esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Correlation Between Operational Parameters and Acoustic Emission Data
#### 4.2.2 Rotor Speed  
The analysis of the correlation between acoustic signal features and rotor speed reveals a more specific dependency than wind speed, as seen in Figure 10 through the trends of the moving averages of the maxima.  
Figure 9: Correlation between wind speed and signal features amplitude in decibel, energy in engineering unit, and spectral centroid in kilohertz highlighted as moving average of the maxima for (**a**) sensor 3 (transition piece) and (**b**) sensor 7 (foundation).  
Again, the correlation is most pronounced for sensor 7's amplitudes. Up to a speed of 5 rpm, the operational noise level remains low, at approximately 70 dB. Between 5 and 7 rpm, the moving average increases by about 12 dB. The changes in this speed range are associated with irregular interactions of rotor speed, blade pitch angle, and wind speed, which are attributable to an optimization of machine operation or switching processes. Between 7 rpm and around 8.5 rpm, another increase in amplitudes of about 6 dB is observed. Throughout the range where acoustic intensity increases, there is a significant scattering of the maxima. At approximately 8.5 rpm, the scattering abruptly decreases, becoming slightly larger again around 14 rpm. This pattern seems to be closely related to the blade pitch angle. A correlation analysis with respect to this parameter indicates that the blade pitch angle remains nearly constant during these speeds and only exhibits more substantial variations around 14 rpm. Other signal features also show these distinctive characteristics in specific speed ranges.
## 3 Results and Discussion
### Sedimentation Performance  
Figure 4a shows the SVI of each compartment on the 35 th day of start-up. Because the 1 st compartment was at the front end of the acid production stage needed to absorb a large amount of suspended solid and organic matrix in the influent for a long time, the values of SVI ranged from 54 to 112 mL/g, with an average value of 71 mL/g, indicating poor sedimentation performance. The 2 nd and the 3 rd compartments were in the flourishing stage of acid production and methanogenesis, and the nutrients were relatively sufficient. The values of SVI ranged from 34 to 96 mL/g and 27 to 74 mL/g, respectively, with average values of 57 mL/g and 42 mL/g, respectively, indicating good sedimentation performance. The 4 th compartment was at the very end of the reactor, where the nutrients were lacking, and the metabolic capacity of the microorganisms was greatly affected. The values of SVI ranged from 47 to 98 mL/g, with an average value of 61 mL/g. Since most of the granular sludge was in the intermediate state of formation, and only a small part had been formed or was in the state of disintegration when the start-up was in the intermediate stage, the values of SVI in each compartment had a large span. The sedimentation performance was generally shown as the 3 rd compartment was the best, the 2 nd compartment was the second, the 4 th compartment was the third best, and the 1 st compartment was the worst.  
Figure 4b shows the SVI of each compartment on the 45 th day of start-up. The position of the 1 st compartment made the sedimentation performance of the activated sludge poor, and the values of SVI ranged from 21 to 57 mL/g, with an average value of 34 mL/g. The 2 nd and the 3 rd compartments were located in the middle of the reactor, where the nutrients were relatively sufficient and it was unnecessary to absorb harmful substances in the influent, so the values of SVI were 18-38 mL/g and 12-36 mL/g, with average values of 26 mL/g and 17 mL/g, respectively. The micronutrients in the 4 th compartment could not meet the normal metabolism of the microorganisms, and the values of SVI ranged from 18 to 64 mL/g, with an average value of 31 mL/g. The conclusion was that the sludge sedimentation performance of each compartment was still the best in the 3 rd compartment, followed by the 2 nd compartment, the 3 rd compartment, and the 1 st compartment when the granular sludge was acclimated and mature. The average value of SVI in each compartment was less than 34 mL/g, and such a low value made the granular sludge have a quite good sedimentation effect.  
Figure 4: SVI of four compartment on (**a**) the 35 th day and (**b**) the 45 th day during start-up.
