esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Models
### Research Methodology  
In order for the obtained results to be reliable and for the analyses and decisions made on their basis to be correct, the research should be comprehensive and methodical. A methodology for conducting comprehensive research was developed, verified, expanded, and improved. The research methodology consists of seven stages and is presented in Figure 2:  
* Stage 1: Obtaining data on construction projects;
* Stage 2: Development of the knowledge base;
* Stage 3: Processing of collected data;
* Stage 4: Graphical representation of the processed collected data;
* Stage 5: Determination of best-fit curves;
* Stage 6: Determination of the area of cost curves;
* Stage 7: Designation of procedure scenarios.  
The research methodology allows for the examination of the shape and trajectory of the cost curve during the implementation of investment projects. This is achieved through the cyclical calculation of cost and schedule deviations, as well as performance indicators,  
Figure 2: Flowchart of the research methodology.  
cost forecasts, and project duration. The research focused on the cost of construction works, which constitutes 75-85% of the budget of the construction project (commonly called \"hard cost\").
# Zero-Voltage Ride-Through Capability of Single-Phase Grid-Connected Photovoltaic Systems
## 3 Overview of PLL Synchronization Techniques
### Comparison of the PLLs  
According to the aforementioned analysis, the applicability of the selected PLL methods should be identified, which can detect the grid fault precisely and rapidly under grid voltage faults (even drop to zero). The system and control parameters are shown in Table 1. Since a fault case of the grid voltage occurs, the steady-state output signal should maintain at the rated frequency with small or no fluctuation and overshoot. Therefore, the comparison will be focused on the speed and precision of detecting the grid voltage, frequency and phase by the selected PLL systems.  
Figure 6 shows the dynamics of the PLLs under a grid voltage drop of 0.6 p.u. Both methods can estimate the amplitude and the frequency of the grid voltage without double-frequency ripples in the steady-state condition, as shown in Figure 6. When comparing with the EPLL, the SOGI-PLL approaches the steady state faster. However, the overshoot of the SOGI-PLL is observed in the progress of the amplitude detection. Figure 7 then shows the dynamics of the PLLs under a voltage sag of 100% (i.e., the grid voltage drops to zero). Compared with the LVRT condition, the superior performance of the SOGI-PLL is significant, as seen from Figure 7. However, the EPLL takes the advantage in suppressing the overshoot. Moreover, it should be noted that the estimated frequency outputs have steady-state errors in both the SOGI-PLL and EPLL when the grid voltage drops. It will affect the accuracy of the phase and frequency detection in the ZVRT operation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Description** & **Value** \\ \hline Normal voltage amplitude & \(V_{\rm gm}=311\) V \\ Normal grid frequency & \(\omega_{g}=314\) rad/s \\ Sampling and switching frequency & \(f_{s}=f_{sid}=10\) kHz \\ plus integral (PI) controller of Loop Filter (LF) for all (phase-locked loop) PLLs & \(k_{p}=112.7\), \(k_{i}=1054\) \\ Control parameters & \(k_{\rm EPLL}=150\), \(k_{\rm SOGI}=0.707\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the PLL system in the single-phase grid-connected system.  
A comparative analysis of the EPLL and SOGI-PLL reveals that both of them can estimate the amplitude and filter out the harmonics, resulting in clean signals for synchronization. However, the output signals of the EPLL present large variations during transients, and the output signals of the SOGI-PLL present large overshoots. Compared with the EPLL, the SOGI-PLL approaches the steady state much faster, and that is more effective depending on the voltage sag depth. Thus, in general, the SOGI-PLL system can be a promising solution for a monitoring and synchronization technique used in single-phase applications, especially in the case of LVRT/ZVRT operations. However, the steady-state error in the case of zero-voltage faults is also observed.
