esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Based on the principles of reinjection converter technology and submodule operation, a novel topology for a reinjection multilevel voltage source converter (RMVSC) is proposed. In this topology, submodules are dynamically connected in series as the reinjection circuit. This new topology retains all the functions of the RMVSC while offering flexible control of the reinjection circuit and ease of modularization, making it highly suitable for high-voltage, high-power applications. A circulating drive pulse sequence was developed for the 7-level serial submodule RMVSC, ensuring dynamic stability of the submodule voltage and maintaining low harmonic distortion when interfaced with the grid. Building on this, a double-loop control strategy--comprising an outer power loop and an inner current loop--was proposed for the static synchronous compensator (STATCOM) of RMVSC. A simulation model was constructed in PSCAD/EMTDC, and the simulation results confirm the excellent performance of the proposed topology and the effectiveness of both the modulation strategy and the double-loop control strategy. The RMVSC-STATCOM demonstrates continuous and bidirectional reactive power regulation, with high control accuracy and superior compensation current quality.  
reinjection converter technology; new topology; multilevel converter; submodule voltage balancing; STATCOM 2024 14 9998 10.3390/app14219998
## 7 Robustness
### Security Analysis
#### 7.5.5 Quantitative Security Assessment  
Analysis confirms that the PRNG approach to block selection effectively resists brute-force attacks. A standard 64-bit watermark embedded in a 512 \(\times\) 512 image yields approximately \(10^{19}\) possible block combinations that require infeasible computational resources for exhaustive search attacks. When tested against adversarial attempts, the system maintained extraction reliability (NC \(>\) 0.85) even when the attackers had partial information on the watermark scheme.  
A comprehensive collusion attack simulation involving 10 uniquely watermarked copies of the same image demonstrated that at least 8 distinct copies were required to sufficiently degrade the watermark below detection thresholds (BER \(>\) 0.35). This significantly exceeds typical distribution scenarios in forensic contexts. Furthermore, cryptanalytic attacks targeting the PRNG seed revealed that prediction remains computationally intensive, even with access to 60% of previously watermarked blocks. Estimates show that a successful attack would require approximately 252 operations, well beyond feasible attack thresholds.  
The analysis of insider threat scenarios quantified the resilience of the system when 40% of the watermarking parameters were compromised. In these cases, watermark extraction still succeeded (BER < 0.25) due to dual domain redundancy, and recipient identification accuracy remained above 92%. These security metrics highlight strong resistance to unauthorised access, tampering, and partial system exposure. The system architecture maintains computational efficiency and a security margin that exceeds what adversaries are expected to achieve in evidence management.  
Compared to adversarially robust watermarking methods [17; 19], our approach is simpler and more transparent. Deep learning techniques resist removal but require heavy computation and lack procedural clarity. Task-specific methods [18] work well for narrow use cases, but do not generalise to varied forensic contexts.  
The dual domain method separates risk. When compression or filtering affects the DCT watermark, the DWT component remains. Geometric changes that disrupt wavelets leave the DCT watermark stable. This avoids complete failure without relying on complex systems such as self-healing watermarking [31].
# Advanced DSP for Coherent Optical Fiber Communication
## 1 Introduction
From Section 3 to Section 5, nonlinearity compensation, SDM applications and FDE approaches are presented in detail, respectively. Section 6 will explore the application of recently proposed and promising ML technologies in optical communications. A brief conclusion with our perspectives is provided in the last section.
## 3 Methodology Concept  
This section introduces a methodology for integrating monitoring data directly into a Monte Carlo probabilistic safety assessment procedure. The key idea is to embed the monitoring data inside a Monte Carlo framework (Figure 2) by weighting each individual simulation according to how closely its predicted behavior matches the monitoring data. Simulations that are closer to the real structural behavior receive higher statistical weight, while those that deviate are suppressed. In effect, the distribution of the baseline Monte Carlo analysis results is \"pulled\" toward the measured behavior without any need to recalibrate the input parameters themselves. The idea is rooted in Bayesian probability theory [34]: in essence, information from monitoring records updates prior beliefs about system behavior. Although the procedure does not strictly follow a full Bayesian update--no explicit posterior parameter distributions are computed--the weighting step acts as an implicit Bayesian correction, markedly reducing uncertainty and yielding more trustworthy reliability estimates for embankment dams.  
