esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Identification of Nonlinear Acoustic Damping in Miniature Loudspeakers
## 3 Identification Strategy for Nonlinear Acoustic Damping  
### Nonlinear Parameters  
To identify nonlinear acoustic damping, other parameters in the loop equations must be simultaneously identified. Nonlinearities in the electrical and mechanical domains can be expressed as polynomial expansion functions [13], as follows:  
\[K_{m}(x)=k_{0}+k_{1}x+k_{2}x^{2}+k_{3}x^{3}+k_{4}x^{4} \tag{10}\]  
\[Bl(x)=b_{0}+b_{1}x+b_{2}x^{2}+b_{3}x^{3}+b_{4}x^{4} \tag{11}\]  
\[R_{m}(v)=r_{0}+r_{1}v+r_{2}v^{2}+r_{3}v^{3}+r_{4}v^{4} \tag{12}\]  
Figure 3: ECM model of miniature loudspeakers considering nonlinear acoustic damping.  
From Equations (7) and (9), we can readily determine that the nonlinear acoustic damping, \(R_{a}(v)\), is coupled with the nonlinear mechanical resistance, \(R_{m}(v)\). Therefore, the nonlinear mechanical resistance described by Equation (12) should be measured in advance, which can be realized by measuring the miniature loudspeaker using an electroacoustic test instrument, such as the Klippel R&D System [14], when the front cover is removed.  
According to Equation (3), nonlinear acoustic damping \(R_{a}\) is a function of the absolute value of \(v_{0}\), that is, the air velocity through the orifice. By applying the law of conservation of mass, the air velocity through the orifice can be related to the velocity of the voice coil, \(v_{0}S_{0}=vA\). Thus, the nonlinear acoustic damping \(R_{a}\) and the equivalent mechanical resistance \(R_{m}\) can be expressed as follows:  
\[R_{a}(v)=R_{l}+\rho_{0}\frac{1-C_{D}}{C_{D}}\frac{|v|A}{S_{0}^{2}} \tag{13}\]  
\[R_{m}(v)=R_{a}(v)A^{2}=R_{l}A^{2}+\rho_{0}\frac{1-C_{D}}{C_{D}}\frac{|v|A^{3}} {S_{0}^{2}} \tag{14}\]  
Based on Equation (14), we can use two parameters to describe \(R_{m}(v)\) in a concise form, as follows:  
\[R_{m}(v)=c_{0}+c_{1}|v| \tag{15}\]  
As discussed in the next subsection, all nonlinear parameters in the loop equations should be differentiable. However, an issue arises when Equation (15) is used directly in the identification process because Equation (15) has no derivative with respect to velocity at zero owing to the absolute value function. A differentiable approximation of Equation (15) can be used in the identification process, as follows:  
\[R_{m}(v)=c_{0}+c_{1}\bigg{(}\frac{v}{1+e^{-\delta v}}-\frac{v}{1+e^{\delta v}} \bigg{)} \tag{16}\]  
where \(\delta\) is the coefficient with a positive value that controls the degree of approximation. An example of the comparison between Equations (15) and (16) is shown in Figure 4 with \(\delta=100\), \(c_{0}=0\), and \(c_{1}=1\). Negligible differences are found only near the zero point.
# Statistical Analysis of Acoustic Emission in Uniaxial Compression of Tectonic and Non-Tectonic Coal
## 2 Experiment
### Experimental Facilities  
The experimental system is shown in Figure 2, which consisted of loading equipment and a real-time AE detection device. Loading equipment was SHIMADZU AGI-250 high-precision electric-servo digital experiment machine, equipped with a visual operation and high-speed data acquisition system, which could track the current load, stress and displacement values. In the compression, we adopted the displacement load control method with a loading rate of 0.1 mm/min. The AE acquisition device adopted the Physical Acoustics Company DISP AE system: 132 MB/SEC of data transmission speed; 150 mflops high-performance processing capabilities; minimum noise threshold value of 18 dB; frequency range was from 10 to 2.1 MHz (\(\pm\)1 dB) and 20,000 hits per second high-speed processing speed. The AE sensors were arranged on the coal sample's side surface directly, and acoustically coupled with a sample by a thin layer of Vaseline, and this experiment has been undertaken with the simultaneous use of eight AE sensors. The elastic waves, generated by sample inner failure, were captured by AE sensors and were transferred into electronic signal waves, see Figure 1. According to the recommendation of Physical Acoustics Company, for natural rock samples the Peak Definition Time (PDT) is 35 \(\upmu\)s, the Hit Definition Time (HDT) is 150 \(\upmu\)s, and the Hit Lockout Time (HLT) is 300 \(\upmu\)s. A proper setting of the PDT ensures the correct identification of the signal peak for rise time and peak amplitude measurements.  