## Results
### Orange TSS Detection by Fusion Data of VIS/NIR Spectrometer and E-nose  
#### 3.3.1 PlsR Detection  
Even though both the reflection spectrometer and E-nose can nondestructively acquire orange TSS related information, the detection ability of those single detection methods are unsatisfactory. Multi-source information fusion methods have been reported that can efficiently improve detection accuracy and stability [28]. To further improve the detection accuracy of orange TSS, modeling work based on the reflection spectrometer and E-nose fusion data was researched. According to the research results above, we merged the 736 VIS/NIR spectrum features and combined E-nose features (50 features)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MI Range** & \(\geq\)**0.61** & \(\geq\)**0.62/0.63** & \(\geq\)**0.64/0.65** & \(\geq\)**0.66/0.67** & \(\geq\)**0.68/0.69** \\ \hline
**Remaining Feature Number** & 48 & 45 & 42 & 40 & 39 \\
**R2 cal** & 0.5319 & 0.5280 & 0.5212 & 0.5197 & 0.5103 \\
**R2 val** & 0.3516 & 0.3426 & 0.3341 & 0.2255 & 0.1815 \\ \hline \hline \end{tabular}
\end{table}
Table 5: PLSR detection results of TSS based on E-nose data with MI feature elimination.  
Figure 7: MI between TSS and feature of each feature. Note: feature 1–10 are the 75 s value of 10 sensor’s response, respectively; Feature 11–20 are the average value of 10 sensor’s response, respectively; Feature 21–30 are the average of differential value of 10 sensor’s response, respectively; Feature 31–40 are the integral value of 10 sensor’s response, respectively; Feature 41–50 are the max value of 10 sensor’s response, respectively.  
for the fusion data. PLSR was applied to test the detection ability of orange TSS based on the fusion data, and the results are shown in Figure 8. The R\({}^{2}\), RMSE, and slope for the calibration set of PLSR detection were 0.9466, 0.2395, and 0.9466, respectively, for the validation set of PLSR detection were 0.7516, 0.7804, 1.1072, respectively. Thus, the data fusion can significantly improve the detection accuracy of orange TSS compared to a single detection method.
## 3 Numerical Simulation and Results
### Finite Element Model
#### 3.1.2 Material Model  
Cutting is a highly complex process, and many factors need to be considered, such as the nonlinear properties of materials, etc. No valid numerical simulation method can directly simulate tool failure, and obtaining temperature-dependent material parameters requires many costly experiments. Therefore, the HSS tool is considered an ideal elastic material for numerical simulations in this paper. The energy-absorbing tube is made of S355 steel and simulated with the Johnson-Cook material model. It is widely used in the cutting simulation to simultaneously consider three effects of strain hardening, strain rate strengthening, and temperature softening of metallic materials. The specific equation is  
\[\sigma_{\rm y}=(A+B\bar{\varepsilon}^{p^{n}})(1+C\ln\dot{\varepsilon}^{*})(1-T^ {*^{n}}) \tag{1}\]  
\[T^{*}=(T-T_{room})/(T_{melt}-T_{room}) \tag{2}\]  
where \(\bar{\varepsilon}^{p}\) is the equivalent plastic strain, \(\bar{\varepsilon}^{p}=\dot{\varepsilon}/\dot{\varepsilon}_{0}\) is the dimensionless plastic strain rate for \(\dot{\varepsilon}_{0}=1.0s^{-1}\), and \(T^{*}\) is the homologous temperature, \(T_{room}\) is the room temperature, and \(T_{melt}\) is the melting temperature. _A,B,C,n,m_ are material constants.  
To simulate the separation of metal material in the cutting process, the Johnson-Cook fracture model is determined, and the expression for strain at fracture is given by  
\[\varepsilon^{f}=[D_{1}+D_{2}\exp D_{3}\sigma^{*}][1+D_{4}\ln\dot{\varepsilon} ^{*}][1+D_{5}T^{*}] \tag{3}\]  
where \(\sigma^{*}\) is the ratio of pressure divided by effective stress  
\[\sigma^{*}=\frac{p}{\sigma_{eff}} \tag{4}\]  
Damage to an element is defined by  
\[D=\sum\frac{\Delta\bar{\varepsilon}^{p}}{e^{f}} \tag{5}\]  
where \(\Delta\bar{\varepsilon}^{p}\) is the cumulative plastic strain, and \(\varepsilon^{f}\) is the fracture strain under the current conditions of stress triaxiality, strain rate, and temperature. A fracture occurs when \(D=1\)  
Figure 4: (**a**) Tool-tube finite element model and (**b**) structural parameters.  
An equation of state is needed for Johnson-Cook to describe how solid materials behave in LS-DYNA. The linear polynomial equation of state is given by  
\[P=C_{0}+C_{1}\mu+C_{2}\mu^{2}+C_{3}\mu^{3}+(C_{4}+C_{5}\mu+C_{6}\mu^{2})E \tag{6}\]  
By setting \(C_{1}\) as the bulk modulus and all other \(C_{x}\) terms to zero, the linear polynomial equation of state could achieve simple bulk behavior if strain rates are low to moderate.  