The variability of monitoring data must be modeled with care. Only validated, high-quality monitoring readings should enter the update. Any irregular data that misrepresent the structure's behavior must be removed. Introducing such values into the analysis not only fails to improve the solution's accuracy, but can produce results that either lie outside the baseline analysis range and prevent convergence entirely or yield overly broad distributions that render the computation uninformative.  
It must be pointed out that the dam monitoring data is usually obtained under the operating conditions (far from the failure state). Therefore, analyses scenarios for which monitoring data are used must mirror the actual operating conditions in terms of both the loading history and hydraulic boundary conditions. If extreme design scenarios (such as the safety-evaluation earthquake) have not occurred during the dam's life, monitoring records cannot validate them directly.
## 4 Conclusion  
Annual GPP measured by the ALOS/AVNIR-2 satellite is lower than that from Aster because ALOS/AVNIR-2 has a higher spatial resolution and smaller spectral range than Aster. Total GPP per year in Denpasar was 52,421.46 tC yr\({}^{-1}\) as estimated by ALOS/AVNIR-2 and 59,355.49 tC yr\({}^{-1}\) as estimated by Aster.  
The medium spatial resolution of the remote sensing data is crucial for discriminating different land cover types in urban areas. Because of the heterogeneous land cover, the maximum value of GPP from ALOS/AVNIR-2 was smaller than that from Aster. Meanwhile, the annual mean GPP from ALOS/AVNIR-2 was higher than that from Aster because the higher spatial resolution of ALOS/AVNIR-2 results in improved detection of vegetation cover and conditions.  
Estimates of GPP are affected by land use patterns. In particular, forests (magnroves) and rice fields are characterized by higher mean GPP. ALOS/AVNIR-2 estimates GPP as 1,123.58 gC m\({}^{-2}\) yr\({}^{-1}\) in forests (magnroves) and 1,030.08 gC m\({}^{-2}\) yr\({}^{-1}\) in rice fields; the totals from Aster are 1,177.40 gC m\({}^{-2}\) yr\({}^{-1}\) and 1,020.65 gC m\({}^{-2}\) yr\({}^{-1}\), respectively. The lowest mean GPP was observed in land with settlements and was 540.49 gC m\({}^{-2}\) yr\({}^{-1}\) in the ALOS/AVNIR-2 data and 492.44 gC m\({}^{-2}\) yr\({}^{-1}\) in the Aster data.  
The maximum GPP measured by ALOS/AVNIR-2 and Aster was smaller than the maximum from the MODIS GPP product (MOD17) in the Denpasar area, over a tropical peat swamp forest in central Kalimantan-Indonesia, and over a tropical forest in central Amazonia, Brazil.  
Differences in spatial and spectral resolution affect the accuracy of object detection. For heterogeneous areas such as those containing settlements, satellites with high spatial resolution are necessary to detect detailed features. For homogeneous areas such as forests (magnroves) and rice fields, high spectral resolution is recommended.  
Further research is needed to more accurately validate ALOS/AVNIR-2 and Aster GPP estimates. In particular, these satellites should be tested in areas with eddy flux towers so that satellite results can be directly compared to accurate _in situ_ data.
# Advanced Unified Earthquake Catalog for North East India
## 4 Regression Analysis for Magnitude Conversion
### Duration Magnitudes into \(M_{\text{wg}}\)  
Based on 376 global data earthquakes from the ISC database, the relationship between duration magnitude (\(\text{M}_{\text{D}}\)) and \(\text{M}_{\text{wg}}\) is derived using GOR1 methodology with \(\eta=1\) and is given below. The plot of the regression is shown in Figure 13.  