## 4 Evaluation
### Application Evaluation  
As an overarching method for application evaluation, testing in an industrial context with follow-up questionnaires and interviews was selected. Testing was carried out as part of the case studies by pilot users from industry (cf. Section 2). According to [PERSON][41], it is important to perform evaluation in the intended application context. Therefore, it was carried out without additional support and based on real development data. To enable pilot usage, the workshop demonstration (cf. Section 4.1) and additional information was used (user guide and demonstration video).  
Pilot users were selected based on their background and experience in requirements engineering. To objectify pilot users' assessment and reduce emotional biases, a questionnaire was used for initial evaluation. To enable in-depth feedback and evaluation aspects beyond the questionnaire scope, this was followed by semi-structured interviews (30 to 45 min) with each pilot user. If necessary, complementary research methods are used for evaluation of certain success criteria and explained in the respective section.
## 4 Conclusions  
In this paper, gas seepage tests were carried out to study the gas seepage regularity of different structural bituminous coal and its influence on outburst-coal breaking. The main conclusions were obtained.  
1. Coal's structural difference and anisotropy yielded distinct mechanical properties. Tectonic and soft coal seams exhibit higher porosity and lower compressive resistance than intact hard ones, enhancing gas seepage, thereby increasing outburst propensity.  
2. Both kinds of coal seams exhibited consistent seepage regularity that the raised axial stress, confining pressure and gas kinetic diameter would inhibit seepage. However, raw coal benefited from low-pressure Klinkenberg-enhanced seepage, while tectonic soft coal seams formed under higher geo-stress exhibited more limited gas seepage.  
3. A self-reinforcing cycle mechanism was proposed to explain the cyclic enhancement in outburst initiation and development. In this cycle, mining-induced coal fragmentation accelerated gas seepage, which promoted gas diffusion and enlarged gas expansion energy, resulting in gas pressure gradient elevation, finally intensifying coal fragmentation in turn. At last, the self-reinforcing cycle promoted outburst-coal breaking and outburst initiation, which exhibits stronger in tectonic soft coal seams than intact hard ones.  
The findings in this study will contribute to the in-depth understanding of outburst mechanisms, outburst prevention, and early-warning, further ensuring global energy security.  
Data curation, L.C.; Formal analysis, W.G.; Funding acquisition, J.Z. and G.H.; Investigation, J.Z. and L.C.; Methodology, J.Z., G.H. and J.W.; Resources, J.Z.; Validation, W.G.; Visualization, L.C. and W.G.; Writing--original draft, J.Z.; Writing--review and editing, J.Z. and L.C. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support of the \"Tianchi Talent\" project funded by the Xinjiang Uygur Autonomous Region Introduction Plan, the National Natural Science Foundation of China (Grant Number 51674049), and Xinjiang Uygur Autonomous Region Special Program for Key R&D Tasks (2022B01034-1).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company SINOPEC Geophysical Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Examples of Forward-Inverse Modeling
### Bidirectional Reflectance of Water--Why Is It Important?
These weights and biases are optimized from the training procedure to minimize the error between the neural network derived \(R_{\rm rs}(\lambda_{i},\theta_{0})\) and the actual \(R_{\rm rs}(\lambda_{i},\theta_{0})\) values in the training dataset. \(N_{\rm in}\) is the number of input parameters, which in our neural network equals 10: three geometry angles plus seven wavelengths. The input parameters are denoted by \(p_{k}\), which in this case are the three geometry angles and the angle-dependent remote sensing reflectances, \(R_{\rm rs}(\lambda,\theta_{0},\theta,\Delta\phi)\), at each of the seven wavelengths. As discussed by [PERSON] et al. [95], this neural network approach to convert remote sensing reflectances from actual slant-viewing to nadir-viewing directions was tested using synthetic data as well as field measurements. The results can be summarized as follows (see [95] for details):  
* The generally anisotropic remote sensing reflectance of oceanic water must be corrected in remote sensing applications that make use of the nadir water-leaving radiance (or remote sensing reflectance) to derive ocean color products. * The standard MAG02 correction method [63], based on the open ocean assumption, is unsuitable for turbid waters, such as rivers, lakes, and coastal water. The MAG02 method requires the chlorophyll concentration as an input, which is a drawback in remote sensing applications, because the chlorophyll concentration is generally produced from the corrected remote sensing reflectance. * To meet the need for a correction method that works for water that may be dominated by turbidity or CDOM, [PERSON] et al. [95] developed a neural network method that directly converts the remote sensing reflectance from the slant-viewing to the nadir-viewing direction. * The neural network was trained using remote sensing reflectances at slant and nadir directions generated by a radiative transfer model (AccuRT), in which scattering phase functions for algal and non-algal particles were adopted. Therefore, the remote sensing reflectance implicitly contains information about the shape of the scattering phase function which affects the BRDF. * This method uses spectral remote sensing reflectances as input. Hence, it does not require any prior knowledge of the water constituents or their optical properties. * Tests based on synthetic data show that this method is sound and accurate. Validation using field measurements [96] shows that this neural network method works equally well compared to the standard method [63] for open ocean or chlorophyll-dominated water. For turbid coastal water a significant improvement over the standard method was found, especially for water dominated by sediment particles.
