esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Non-Destructive Investigation
### Structural Investigation
#### 4.2.5 Monitoring  
Monitoring was carried out at the tower to determine the presence of ongoing movement. Three types of monitoring devices were installed: sensors for environmental conditions, manual crack-width monitors and a digital crack-width monitor.  
Digital monitoring of the main crack that runs vertically along the tower (seen propagating vertically on the left in Figure 10) was carried out for an approximate duration of 13 months. Average temperatures fluctuated between 8 \({}^{\circ}\)C and 26 \({}^{\circ}\)C and relative humidity (RH) between 52% and 85% during the period of study, while crack-width variation fluctuated between \(-\)1.051 mm and +0.425 mm (Figure 11). The temperature and relative humidity values presented along with the crack-width variation are monthly averages for the city of Gjirokastra. Negative and positive values of crack-width variation indicate closing and opening of cracks, respectively.  
A sudden change in the crack width was detected between 8:00 and 9:00 a.m. of local time in Gjirokastra on 12 January 2021. This coincided with a seismic event registered by several seismological institutions, such as the National Observatory of Athens and the European Mediterranean-Seismological Centre. The movement was evidence of the tower and/or conglomerate being vulnerable to seismic events and, therefore, signalled a need for intervention to avoid further propagation of damage or potential collapse in the case of seismic events with higher magnitudes.
# A Fluid-Structure Coupling Analysis of a Far-Field Flat Mirror for AliCPT-1 Telescope Calibration
## 3 Fluid Field Calculation
### Grid Independence Verification  
In the realm of computational fluid dynamic (CFD) simulations, the accuracy of calculations is heavily influenced by both the quantity and quality of the grids within the fluid model. Therefore, it is essential to ensure a well-structured grid to achieve accurate simulations [20; 21; 22]. Structured grids consist of regular, ordered grid cells, typically quadrilaterals or hexagons, while unstructured grids are composed of irregular, unordered grid cells, such as triangles, tetrahedra, and so on. Due to the complex structure of the reflector mirror, an unstructured grid refinement is applied to the internal domain, while a structured grid refinement is applied to the external domain.  
Additionally, grid refinement is applied to the grid interface for fluid-structure coupling to maintain consistency in node quantity and position between the fluid and solid interfaces.  
To balance computational accuracy and time efficiency, grid independence verification was conducted for the FFF reflector structure. Initially, six different grid configurations were generated, each with a mesh quality above 0.85. Simulations were carried out with the same boundary conditions to examine the impact of grid quantity on the maximum pressure (Pmax) and maximum velocity (Vmax), as illustrated in Figure 7. The results revealed that increasing the number of grids beyond 16,958,409 led to changes in Pmax and Vmax by less than 4.7%, indicating that further grid refinement would not significantly affect the simulation results.  
Subsequently, the optimal grid quantity was determined to be 24,031,137, with a base size of 5 mm, considering a balance between computational time and accuracy. The optimized mesh of the simulation model is depicted in Figure 8.  
Figure 6: Computational domain representation.  
Figure 7: Grid independence verification plot.
# Recent Progress in Synthesis and Photonic Applications of Two-Dimensional Bismuthene
## 3 Production of 2D Bismuthene
### Bottom-Up Approach
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Size** & **Throughput** & \begin{tabular}{c} **Thickness** \\ **Homogeneity** \\ \end{tabular} & \
\begin{tabular}{c} **Fabrication** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{c} **Processing** \\ **Temperature** \\ \end{tabular} & **Refs** \\ \hline Liquid exfoliation & 1-10 \(\mu\)m & High & Moderate & Moderate & RT & [24] \\ Electrochemical & 1-10 \(\mu\)m & High & Moderate & Moderate & RT & [53] \\ CVD & Over 1 cm & High & Very High & Slow & High & [56] \\ PLD & Over 1 cm & High & High & Fast & Moderate & [17] \\ MBE & High & High & Slow & High & [61] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of techniques to fabricate bismuthene.