The relationship between \(\text{M}_{\text{D}}\) and \(\text{M}_{\text{wg}}\) using \(\eta=1\) is stated as  
\[\text{M}_{\text{wg,GCMT}}=0.821(\pm 0.002)\text{M}_{\text{D}}-0.832(\pm 0.102). \tag{6}\]  
\[\text{R}_{\text{xy}}=0.81,\quad\text{RMSE}=0.140,\quad\text{n}=376.\]  
For the conversion of \(\text{M}_{\text{D}}\) to \(\text{M}_{\text{wg}}\), it is also found that the GOR1 method has the highest accuracy compared to SLR and GOR2 (Tables 2 and 3 and Figure 14).  
Figure 14: Comparison of the uncertainty in regression parameters for (\(\text{M}_{\text{D}}-\text{M}_{\text{wg}}\)) data pairs using GOR1, GOR2, and SLR approaches considering function of error variance ratio: (**a**) Correlation Coefficient determination (\(\text{R}_{\text{xy}}\)), (**b**) Root mean square error (RMSE), (**c**) uncertainty values of slope and (**d**) uncertainty of intercept.  
Figure 13: The (\(\text{M}_{\text{D}}-\text{M}_{\text{wg}}\)) data, as well as the GOR1, GOR2, and SLR regression lines.
# Shear Mechanical Properties of Bolt-Grout Interface under Different Bolt Surface Profiles
## 3 Test Results
### Interface Shear Failure Characteristics  
The interface shear failure modes can be mainly categorized into the following three types: shear-slip failure, shear-break failure and combined failure. From the test results, the Bolt-Grout interface exhibits different failure modes with different rib spacing, normal stress and rib angle [34]. The study of interfacial shear failure characteristics is crucial for analyzing the shear failure mechanism of the Bolt-Grout interface, and then analyzing the shear mechanical properties of the rock bolting system [40].
# BRNN-LSTM for Initial Access in Millimeter Wave Communications
## 4 Simulation Results and Performance Evaluation
### Energy Consumption  
The energy consumption is measured at the MS in Figure 7, and it is defined as the power consumption (in microjoules) during the beam access time interval. It is given by \(E_{\mathrm{C}}=Q_{\mathrm{MS}}^{\mathrm{ABF}}T_{\mathrm{acc}}^{MS}\), where \(Q_{\mathrm{MS}}^{\mathrm{ABF}}\) is the power consumption in the ABF at the MS,  
\[Q_{\mathrm{MS}}^{\mathrm{ABF}}=N_{\mathrm{MS}}(q_{n}+q_{\mathrm{PS}}+q_{ \mathrm{PS}})+q_{\mathrm{LNA}}+q_{\mathrm{RF}}+q_{\mathrm{ADC}}+q_{\mathrm{BB}}, \tag{14}\]  
\[q_{\mathrm{RF}}=q_{\mathrm{M}}+q_{\mathrm{LO}}+q_{\mathrm{LPF}}+q_{\mathrm{AMP} },\ q_{\mathrm{ADC}}=E_{\mathrm{ADC}}^{\mathrm{step}}S_{\mathrm{ADC}}2^{B}, \tag{15}\]  
where \(q_{n}\), \(q_{\mathrm{PS}}\), \(q_{\mathrm{LNA}}\), \(q_{\mathrm{RF}}\), \(q_{\mathrm{ADC}}\), \(q_{\mathrm{BB}}\), \(q_{\mathrm{M}}\), \(q_{\mathrm{LO}}\), \(q_{\mathrm{LPF}}\), \(q_{\mathrm{AMP}}\) are the power consumption values for a single microstrip antenna, the phase shifter (PS), low-noise amplifier (LNA), RF chain, ADC, baseband combiner (BB), mixer (M), local oscillator (LO), low-pass filter (LPF) and the baseband amplifier (AMP), respectively. Additionally, the terms \(E_{\mathrm{ADC}}^{\mathrm{step}}\), \(S_{\mathrm{ADC}}\) and \(B\) are the energy consumption per conversion in the ADC, sampling rate and number of bits, respectively [7]. The power consumption values (in milliwatts) for these components are listed in Table 3, recorded from studies in [24].  