# Detecting and Mitigating Attacks on GPS Devices
## 6 Countermeasures and Their Comparison
### Countermeasures for Spoofing  
Many antenna-based approaches for jamming mitigation can also be applied for GPS spoofing mitigation. Many of these approaches are predicated on determining the DOA of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Reference** & **Technical Approach** & **Jamming Threats Addressed** \\ \hline [PERSON] & [106] & Adaptive Notch Filter (ANF) & CW interference \\ \hline [PERSON] et al. & [107] & ANF combined with neural network & CW interference \\ \hline [PERSON] et al. & [108] & Transversal Finite Impulse Response (FIR) Filter & Chirp jamming \\ \hline [PERSON]. [PERSON] et al. & [68] & Complex Adaptive Notch Filter (CANF) & CW interference \\ \hline \hline \end{tabular}
\end{table}
Table 13: Summary of signal processing-based approaches for GPS jamming mitigation.  
the interfering signal and then creating a null in the receive antenna pattern in the direction of the hostile attack system. Most of these approaches, particularly those that work on the carrier phase, can also be applied to the spoofing threat. Additional papers in the literature presented antenna-based approaches that optimized their detection and antenna nulling specifically to address GPS spoofing by taking advantage of code-based GPS signal structure features. For example, the authors of [91] looked at carrier phase differences in received signals by taking advantage of C/A code symbol alignment properties between the actual and spoofed GPS signals, reporting an attenuation of unwanted signals of at least 60 dB when the SNR of the spoofed signal was high. Most antenna-based methods discussed for GPS spoofing detection also include mitigation approaches focused on creating antenna nulls in the direction of the GPS spoofer. Most signal processing-based approaches to GPS jamming mitigation do not apply to GPS spoofing mitigation. Most papers that propose signal processing-based jamming mitigation approaches primarily address narrowband unwanted signals and cannot remove the wideband spoofed GPS signal.  
The common approach to GPS spoofing mitigation is simple message filtering. GPS spoofing detection methods focus on identifying the presence of the spoofed GPS signal. The assumed mitigation approach is for the system to simply ignore the resulting position once it is detected. This capability is essential since the unmanned system will no longer follow false position estimates and can no longer have its flight path manipulated by the GPS spoofer.
## 2 Materials and Methods
### Sensory Analysis  
The sensory panel consisted of 16 trained panelists in the olfactory experiments and quantitative descriptions of aromas, aged 23-26 years, with a male to female ratio of 1:1. All panelists were from the Beijing Key Laboratory of Flavor Chemistry, Beijing Technology and Business University. Before the sensory evaluation experiment, the panelists were trained for 15 min every day for 3 months to improve the accuracy and reproducibility of the test. Sensory tests were performed in a sensory evaluation laboratory at 20 \(\pm\) 1 \({}^{\circ}\)C. First, the panelists described the aroma profile of peach spirits by evaluating the samples, and a list of aroma attributes was obtained based on the frequency and intensity described by the panelists. According to the determined method of [PERSON] et al. [28], eight sensory attributes were selected, including malty, cooked-apple, fruits, floral, honey, grass, acidic, and alcoholic. Standard solutions of 3-methylbutanan-1-ol (malty), (E)-\(\beta\)-damascenone (cooked-apple), ethyl 3-methylbutanoate (fruity), phenylethyl alcohol (floral, honey-like), phenethyl acetate (honey), hexanal (grass), acidic (acetic acid), and alcohol (alcoholic) were used as references in the experiments, respectively [28; 29]. Panelists evaluated the similarity of the samples using a 7-point scale. The intensity started at 0 (imperceptible) and increased at 0.5 step each time to 3 (strongly perceptible) [29]. The results gained from 16 evaluators were averaged and plotted as a radar diagram.