## 2 Materials and Methods
### Generalized Analytic Microdosimetric Models
#### 2.2.4 The Combined Xapsos-Olko Model  
In this model [52] (hereafter denoted as \"XO\"), Xcom is used for the calculation of energy deposition and the microdosimetric spectra of the ion (direct) and electron (indirect) events. Specifically, to calculate the ion (direct events) energy deposition, one uses the Equations (8)-(12) and (17)-(24) from the 'X96' model. For the electron energy deposition (indirect events), one uses Equations (13) and (14) and Equations (17)-(24) for energy-loss straggling, relative variance, and the log-normal process of energy deposition, respectively. However, contrary to the Xcom model, in the XO model one uses the full secondary electron spectrum which is calculated using the following expressions [52]:  
\[\overline{L}_{el}=\frac{\int_{3W}^{T_{el,max}}L_{el}(K)\varphi(K)\mathrm{d}K}{ \int_{3W}^{T_{max}}\varphi(K)\mathrm{d}K}, \tag{25}\]  
where \(W\) is the energy required for an ion-electron hole. \(\varphi(K)\) is the degraded electron spectra given by:  
\[\varphi(T)=\frac{\int_{T}^{T_{el,max}}T^{\prime-2}\mathrm{d}T^{\prime}}{L_{el} (T)}. \tag{26}\]  
To visualise the central assumptions of the present microdosimetric models, Figure 1 depicts the two ways in which the energy can be deposited (direct, indirect) on a spherical site of arbitrary diameter.  
Figure 1: Schematic representation of the energy deposition on an arbitrary spherical target from ions (direct events) and \(\delta\)-rays (indirect events), used by the present analytical models (Xcom, XO, X94, X96).
## 4 Numerical Experimentation and Results
### Example 5  
Consider the Lorenz system [18] given by  
\[\begin{array}{llll}y_{1}^{\prime}&=a(y_{2}-y_{1}),&y_{1}(0)&=1\\ y_{2}^{\prime}&=-y_{1}y_{3}+by_{1}-y_{2},&y_{2}(0)&=5\\ y_{3}^{\prime}&=y_{1}y_{2}-cy_{3},&y_{3}(0)&=10\end{array}\]  
The constants are \(a=10,b=28,c=8/3\). With these selected constants, the Lorenz system exhibits chaotic behaviour, with the solution profiles fluctuating rapidly over extremely brief time intervals. To precisely and efficiently capture the solution, robust numerical approaches are necessary. The proposed overlapping HBM with the fewest intra-step points was employed to tackle this problem.  
The parameters of the iteration scheme are given below.  
\[\begin{array}{llll}\phi_{1}(t,y_{1},y_{2},y_{3})&=-a,&\psi_{1}(t,y_{1},y_{2 },y_{3})&=ay_{2},\\ \phi_{2}(t,y_{1},y_{2},y_{3})&=-1,&\psi_{2}(t,y_{1},y_{2},y_{3})&=by_{1}-y_{1} y_{3},\\ \phi_{3}(t,y_{1},y_{2},y_{3})&=-c,&\psi_{3}(t,y_{1},y_{2},y_{3})&=y_{1}y_{2} \end{array}\]  
In Figures 9 and 10, respectively, the time series solutions and phase portraits for the Lorenz equation are displayed. These results were produced using OOHBM-3 with \(h=0.01\) across the integrating domain [0,50]. The results are identical to those reported in numerous publications on the Lorenz system's solutions (see, for example, [19]).  
Figure 8: Solution profiles of Example 4 with \(m=3\) and \(h=0.001\). (**a**) concentrations of \(y1\) and \(y3\). (**b**) concentrations of \(y2\) (**c**) concentration \(y1\), \(y2\) and \(y3\).  
Figure 10: Phase portraits of the Lorenz system. (**a**) \(y1\) and \(y2\); (**b**) \(y1\) and \(y3\); (**c**) \(y2\) and \(y3\).  
Figure 9: Time series profiles of the Lorenz system. (**a**) \(y1\); (**b**) \(y2\); (**c**) \(y3\).
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 5 Artificial Intelligence in Solar Plant Monitoring  
Artificial intelligence (AI) can perform a vital role in the monitoring, analysis, and management of solar PV systems. The solar plants are mostly installed in remote areas, while ensuring optimal energy generation, predicting and preventing system failures, and maintenance of the solar plants is crucial.  