Figure 5: Loss model for the proposed prediction network.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 2 System Model
### Principle of GOMIMO  
The concept of GOMIMO was first proposed in [15], which aims to fully explore the potential of MIMO transmission for spectral efficiency enhancement of bandlimited OWC systems. Specifically, GOMIMO techniques can be generally divided into two main categories: one is GOSM, in which all the activated LED transmitters transmit the same signal, and the other is GOSMP, in which the activated LED transmitters transmit different signals. For more details about the GOMIMO techniques, please refer to our previous work [15].  
Figure 1 illustrates the block diagram of a general \(N_{r}\times N_{t}\) GOMIMO system, where \(N_{a}\) (\(1\leq N_{a}\leq N_{t}\)) LEDs are activated for signal transmission during GOMIMO mapping. As we can see, the input bits are first divided into two streams: one is fed into the constellation mapper which converts the binary bits into constellation symbols, and the other is sent into the LED index selector which selects the desired LEDs to transmit the generated constellation symbols accordingly. Based on the obtained constellation symbol vector \(\mathbf{c}\) and spatial index vector \(\mathbf{v}\), GOMIMO (GOSM or GOSMP) mapping is performed to generate the transmitted signal vector \(\mathbf{x}\). The mapping tables for GOSM and GOSMP with \(N_{t}=4\) and \(N_{a}=2\) are given in insets (a) and (b) of Figure 1, respectively. On the receiver side, the received signal vector \(\mathbf{y}\) is fed into the GOMIMO detector which finally yields the output bits. The detailed GOMIMO detection schemes will be discussed in the following section.  
In typical LED-based OWC systems, intensity modulation with direct detection (IM/DD) is generally applied due to the noncoherence nature of LEDs. As a result, only real-valued nonnegative signals can be successfully transmitted in the IM/DD OWC systems [29]. In this work, unipolar \(M\)-ary PAM (\(M\)-PAM) is adopted as the modulation format for GOMIMO systems. In order to avoid the loss of spatial information when performing GOMIMO mapping, the \(M\)-PAM symbols cannot have zero values [15]. Therefore, unipolar nonzero \(M\)-PAM modulation is utilized here and the corresponding intensity levels are given by  
\[I_{m}=\frac{2I_{\mathrm{av}}}{M+1}m,\quad m=1,\cdots,M, \tag{4}\]  
where \(I_{\mathrm{av}}\) denotes the average optical power emitted [8]. By using \(M\)-PAM modulation, the spectral efficiencies (bits/s/Hz) of the \(N_{r}\times N_{t}\) GOMIMO system with \(N_{a}\) activated LEDs applying GOSM and GOSMP mappings are respectively given by  
\[\eta_{\mathrm{GOSM}}=\log_{2}(M)+\lfloor\log_{2}(C(N_{t},N_{a}))\rfloor, \tag{5}\]  
\[\eta_{\mathrm{GOSMP}}=N_{a}\log_{2}(M)+\lfloor\log_{2}(C(N_{t},N_{a}))\rfloor, \tag{6}\]  
where \(\lfloor\cdot\rfloor\) denotes the floor operator, which outputs an integer smaller or equal to its input value and \(C(\cdot,\cdot)\) represents the binomial coefficient.
# A Wear Simulation Method for Mechanical Face Seals under Friction Instability Conditions
## 2 Mathematical Model of Mechanical Face Seals under Friction Instability Conditions
### Fluid Mechanics Analysis  
The fluid pressure \(p\) was evaluated by a tribological model of the mechanical seal based on the Reynolds lubrication theory. The output of the model showed that the fluid pressure occurred at the sealing interface.  
