstrated a good tradeoff between identification performance and computational complexity, and could therefore be regarded as a good candidate for use in EONs with improved flexibility.
## 3 Numerical Simulation
### Noise Effect  
In the field application, the measurement is always perturbed by noise so it is important to examine the performance using noisy measurement. Signal-to-noise ratio (SNR) is commonly used to represent the level of background noise was adopted in this study. By adding different levels of noise into the measurement, the identified system's poles are displayed in Figure 5. In the noise-free case shown in Figure 5a, both RSI via the bona fide recursive algorithm and SI via the moving window method can extract the identical 8 system's poles during the seismic excitation. When noise is blended into the measure, spurious poles appear and dramatically increased (in number) as the noise level. It is observed that the spurious poles only come out under the small row number of the Hankel matrices, implying a larger extension coefficient, \(C_{E}\), can somehow deal with the noise problem. However, even an enlarged row number (\(i=20\)) can get rid of the spurious poles if SNR keeps reducing, as shown in Figure 5d.  
Figure 4: The identified modal frequencies across various \(C_{E}\) (\(C_{E}=0.78\), \(1.3\), \(1.57\), \(2.35\), \(2.75\), \(3.53\), or \(3.92\) when \(i=2\), \(3\), \(4\), \(6\), \(7\), \(9\), or \(10\), respectively). (**a**) smallest \(i\); (**b**) first \(i\) group; (**c**) second \(i\) group; (**c**) third \(i\) group.  
The modal assurance criterion (MAC) is usually used to discriminate between the spurious poles and the true system's poles while implementing SI [38; 39; 40; 41]. It is defined as the coherence between two vectors, \(\overline{\mathbf{\varphi}}_{k}\) and \(\widetilde{\mathbf{\varphi}}_{k}\), as  
\[\text{MAC}=\frac{\widetilde{\mathbf{\varphi}}_{k}^{H}\widetilde{\mathbf{\varphi}}_{k}}{ \sqrt{\widetilde{\mathbf{\varphi}}_{k}^{H}\widetilde{\mathbf{\varphi}}_{k}\widetilde{ \mathbf{\varphi}}_{k}\widetilde{\mathbf{\varphi}}_{k}}} \tag{30}\]  
where the superscript \(H\) is Hermitian transpose. Generally, those vectors are the mode shapes taken out from a stabilization diagram generated over various values of the user-defined parameters, but RSI uses a consistent value and has no stabilization diagram. In the proposed method, one of the vectors can be the column space of the extended observability matrix and the other one can be the reconstructed column space from the eigenvalues and eigenvectors. Sieving these vectors with MAC and an adequate threshold, \(C_{MAC}\), can remove the spurious modes during the recursive steps. Figure 6 shows the identified system poles with and without MAC criterion under the high noise level. Clearly, all the spurious modes are removed after \(C_{MAC}\) is set to 0.98 and the effectiveness of the proposed method is demonstrated.  
Figure 5: The identified system poles under different levels of background noise. (**a**) noise-free (\(i=10\)); (**b**) low noise (SNR = 30); (**c**) medium noise (SNR = 25); (**d**) high noise (SNR = 20).
## 3 System Model
### Receiver Structure
Further, performance of PPI has been compared with RI under multipath channel conditions with above-mentioned multiple access schemes for variation in data length as shown in Figure 7. System with 16 users has been simulated for a 5-iteration count with the numbers of symbols considered as 128 and 64, respectively. Small data length shows more correlated interleaving patterns among users as compared to large data length. Therefore, results for small data length are inferior slightly as compared to large data length for both the interleaver types. It is evident from the results that PPI performs with the same efficiency as RI for smaller data lengths, even in a multipath channel environment. Figure 5: BER performance of PPI with OFDM-IDMA, LFDMA and DFDMA in Veh. A channel for user count (U) 16 and 32. In general, interleaving patterns for different users are less correlated if the data length is large; however, a small data length results in large correlation among patterns of different users resulting in inferior performance. Most of the orthogonal interleavers suggested in the literature [27] work well with large data lengths; however, due to their generation mechanism, if data lengths are small, the correlation among multiple users' data increases, resulting in overall inferior performance of the NOMA receiver. PPI and RI work well  
Figure 6: BER performance of PPI and RI for OFDM-IDMA, LFDMA and DFDMA in Veh. A channel for iteration count (It) 2, 5 and 6. Figure 7: BER performance of PPI and RI for OFDM-IDMA, LFDMA and DFDMA in Veh. A channel for data length (DL) 128, 64.  
with small data lengths. In comparison to RI, the generation of PPI is less complex while implementing MUD at the receiver. Therefore, PPI is a better choice to distinguish multiple users in NOMA scenario even with smaller data lengths.
# Analysis of Damage and Permeability Evolution of Sandstone under Compression Deformation
## 2 Coupling Analysis of Rock Damage and Permeability  
### Coupling Mechanism  
Damage and permeability are two characteristics of rock. The former is used to characterize the deterioration of rock mechanical properties under load or the environment. In other words, damage affects the skeleton structure and integrity of rock. The latter is used to measure the ability of the rock to transmit fluids, depending on the skeleton structure and integrity of the rock. Therefore, damage induces changes in the rock's permeability. The interaction between them usually appears in the case of multi-physics coupling [24; 26]. For example, stress-induced damage (stress field) in rocks under compression or shear results in microcracks in the rock. The germination and propagation of microcracks change the size and connectivity of rock pores, thereby affecting the hydraulic characteristics of the rock. Meanwhile, the pore pressure (seepage field) drives the fluid to seep into the damage-induced pores and cracks of the rock, ultimately leading to a reduction in strength, an increase in cracks, and the intensification of damage. The full coupling mechanism of rock damage and permeability due to the interaction of the stress field with the seepage field is shown in Figure 1.