Figure 6: AWS infrastructure architectural overview for solar plant monitoring. This figure demonstrates the integration of AWS services involved in smart solar plant monitoring systems. Data flow from edge nodes through AWS IoT Core and are processed using Kinesis Analytics to detect anomalies. Depending on the analysis, notifications are sent via AWS SNS, and the data are stored in Amazon S3 for further use. The AWS Cognito facilitates secure access to user dashboards for monitoring and decision making.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis
### Energy Consumption Assessment  
In order to test the energy consumption of this design, we selected the GPS positioning module u-blox NEO-6M (u-blox, Thalwil, Switzerland) commonly used in the market to conduct power consumption comparison experiments with this design. The test platform selected was the laboratory digital-analog hybrid test platform ST3020, and it performed an FVMI analysis on standby mode (only maintaining the RTOS idling) and continuous positioning mode (1 Hz positioning + screen brightness), respectively. The power supply voltage was 3.3 V \(\pm\) 1%, and the ambient temperature was 20 \({}^{\circ}\)C. The test results are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Average Error** & **Maximum Error (\%)** & **Number of (\%)** & **Memory Overflow** \\  & **(\%)** & **(\%)** & **Trajectory Breaks** & **Rate (\%)** \\ \hline GPS + fixed block memory & 3.62 & 6.71 & 1.8 & 8.4 \\ BeiDou + fixedblock memory & 3.15 & 5.89 & 1.4 & 7.6 \\ This design & 1.71 & 2.72 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Analog signal interrupt test results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operating Mode** & **This Design Current (Ma)** & **GPS Module (Ma)** & **Energy Saving Ratio (\%)** \\ \hline Standby & 8.2 & 12.5 & 34.4 \\ Continuous positioning & 68 & 92 & 26.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Power consumption comparison table.
## 6 Experimental Verification
As shown in Figure 6, compared with no output disturbance, the signals of the yaw rate sensor and the roll rate sensor with output disturbance produce high fluctuation and cannot accurately reflect forklift conditions. In Figure 7, compared with the SMO, the ASMO can reconstruct yaw rate sensor faults and roll rate sensor faults more effectively. On the other hand, the ASMO can also suppress external output disturbance. As shown in Figure 8, based on the adaptive algorithm, adjustable gain parameters can change with the fault amplitude and the external output disturbance. When a fault occurs, values of the  
Figure 5: (a) Fault and its reconstruction value of the yaw rate sensor; (b) Fault and its reconstruction value of the roll rate sensor. ASMO: Adaptive sliding mode observer; SMO: sliding mode observer. Figure 6: (a) The yaw rate sensor signal without a fault; (b) The roll rate sensor signal without a fault. Figure 7: (a) Fault and its reconstruction value for the yaw rate sensor; (b) Fault and its reconstruction value for the roll rate sensor. ASMO: adaptive sliding mode observer; SMO: sliding mode observer. adjustable parameters rise because of the increase in state estimation. And as \(f_{s1}\) is higher than \(f_{s1}\), \(\rho_{1}\) is higher than \(\rho_{2}\). When the fault is removed, the \(\text{sgn}(\|D\tilde{e}_{y\|}|-\lambda_{i})=-1\), \(\rho_{i}(t)\) decreases correspondingly. It means that the ASMO can improve its fault-tolerant capability to deal with possible unknown faults.
## 3 Problem Formulation & Terminology
### Evaluation Metrics  
In this section, we describe the three evaluation metrics used to assess the effectiveness of the proposed partitioning strategy. Note that our strategy was only tested through a simulation, meaning that metrics such as scalability, distinguishability, QoS, and defense cost [21] are not directly applicable. Nevertheless, as future work, we aim to stretch our strategy in real-life scenarios, e.g., through a testbed deployed on a cloud data center and in the presence of a real-life attack, say, Slowloris.
**Time elapsed:**: When no stopping criteria are applied this metric refers to the time required to fully isolate all malicious connections to several nodes. Conversely, if a stopping criterion is applied the metric pertains to the time required to isolate a satisfactory percentage of the malicious connections in several nodes. Note that in case of a suboptimal solution, we are willing to sacrifice a small number of benign connections. By relaxing this constraint the system may yield suboptimal solutions significantly faster.  
**Detection rate:** The percentage of the benign connections that are salvaged in the event of a suboptimal solution. In the case of a complete solution, this metric is irrelevant, as all the malicious connections are isolated.  
**Resources consumption:** The number of resources including nodes spawned, Random-Access Memory (RAM) consumption, and number of threads required for the scheme to converge to a solution. The act of partitioning that internally involves the spawning of new nodes contributes to the consumption of significant resources. Note that since our partitioning strategy was only tested in a simulation context, we only measured the nodes spawned as the resource consumption metric.