As shown in Figure 1, a sealing dam is formed between the rotor surface and the stator surface. The fluid film not only lubricates the interface surface and reduces friction and wear but also supports the parallel mating surfaces through a hydrodynamic effect. For an incompressible lubricant and considering the flow factors introduced by [PERSON] and [PERSON] [24], the average Reynolds equation for smooth surfaces [25] is:  
\[\frac{1}{r}\frac{\partial}{\partial\alpha}(\frac{h_{n}^{3}}{12\mu}\frac{ \partial p}{\partial\alpha})+\frac{\partial}{\partial r}(\frac{m_{n}^{3}}{12 \mu}\frac{\partial p}{\partial r})=\frac{r\omega}{2}\frac{\partial h_{n}}{ \partial\alpha}+r\frac{\partial h_{n}}{\partial t}, \tag{1}\]  
where \(r\) is the radial direction, \(\alpha\) is the angular coordinate, \(\mu\) is the dynamic viscosity of the lubricant, \(\omega=\partial\alpha/\partial t\) is the relative angular speed, \(h_{\text{n}}\) is the nominal film thickness, and \(p\) is the mean hydrodynamic pressure.  
Due to the axisymmetric configuration, \(\partial p/\partial\alpha=0\) and \(\partial h_{n}/\partial\alpha=0\). Installation misalignment and cavitation are neglected. The seal operates between the mixed and boundary lubrication regimes, and the asperities interact so that the squeeze term \(\partial h_{n}/\partial t\) can be neglected [4] in the assessment of the fluid pressure. Thus, Equation (1) can be simplified as:  
\[\frac{\partial}{\partial r}(\frac{rh_{n}^{3}}{12\mu}\frac{\partial p}{ \partial r})=0. \tag{2}\]Equation (2) is then integrated to obtain the fluid pressure \(p_{r}\). Figure 2 shows the tapered film. The shape of the fluid film as a function of \(r\) is expressed as follows:  
\[h_{n}(r)=h_{1n}+n(r-r_{i}), \tag{3}\]  
where \(n\) represents the slope, and \(h_{1n}\) indicates the height of the minimum gap located at the inner radius.
## 3 Results
### AI in Regional Contexts: Business, Industry, and Agriculture
At the intersection of natural resource management and disaster response, Ref. [134] demonstrates the use of AI and machine learning to analyze the impacts of natural disasters--such as typhoon--on forest ecosystems near urban areas. This enables municipal and governmental agencies to plan the restoration of affected territories, reduce the risk of infrastructure damage, and improve preparedness for future extreme events. In [135], an environmental monitoring methodology is introduced, based on the digital information-analytical system \"Ecological Barometer\", which is designed to assess anthropogenic pressure and prevent environmental threats. The system supports environmental quality management and sustainable territorial development by providing strategic decision-making data for public authorities. In the context of monitoring progress toward Sustainable Development Goals 13 (climate action) and 15 (life on land) in Tunisia, the study proposes integrating Earth observation (EO) data with deep learning (DL) architectures [136]. A DL-based model is used to quantify progress in SDG implementation, demonstrating AI's potential in analyzing large-scale environmental datasets and informing decision making at the level of local ecosystems. Particular attention is given to the application of AI in the context of smart cities in developing countries. As noted in [137], AI, ML, IoT, and blockchain play a crucial role in addressing the challenges faced by African cities, including inadequate infrastructure, unstable access to resources, and the need for adaptive solutions. The deployment of AI under such conditions enables the development of innovative ecosystems that are resilient to both economic and environmental challenges. At the same time, Ref. [118] highlights institutional and human capital barriers to AI adoption in Africa, stressing the importance of national digitalization strategies and capacity-building initiatives to support sustainable urban growth. An example of digital twin implementation for urban infrastructure management is presented in [138], which introduces a digital village framework that leverages machine learning for environmental monitoring and enhanced emergency preparedness. These solutions strengthen the resilience of small settlements and have the potential to be scaled for urban applications. The integration of machine learning with GIS methods for synchronizing biophysical and socio-economic data provides deeper insights into the causal relationships affecting ecosystems [139]. The application of such approaches to the study of urban system resilience is particularly important, as it promotes more comprehensive and sustainable urban planning. In total, according to the regional classification, the following references pertain to Asia [134; 137; 125; 31; 25; 39]; Africa [117; 118; 119; 136; 137]; Europe [8; 40; 120; 127]; North America [121] and Latin America [124].