# Speciation of the Removed Pollutants in Bioremediation of Hydrocarbon-Contaminated Soil
###### Abstract  
The biological removal of a mixture of soil contaminants, namely, hydrocarbons, is not equally efficient for each compound. Some pollutants can be metabolized by the microbial consortium but also generated again as by-products from the removal of others. At the end of the runs, notwithstanding the high integral removal, single compounds can still be present with a relevant concentration. This paper presents the results achieved in a study of the aerobic degradation of diesel oil in three mesocosms carried out for several months with the same operative conditions. They differed in biological management: Natural Attenuation (NA), Biostimulated without inoculation (BS), and Biostimulated with Inoculation (BS + IN). At the end of the runs, the pollution removal was calculated by measuring the residual diesel oil, both as an average in the total amount of soil and only at the bottom of each column. The overall removal was around 2%, 66%, and 72% for NA, BS and BS + IN, reduced to 0%, 48%, and 47%, respectively, if measured only at the bottom. For the biostimulated mesocosms, the speciation of the hydrocarbons was carried out to assess their concentration. The findings evidence the need to delve deeper into this issue and assess the speciation of contaminants.  
soil bioremediation; chemical speciation; gas chromatography analysis; diesel oil; pollution removal +
Footnote †: journal: Medical  
In the current paper, bioremediation of soil polluted with diesel oil was studied in three systems at the mesocosm scale.  
The findings of the geophysical monitoring and overall removal efficiency were published in a previous paper [8]. Concerning the geophysical properties, the results showed that the inoculated system had the fastest change rate of the electromagnetic properties, probably due to the enhanced microbial activity. This is in line with the hypothesis of depletion of the salts added for the process (decrease in the electrical conductivity) and growth of the microbial community (decrease in the electrical permittivity). These two hypotheses have not been experimentally demonstrated, yet.  
In the present manuscript, the results achieved from the speciation of the residual hydrocarbons present at the end of each run in the biostimulated systems (BS and BS + IN) are presented.  
In both mesocosms (BS and BS + IN), the removal efficiency by clusters achieved for the total amount of soil and just on the bottom was different, with a higher value for the total amount of soil for all the clusters. The results evidence the need to delve deeper into this issue when the process is applied onsite and the legislative limits to the concentration of specific pollutants must be respected.
## 4 Results
### Quantitative User Evaluation  
To evaluate the perceptual realism and interaction quality of the cloth simulation systems, a quantitative user study was conducted comparing Unity's built-in Cloth system and the proposed GPU-PBD Cloth system. Each participant interacted with both systems using hand-based interaction scenarios with capsule colliders on the Meta Quest 3 device.  
A total of 10 participants (6 male, 4 female, aged 20-33), all of whom had prior experience with XR devices, completed the evaluation. After performing the tasks, participants rated both systems based on five criteria using a five-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). The evaluation criteria were:  
* **Visual Realism:** \"The appearance of the cloth, including folds and wrinkles, looked similar to real fabric.\"
* **Interaction Naturalness:** \"The cloth responded physically naturally when touched, pushed, or stretched.\"
* **Responsiveness:** \"The cloth responded promptly to my hand movements without noticeable delay.\"  
\begin{table}
\begin{tabular}{l l} \hline
**Item** & **Details** \\ \hline Cloth Resolution & 32 × 32 \\ Hand Tracking Method & Meta Quest 3 OVRMesh (Real-Time Update) \\ Collision Surface & Dynamically updated hand mesh \\ Interaction Scenarios & Right-hand push (moving from right to left), two-hand stretching (pulling cloth in opposite directions) \\ \hline \end{tabular}
\end{table}
Table 7: Experimental setup for hand mesh collision testing.  
Figure 13: Limitation observed during high-speed two-hand interaction using hand mesh input. From left to right: (1) cloth begins to stretch under dual-hand control; (2) excessive stretching results in unnatural deformation; (3) tearing artifacts occur due to instability in constraint resolution and insufficient temporal granularity.  
* **Spatial Alignment:** \"The cloth correctly interacted with real-world surfaces such as desks and floors without misalignment.\"
* **Immersive Experience:** \"The simulation felt immersive and enhanced the overall realism of the XR environment.\"  
Figure 14 presents the average scores across these criteria. The results show that the GPU-PBD system significantly outperformed Unity Cloth in _Visual Realism_ (4.6 vs. 3.1) and _Interaction Naturalness_ (4.8 vs. 3.0), demonstrating its superior physical fidelity and natural behavior in user interactions.  
Although the two systems showed comparable performance in _Responsiveness_ (4.2 vs. 3.4), the GPU-PBD system received a substantially higher score in _Spatial Alignment_ (4.6 vs. 1.1), clearly indicating Unity Cloth's limitations in accurately responding to complex real-world geometries such as furniture edges and mesh surfaces. In addition, the GPU-PBD system also surpassed Unity Cloth in _Immersive Experience_ (4.8 vs. 2.6), reflecting a stronger overall sense of realism and engagement perceived by users.  
These results reinforce the technical findings presented in earlier sections and further highlight the perceptual and experiential advantages of the proposed GPU-PBD cloth simulation system in XR environments.