The specific values of the Johnson-Cook material model and material thermal properties are presented in Tables 1 and 2.
## 5 Experimental Results and Discussion
### Experiments of 1# Piezolectric Brick
#### 5.1.2 Average Powers from PZT4 Patch of 1#1 on Unimorph PCS (Close-Circuit)  
Based on a series of experiments, the average powers from 1#1 PZT4 patch on unimorph PCS of 1# piezoelectric brick corresponding to different load resistance are recorded in the condition of \(F\) = 500 N and \(f\) = 1 Hz. The average output power from 1#1 PZT4 patch on unimorph PCS versus load resistance from 10 k\(\Omega\) to 900 k\(\Omega\) is displayed in Figure 9. Obviously, in the excitation of exerted force (_F_ =500 N, \(f\) = 1 Hz), for the force arm length of 19.5 mm, 26.5 mm, and 33.5 mm, the average output powers from 1#1 PZT4 patch show a trend of rising first and then a dropping as (0.01 mW, 2.04 mW, 0.68 mW), (0.78 mW, 5.67 mW, 2.45 mW), and (3.65 mW, 11.32 mW, 4.1 mW) respectively with the increase in the load resistances from 10 k\(\Omega\) to 900 k\(\Omega\). The optimal load resistances corresponding to the maximum average powers of 2.04 mW, 5.67 mW, and 11.32 W are 333. K\(\Omega\), 285.7 K\(\Omega\), and 230.8 K\(\Omega\), respectively. The maximum average output power densities from 1 # 1 PZT4 patch are 0.82 uW/mm\({}^{3}\), 2.27 uW/mm\({}^{3}\) and 4.53 uW/mm\({}^{3}\) corresponding to the force arm of 19.5 mm, 26.5 mm, and 33.5 mm, respectively. The results show that with the increase in the force arm length, average output powers and average power densities from 1#1 PZT4 patch increase, but the optimal load resistance decreases. This is because the increase in the force arm length would cause the increase in the surface strain exerted on LPCB and finally leads to the increases in output average powers and average power densities. This phenomenon can also be explained by Equations (9)-(11) and (16)-(18). In addition, with the increase in the force arm length, the 1 st resonant frequency of the LPCB will decrease which is positive correlation to the optimal load resistance.
## 4 Evaluation
### Experimental Setup
#### 4.1.3 Evaluate Metrics  
We evaluate and compare BBDetector, B2 SFinder and ModX based on three criteria: effectiveness, efficiency and code obfuscation-resilient capability. Specifically, we compare the effectiveness and code obfuscation-resilient capability of these TPL methods on the mix binary dataset or ubuntu binary dataset by using three metrics: precision (P), recall (R)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Set A** & **Set B** & **Set C** & **Total** \\ \hline File Size (KB) & 0–100 & 100–1000 & \textgreater{}1000 & 16.4–4413.5 \\ Average Size (KB) & 61.8 & 297.8 & 2210.2 & 724.8 \\ Number of Binaries & 15 & 66 & 25 & 106 \\ Average Number of Functions & 159.6 & 652.1 & 4224.8 & 1425 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nix binary dataset used for evaluating TPL detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Program** & **Libs Num** & **Libs Linked** \\ \hline ssldump & 2 & libc.so.6, libpcap.so.0.8 \\ vim & 4 & libtinfo.so.6, libdl.so.2, libm.so.6, libc.so.6 \\ busybox & 3 & libresoliv.so.2, libm.so.6, libc.so.6 \\ tcpdummy & 3 & libpcap.so.0,8, libc.so.6, libcap-ng.so.0 \\ openvpm & 5 & liblzo2.so.2, libssl.so.47, libcrypto.so.45, libthread.so.0, libc.so.6 \\ sqlite3 & 3 & liblbl.sol.2, libtbread.so.0, libc.so.6 \\ opensl & 5 & libssl.so.1.1, libdl.so.2, libcrypto.so.1.1, libthread.so.0, libc.so.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ubuntu datasets used for evaluating TPL detection.  