## 3 Modeling and Signal Analysis Method
### Joint Time-Frequency Method based on EEMD and WVD
The stopping criteria for terminating the sifting process in Figure 3 are: (1) that the number of extrema and the number of zero-crossings must differ at most by one, and (2) that the mean between the upper and lower envelopes can be considered to be zero. The disadvantage of EEMD is that the signatures overlap in both time and frequency because the extremes identify \(x(t)\). The correlation coefficient method [25] is thus used to eliminate false IMFs by a sifting process. Third, the remnant IMFs are analyzed by using the WVDas follows:  
\[WVD_{x}(t,f)=\int_{-\infty}^{\infty}c(t+\tau/2)c^{*}(x-\tau/2)e^{-jf\tau}d\tau \tag{13}\]  
where \(f\) is the frequency, \(\tau\) is the integration variable, and the asterisk (*)indicates complex conjugation. Finally, the \(WVD\)s are summed together to reconstruct the WVDs of the original signal:  
\[WVD_{x}(t,f)=WVD_{\text{C1}}(t,f)+WVD_{\text{C2}}(t,f)+\ldots\ldots+WVD_{ \text{Cn}}(t,f)=\sum_{i=1}^{n}WVD_{\text{Ci}}(t,f) \tag{14}\]
## 5 Numerical Results and Discussion
\"*\" marks indicate results of closed-form SE equation, and 'x' marks indicate results of MC simulations. Lines are plotted based on theoretical derivations. As observed, all 3 kinds of results are well-matched, and the derived closed-form equations for \(N_{g}\) can be used with  
Figure 6: SE versus number of TX antennas, \(N_{t}\). Red “*’s and black ‘x’ indicate the simulation results. high accuracy. In the case of ZF precoding, the lower bound of SE is sensitive to the variation of \(N_{g}\), while in the case of MF precoding, the lower bound of SE is not so sensitive to the variation of \(N_{g}\). Maintaining SE lower bounds is quite important to the energy efficient sensor network. The important metric of wireless sensor network is moving from SE to energy efficiency. However, keeping a stable SE lower bound is also very important for some kinds of sensor network, such as Industrial internet network. The proposed scheme provides the logic of keeping high energy efficiency maintaining SE lower bound, thus it can be a core technology for the massive MIMO-based energy efficient sensor network. Figure 8: Determined \(N_{g}\) versus guaranteed SE (\(\mathrm{bps}/\mathrm{Hz}\)) when ZF precoding is applied. “*” marks indicate the results of closed-form SE equation, and ‘O’ marks indicate results of MC simulations. Figure 7: SE versus number of antennas in each group, \(N_{g}\). Red “*”s and black ‘x’ indicate the simulation results. As we mentioned in the previous section, for the case of RZF precoding, we can use the case of ZF precoding. Figure 10 presents the SE comparison of ZF precoding and RZF precoding. We use \(\
u\triangleq\text{diag}\left[\frac{1}{\rho_{r}},\cdots,\frac{1}{\rho_{r}}\right]\) which is the downlink RX inverse SNR matrix [28; 30; 40]. As observed, the SE of RZF precoding also exactly matches with the analytical SE of ZF precoding. Thus, all the analysis for ZF precoding in this paper is also applicable to RZF. To completely validate the proposed system, it is important to perform real measurement. Since we show the feasibility of the proposed system in this paper, the real measurement for the system can be performed in future work. Figure 10: SE comparison of ZF and RZF precoding. ‘\(\star\)’ marks indicate results of closed-form SE equation for ZF precoding. Figure 9: Determined \(N_{g}\) versus guaranteed SE (\(\text{bps}/\text{Hz}\)) when MF precoding is applied. ‘\(\star\)’ marks indicate results of closed-form SE equation, and ‘O’ marks indicate results of MC simulations.