## 4 Discussion
Variations in the discount rate can markedly influence NPC and LCOE, potentially altering conclusions regarding scenario preferences. Similarly, fluctuations in component costs--particularly for PV modules, biogas generators, battery storage systems, and biomass feedstock--can affect the system's overall economic attractiveness. Given these sensitivities, caution is required when interpreting and generalizing results based on single-value analyses. Future studies should incorporate detailed sensitivity or uncertainty analyses, such as Monte Carlo simulations, to capture these variabilities comprehensively and provide more robust decision-making guidance. Future improvements could include exploring advanced storage solutions, such as lithium-iron-phosphate or flow batteries, to enhance load leveling, system flexibility, and thermal storage for combined heat and power (CHP) applications. Demand-side management strategies like load shifting and energy efficiency upgrades could reduce overall consumption and align demand with periods of high renewable generation. Integrating wind or micro-hydro resources could diversify the power mix if geographically viable. High-resolution monitoring and AI-driven forecasting would allow real-time generator dispatch and battery scheduling optimization. On the modeling side, sensitivity analyses and hybrid approaches that combine HOMER with other specialized tools (e.g., life-cycle assessment) would yield a fuller understanding of technical, economic, and environmental trade-offs. Additionally, expanding to a community or regional scale could facilitate resource-sharing networks, achieving higher economies of scale and stimulating local investment. It is important to note that the present analysis assumes fixed economic parameters, such as discount rates, component costs, and biomass feedstock prices. Given that variations in these parameters may significantly impact economic feasibility and decision-making outcomes, future research is strongly recommended to perform sensitivity analyses or uncertainty modeling (e.g., using Monte Carlo simulations) to better quantify and manage these uncertainties, thereby enhancing the robustness and practical applicability of hybrid renewable energy system evaluations. Ultimately, this study achieves its initial objective by demonstrating how the proposed HES can address cost concerns, enhance reliability, and reduce emissions in rural or semi-remote areas. Scenario I reveals a cost-effective pathway through PV self-consumption, whereas Scenario II provides stronger energy independence and environmental gains. These outcomes confirm that tailoring system architectures to local resources and conditions effectively bridges the gap between existing infrastructure limits and sustainable energy requirements. Hybrid microgrids can be scaled into powerful tools for addressing global energy challenges, reducing fossil fuel dependence, and promoting equitable socio-economic development by incorporating improvements in storage, demand-side management, and policy frameworks.
## 4 Hardware Implementation of CPU-SPU Collaborative SoC
### Performance Optimizations of SPU Security Monitoring
#### 4.3.3 Worst Situation Depiction  
In the above-mentioned optimizations of M-Cache and I-Cache, an important consideration is the clock timing overhead or conflict induced by SPU, which induced by instruction monitoring and instruction fault recovery. The worst situation of performance overhead occurs when M-Cache and I-Cache both fail to contribute to the integrity verification for the current BB, and the instruction monitor requires searching the BB's \(INF_{ref}\) block in the entire monitor memory.  
Figure 10: The internal implementation mechanism of the I-Cache with the size of 8 KB.  
Moreover, the FSRM will further perform the instruction fault recovery once the BB integrity check is invalid. Figure 11 depicts the timing diagram of one BB execution with integrity validation and instruction recovery in the worst situation. The period of T1 represents the total time consumption for searching the BB \(INF_{ref}\) block in both M-Cache and monitor memory from a new BB being detected, and its search result can be obtained with a high probability before the recalculated results of the LHash engine.  
The period of T2 represents the time consumption of the golden LHash \(DIG_{Hash}\) being obtained and waiting for verification. Period T3 indicates that the integrity checker completes the comparison and outputs the validation status within one clock cycle. Since the searching process of \(INF_{ref}\) relying on the \(ADD_{start}\) of each BB in the M-Cache and monitor memory is simultaneous with instruction executions, it can minimize the performance overhead of the BB integrity checking; thus, the time consumption on searching \(INF_{ref}\) in the M-Cache and monitor memory are both acceptable for integrity validation.  
In the instruction recovery phase when the BB integrity verification has failed, the activated checkpoint recovery signal closely follows the status of the BB integrity validation. It is later only three clock cycles from the end of the current BB boundary to CPU_Stall signal suspension, it is indicated that the BB recovery will not cause potential clock timing conflicts even in executing the shortest BB (only three instructions).