and F1-score (F1). The definitions of these metrics are shown as Equation (4), where TP is the number of TPLs detected by detection methods that are actually in the binaries; FP is the number of TPLs which do not belong to the binaries, but are wrongly recognized by detection methods; and FN is the number of TPLs which belong to the binaries, but are unrecognized by detection methods. For efficiency, we compare the detection time of each method.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}},\text{Recall}=\frac{ \text{TP}}{\text{TP}+\text{FN}},\text{F1-score}=\frac{2*\text{Precision}*\text{ Recall}}{\text{Precision}+\text{Recall}} \tag{4}\]
## 3 Lightweight Cryptography for Smart Objects  
The development of the IoT will result in the extensive deployment of billions of smart objects that will interact with the existing Internet. Smart objects are tiny computing devices, featuring constrained resources, such as low computation capabilities, little memory, and limited battery. Communication with smart objects in resource-constrained environments must necessarily take into account these hard limitations, especially in scenarios where security is a crucial aspect and conventional cryptographic primitives, such as the Advanced Encryption Standard (AES) [22], are inadequate.  
_Lightweight Cryptography_ (LWC) is a very interesting research area, aiming at the design of new ciphers that might meet the requirements set by the use of smart objects [23]. The term \"lightweight\" should not be mistaken with weak (in terms of cryptographic protection), but should instead be interpreted as referring to a family of cryptographic algorithms with smaller footprint, low energy consumption, and low computational power needs. These ciphers aim at providing sufficient security in the environment of restricted resources as can be found in many ubiquitous devices [24]. LWC thus represents a cryptography tailored to constrained devices, which must cope with the trade-offs between security level, cost, and performance.  
In this section, an overview of the most prominent cryptographic algorithms is presented, followed by a comparison among lightweight cryptographic primitives and conventional ones, such as AES, which are currently adopted by standard Internet security protocol, such as IPSec and TLS. Symmetric ciphers for lightweight cryptography are presented first, followed by asymmetric ciphers. Cryptographic hash functions are discussed next. Finally, privacy homomorphism is discussed. We remark that this overview is not meant to be detailed or extensive, but aims at pointing out which encryption algorithms are most suitable for practical implementation in IoT scenarios.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 5 Future Trends
### Integration of Autonomous Agents  
Another major application trend will be the incorporation of autonomous agents such as unmanned aerial vehicles (UAVs), robots, and other artificial intelligence (AI) platforms. The recent adoption of UAVs and mobile robots for various activities within PV installations has created a new niche in PV manufacturing and maintenance [140].  
Solar cell prototyping DTs require AI platforms to learn and evolve [141] continuously in modeling and evaluating the new cell varieties being prototyped. Solar cell and module manufacturing DTs require several AI platforms and industrial robots to ensure precision engineering and quality monitoring of the manufactured devices [38]. The operation and maintenance of DTs will require the inclusion of UAVs, robots, AI platforms, and associated data for effective performance estimation. The use of UAVs for infrared [142; 143], photoluminescence [144], or electroluminescence [145; 146] imaging of solar arrays has proven to meaningfully improve predictive maintenance, thereby saving installation owners a significant amount in maintenance costs and improving overall energy yield [147]. The inclusion of DTs will imply that the UAVs and robot inspection can be monitored and controlled in real time from the DT. This way, the data collected can be autonomously transferred to the DT database rather than the manual transfer approach that is currently being implemented. It is envisaged that this would improve workflow autonomy and reduce associated labor intensiveness of the existing inspection using UAVs and robots.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 2 Related Work
### Model Pruning and Quantization
At the time of update, all gradients are added in groups by bin, then multiplied by the learning rate (lr) and subtracted from the last shared centroid. This allows the COTV layer in the model to be quantized by 8 bits, reducing memory while maintaining accuracy. [PERSON] et al. [36] proposed a training quantization method for binary networks, quantized the BinaryConnect activation value to 1 bit, and replaced addition and multiplication operations with bitwise operations, which effectively reduced the memory consumption of memory. In 2022, [PERSON] et al. [37] proposed a quantitativeimprovement strategy based on learnable lookup tables (LLT), which greatly reduced the computational cost by converting quantification into a lookup process.