# Towards Machine Learning for Error Compensation in Additive Manufacturing
## 3 Data-Driven Modelling in Additive Manufacturing
### Artificial Neural Network Framework  
In AM, the thermal nature of materials and processes may cause uncertainties in the temperature gradients during construction phase. As a result, the parts being built undergo irregular deformations that usually leads to dimensional inaccuracies from the modelled to finished parts. An ANN based approach is proposed in [83], to tackle the problem of unwanted thermal deformations in manufactured parts adapting FDM.  
ANN is a computation system loosely inspired on how human brains process information. A feedforward neural network has three types of neurons composed of input neurons, hidden neurons and output neurons. There is only one input and output layer, but there can be multiple hidden layers in between. Every connection of neurons is assigned with weights where weighted outputs from previous nodes are introduced with activation function [84].  
The proposed model is a feedforward ANN which is trained using backpropagation algorithm [85; 86] to obtain the information on deformity. Previously, slice and stereolithography (STL) based compensation approaches are showcased for geometric errors in fabricated parts using the Rapid Prototyping (RP) method [87; 88].  
On the proposed thermal geometry compensation method, finite element model is used to simulate the creation of solid materials in AM process. Two sequential steps are involved, firstly the temperature history together with the thermal gradients across all layers are computed using finite element analysis, next, the overall deformation induced in the part is determined using ANN implementing Lavenberg-Marquardt variant of error backpropagation algorithm for training. Two sample parts were tested using the proposed method showing a significant error reduction of manufactured parts conforming to the original CAD file with a reduction in conformity error by 58.86% to 63.99% using an ANN setup shown in Figure 4.
## 4 Conclusions  
A manager of a construction project faces a fundamental managerial challenge, which consists of managing the course of the accumulated investment cost in such a way that its actual value (actually performed and paid for construction works) is as close as possible (with as little deviation as possible) to the planned value. Therefore, forecasting the actual course of the implementation cost is of great importance in the management of construction projects and enables predictable maintenance of planned investment budgets in a controlled manner and as close to the actual state as possible.  
On the basis of the analysis of the developed knowledge base, containing data from 612 reports of the Bank Investment Supervision on 45 construction projects from 2006 to 2023 with a total value of over PLN 1,300,000,000, the best-fit curve was determined, and the predicted area of the accumulated cost in selected construction projects belonging to different groups/sectors of the construction industry was determined. The determined polynomial functions and graphs of the areas of real cost curves, in the form of nomograms, constitute a reliable graphical representation enabling the application of research results in typologically similar groups/sectors of construction.  
The conclusions from the research, applied methods, and developed modeling are as follows:  
* Research related to the analysis of the cumulative cost curve with the potential to forecast costs and their exceedances was carried out.
* On the basis of the collected reports of the Bank Investment Supervision, a representative set of data was created to conduct research on the development of an original method for forecasting the best match of cost curves and cost area in selected construction projects.
* A model was developed, and the course of planned, actual, and developed cost curves for selected construction projects collected in the developed knowledge base was developed.
* A methodology of cost curve research has been proposed by combining two methods used so far for the control and monitoring of construction projects (the cumulative cost curve and the earned value method) into one original method of forecasting the best fit of the cost curve and the cost area in selected construction projects.