# Study of Standing-Wave Thermoacoustic Electricity Generators for Low-Power Applications
## 5 Conclusions  
This paper describes the design methodology of thermoacoustic electricity generators using a standard loudspeaker as a linear alternator. Based on target electrical output power of 50 and 100 W, three standing wave TAEGs working in half-wavelength mode have been conceived with different coupling arrangements of the LA. All the TAEGs models, intended for low-power and low-cost applications, use air at atmospheric pressure as working fluid. Simulation performance by the specialized DeltaEC design tool reveal that the one-stage engine and two-stage engine with LA coupled in a side branch arrangement have comparable efficiencies and are able to meet the target output power values (50 and 100 W, respectively) although they are placed in different impedance locations (high impedance for the first, and low impedance the second). The two-stage engine with LA coupled in push-pull mode is unable to reach the desired performance.  
These different behaviors can be explained observing that in the one-stage TAEG the LA is placed in a high-impedance location of the standing wave (\(\,\mathrm{I\,Z_{a}}\,\mathrm{l}\,\approx 10^{5}\) Pa s/m\({}^{3}\)). This entails relatively high driving pressures (\(\,\mathrm{l}\,p_{1}\,\approx 10^{4}\) Pa) which, in turn, enable the stack for producing high acoustic power levels. In this case, the phase angle \(\phi_{pU}\) is quite high (\(\phi_{pU}\approx 82^{\circ}\)) as is typical of standing waves.  
In the TAEG with LA in a side-branch arrangement the LA is placed in a location with relatively low impedance (\(\,\mathrm{I\,Z_{a}}\,\mathrm{l}\,\approx 10^{4}\) Pa s/m\({}^{3}\)), and this entails low driving pressures (\(\,\mathrm{l}\,p_{1}\,\approx 10^{3}\) Pa). In this case, however, the branch decouples the acoustic field driving the alternator from the one sustained by the trunk, allowing the stacks to work under high pressure levels, which enables the production of high acoustic powers. Furthermore, the impedance change operated by the branch and by the compliance associated to the back volume brings a favorable \(\phi_{pU}\) near low values (\(\sim\)10\({}^{\circ}\)).  
In the TAEG with LA in push-pull mode, the LA is also placed in a low-impedance location of the standing wave (\(\mid\!Z_{\mathrm{a}}\!\mid\approx 10^{4}\) Pa s/m\({}^{3}\)) and the driving pressures are low (\(\mid\!p_{1}-p_{2}\!\mid\approx 10^{3}\) Pa). As a consequence, the stacks generate a low acoustic power which, with the phase angle \(\phi_{pU}\) being quite high (\(\phi_{pU}\approx 82^{\circ}\)), is poorly absorbed by the LA. In this case, poor impedance matching is realized.  
The author declares no conflict of interest.
## 4 Classification of MAC Protocols Based on Channel
### Centralized Single-Channel MAC Protocols  
It is either RSU based or cluster head (CH) based. RSUs allocate time slots to the nodes for channel access. It is a better mechanism for providing collision-free access as scheduling of time slots is not as complicated. The periodical broadcasting mechanism employed by RSUs minimizes collision by communicating the time slot reservation messages to nearby vehicles.  
The coordination is wholly dependent on either the RSU or the CH. The data are transmitted by a time-synchronization mechanism to avoid collision, but there is network overload due to the centralized control.
## 1 Introduction
A computationally-efficient table-based adaptive DPLL algorithm for GNSS receivers operating in Moon exploration missions is proposed. Each noise bandwidth value of the table is calculated optimally in consideration of thermal noise, Allan deviation, vibration-induced oscillator phase noise, and dynamic stress error in the receiver design phase. The simulation scenario is configured using the analysis result of the mission trajectory for the Moon exploration, and the numerical simulation result for the proposed algorithm is presented. This algorithm is compared with other adaptive DPLL algorithms (FAB, FL, LBCA) in terms of jitter performance and execution time. The remainder of this paper is composed as follows: The Moon exploration mission trajectory is analyzed in Section 2. The calculation method and generation result example of the optimal bandwidth table are presented in Section 3. The proposed table-based adaptive DPLL algorithm is illustrated in Section 4, and the numerical simulation result is discussed in Section 5. The proposed algorithm is evaluated in Section 6, followed by the conclusion in Section 7.