## 2 Image Acquisition  
### Patient cohort  
A total of 85 patients with GBM treated by anti-angiogenesis drugs were included in our preliminary study from our research database. Images in this database were acquired as part of multicenter GBM treatment trial. Tumors were diagnosed by board-certified radiologists as responders or non-responders to drugs based on Macdonald criteria from follow-up scans (8-10 weeks after baseline). The Macdonald criteria define tumor response by use of tumor size change, steroids, and neurological functions. There are four'response' categories: complete response (CR): disappearance of enhancing tumors, off steroids, and neurologically stable or improved. Partial response (PR): \(>\)50% reduction in size of enhancing tumor, steroids stable or reduced, neurologically stable or improved. Progressive disease (PD): \(>\)25% increase in size of enhancing tumor or any new tumor, or neurologically worse, and steroids stable or increased. Stable disease (SD): all other situations. In our study, we used tumor volume to evaluate tumor sizes. More than 50% increase in volume is considered to be the PD based on the neuro-radiologist's suggestion [44]. Since GBM is a rapidly progressing disease, we classified PD as non-responders and CR, PR and SD as responders. As a result, 39 were responders and 46 were non-responders. The DW-MRI scans were performed 5-7 weeks apart between baseline and follow-up scans.  
The patients in this study were pooled from six medical sites scanned on 9 different scanner models (GE/Siemens) including both 1.5 T and 3 T scanners. The imaging protocol for T1 wCE is 3D volume in the axial plane with flip angle-spoiled gradient echo sequence (FSPGR) or magnetization-prepared rapid gradient-echo (MP-RAGE) sequence, 1-5 mm slice thickness, 0.9375 mm by 0.9375 mm pixel size, and 256 \(\times\) 256 in-plane resolution. The imaging protocol for the DW images is either DWI or different tensor imaging (DTI), 700-1,000 s/mm\({}^{2}\) for b-value, 3-30 for the number of diffusion sensitization probing directions, 5-7 mm slice thickness, 1.797 mm by 1.797 mm pixel size, and 256 \(\times\) 256 or 128 \(\times\) 128 in-plane resolution.  
We developed a quality assurance technique to evaluate the consistency of ADC measurements from multiple scanners and multiple visits by use of ROI analysis with normal appearing white matter [35]. Our study [35] showed that there is no significant difference in ADC measurement among the different scanner models used. For the between-visit reproducibility, ADC measurement was found to be reproducible with consistent image protocols.
# Applied Engineering Using Schumann Resonance for Earthquakes Monitoring
## Materials and Methods
### ELF Sensor
The magnetic sensors are two magnetic antennas oriented along the main cardinal directions (north-south and east-west). Each sensor is made up of two symmetrical coils that provide balanced differential signals helping to reduce common mode effects [59; 60]. An optional notch filter was implemented as part of the measurement system using a double Active cell [61] to attenuate the power signals. The A/D converter system consists of a 24-bit high resolution system that is based on oversampling techniques. The obtained data from the converter is subject to a decimating process through temporary averaging. This causes a reduction in the acquisition noise, as well as in the sampling frequency. By a high-performance embedded Digital Signal Processor (DSP) system is done by this pre-processing. Ultimately, data are sent in real-time by a high-speed link to the research group facilities in Almeria University, where is worked using Power Spectral Density (PSD) estimation techniques [62]. Through the development of this system, the Schumann resonance signal capture efficiency will be enhanced, thereby facilitating their subsequent analysis and study. The data collected from these observatories are like those presented in Figure 4a,b. In these figures, a periodogram and a spectrogram are represented, respectively, recorded from the Calar Alto ELF station (Spain). These representations were established from the data recordings in normal conditions of the day 16 September 2015. The periodogram (Figure 4a) was obtained by 30 min averaging, the result of Fast  
Figure 3: ELF wave sensor diagram. Fourier Transform (FFT) of 20 s data segments. The 24 h spectrogram is composed from periodograms that are obtained by averaging each 10-min segments of 20 s.  
In this periodogram, the first 7 resonance peaks can be observed, as well as the signal from the power lines. In the spectrogram, you can see beside the Resonance Schumann modes, variations in power line signals and other signals that affect all frequencies about the 500 min, which is due to strong wind. Therefore, by monitoring the evolution of these electromagnetic signals, we can establish a study of their variation and find out the mechanisms that are related these changes with the occurrence of seismic events. To relate ELF signals' alterations with strong earthquakes, several cases must be studied. The significance of the data would depend on the number of ELF stations around the epicenter, so common ELF alterations could be found and lay out the criteria that might allow early earthquake detection.