* It has been shown that the shape of the cumulative cost curve within a homogeneous group/sector of construction is similar, but when comparing them between different groups of investment projects, a large diversity is visible.
* A research model was developed and its parameters were given in order to elaborate the best fit of the cost curve based on the course of the planned and actual cost of selected construction projects in the form of a third-degree polynomial function.
* The area of best matching of the course of cost curves to the planning and monitoring of costs in various construction projects has been proposed.
* Developed a model with specific parameters of the 'irregularity alert' system, based on the area of cost curves and the three sigma rule.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 3 Low-Power-Density Rectenna
### Beamforming Techniques  
The beam characteristics of rectennas should be designed according to different application scenarios. Collecting all possible RF energy in the environment is necessary for ambient RF energy harvesting. Therefore, the beam should ideally cover all directions. In contrast, for wireless power transfer targeting dynamic objects, the beam of the rectenna should be steerable to maximize transmission efficiency. Dipole rectennas are widely adopted due to their quasi-full spatial coverage of linearly polarized radiations [92; 93]. However, limited by their relatively low gain, dipole rectennas generally exhibit low rectification efficiency. Figure 17 shows a cylindrical flexible antenna array of five vertical antenna arrays, each consisting of four patch antennas. The designed energy harvester can cover 97% of all possible angles of arrival of the incoming radiation. Under an ambient power density of 1 \(\mu\)W/cm\({}^{2}\), a rectification efficiency of up to 45% was achieved [94]. Beamforming networks are the most commonly used method for achieving beam control. Typical beamforming networks include thenRotman lens [95], parabolic reflector [96], Blass matrix [97], Nolen matrix [98], and Butler matrix [99]. Using image recognition technology and beamforming techniques, a 1 \(\times\) 4 phased array antenna with target tracking capability was designed, as shown in Figure 18[100]. Compared with uniform beam transmission, this system can significantly improve transmission efficiency within a distance of approximately ten wavelengths. The time required from target identification to beam phase adjustment is about 100 ms.  
Figure 17: Photograph of cylindrical flexible rectenna [94].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Operating** & \multirow{2}{*}{
\begin{tabular}{c} **Input** \\ **Power** \\ \end{tabular} } & **No. of** & **Peak** & **Conversion** & **Polarization** \\  & **(GHz)** & & **Elements** & **Gain** & **Efficiency** & **State** \\ \hline
[79] & 33.1–41.6 & N/A & 4 \(\times\) 4 & 23.4 dBic & N/A & CP \\
[82] & 1.85, 2.15, 2.48 & \(-\)30 dBm & 2 \(\times\) 2 \(\times\) 2 & 9, 11, 11 dBi & 7.6\%, 8.6\%, 4.3\% & Dual-LP \\  & 1.84, 2.04, & & & & 49\%, 51\%, & \\
[83] & 2.36, 2.54, & 3 dBm & 1 & 3 dBi * & 52\%, 53\%, & N/A \\  & 3.3, 4.76, 5.8 & & & & 46\%, 45\%, 35\% & \\  & 1.8, 2.1, 2.4, 2.65, & & & 1.59, 3.18, 2.25, & 21.6\%, 26.7\%, & N/A \\  & 3.5 & \(-\)20 dBm & 2 & 2.41, 3.46 dBi & 28.8\%, 19.8\%, 17.9\% & \\
[87] & 24.28 & 18 dBm & 2 \(\times\) 2 & 17.1, 17.0 dBic & 4.9\%, 47.8\% & LHCP, RHCP \\
[89] & 1.7–2.5 & 1 \(\mu\)W/cm\({}^{2}\) & 1 & 5 dBi * & 24.4\% & AP \\
[91] & 2.45 & 23.3 dBm & 1 & 6.4 dBi & 82.2\% & AP \\ \hline \hline \end{tabular}  
* CP: circularly polarized. LP: linearly polarized. LHCP: left-hand circularly polarized. RHCP: right-hand circularly polarized. AP: all-polarized. N/A: not available.
* Average gain over all operating bands.  
\end{table}
Table 3: Performance comparison of different rectenna designs.