Figure 1: SEM observations for (**a**) tectonic coal and (**b**) non-tectonic coal.
# Adversarial Training for Mitigating Insider-Driven XAI-Based Backdoor Attacks
## 3 Proposed Approach
### Insider-Driven Backdoor Attack Using XAI
#### 3.1.1 Trigger Generation
```
Input:\(x^{\prime}\in X\) where \(X^{\prime}\subset X^{\text{Train}}\) Output:\(B\leftarrow\) Poisoned Samples /* Build surrogate model */ \(f^{\prime}(X^{\prime};\theta)\) such that \(f^{\prime}(x^{\prime})\gets y^{\prime}\) S \(\longleftarrow\) SHAP(X') \(y\_target\longleftarrow 0\) \(I_{j}=\frac{1}{n}\sum_{i=1}^{n}\left|\phi_{j}^{(i)}\right|\) ; // get the important features while\(j>0\)do if\(I_{j}>0\)then \(F_{\text{Bj}}\longleftarrow I_{j}\)  end if /* Iterate over the samples to poison */ for(\(x\in X^{\prime}\)) { \(b_{i}\longleftarrow F_{\text{mal}}\cup F_{\text{B}}\) \(y_{\text{b}}\longleftarrow y\_target\) }
```  
**Algorithm 1**Insider-driven backdoor attack using XAI  
Consider a subset of training data \(X^{\text{Train}}\), and use the surrogate model to train the data. Apply the SHAP interpretation and obtain the Shapley values as a 2D matrix represented as \(S\). Each row corresponds to a prediction produced by the model, and each column corresponds to a feature the model uses. The SHAP value for each feature indicates how much that feature contributes to the prediction outcome for this row. The size of matrix \(S\) is \(M\times N\), where \(M\) is the number of samples for inference, and \(N\) is the number of features. A positive SHAP value positively impacts prediction, causing the model to predict 1 (malicious class). A negative SHAP value indicates a negative influence, which causes the model to forecast 0 (non-malicious class). The features are ranked based on their influence on the model prediction. Consider the mean of the absolute values of the importance of features with non-zero values. Consider a targeted attack on non-malicious samples. Create the backdoor trigger using important features that help classify non-malicious instances. The algorithm then identifies the most influential features, creating the backdoor trigger set \(F_{B_{j}}\). These features are used for malicious trigger generation. Finally, the attacker iterates over the selected training samples \(x\in X^{\prime}\), manipulating these malicious features \(F_{B_{j}}\) into each sample. Each poisoned sample \(b_{i}\) thus becomes a combination of original features \(F_{\text{mal}}\) and injected backdoor features \(F_{B_{j}}\). The attacker assigns these manipulated samples to a predefined target label \(y_{\text{target}}\), typically a specific misclassification intended by the attacker. Once the backdoor is generated, the adversary needs to verify the efficacy of the proposed backdoor attack. This is achieved using a surrogate model that mimics the original model used for the prediction. The purpose and details of the surrogate model are provided in the section below.
# Climate-Quality Calibration for Low Earth-Orbit Microwave Radiometry +
## 5 Summary and Conclusions  
Monitoring of long-term atmospheric trends, especially of temperature, requires radiometric stability and linearity, whereas radiometric resolution and spatial resolution become less important than for weather observations. We have described how radiometer design could be optimized for the former purpose, and it differs in some respects from the design of existing weather-satellite instruments. By referencing to radio-occultation measurements, radiometer stability would be traceable to frequency standards. By means of the limb-view RO comparisons, all channels, including those with lower-troposphere weighting functions at nadir, would acquire this stability. A suitable selection of orbit would permit derivation of a climatology of the diurnal cycle of temperature, with benefits for the interpretation of trends from weather-satellite radiometers over the past four decades. Further evaluation of radiometer configurations, target design, scan pattern, orbit, and error analysis would be warranted.  
Conceptualization, P.W.R. and W.J.B.; funding acquisition, W.J.B. and R.V.L.; writing--original draft preparation, P.W.R.; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded in part by NASA 80 GSFC19T0038.  
The authors thank [PERSON], [PERSON] and [PERSON] for helpful discussions. Approved for public release. Distribution is unlimited. This material is based upon work supported by the National Aeronautics and Space Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Aeronautics and Space Administration.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMSU & Advanced Microwave Sounding Unit \\ ATMS & Advanced Technology Microwave Sounder \\ GNSS & Global Navigation Satellite System \\ LO & local oscillator \\ MSU & Microwave Sounding Unit \\ RO & radio occultation \\ SSMIS & Special Sensor Microwave Imager/Sounder \\ \(T_{A}\) & antenna temperature \\ \end{tabular}
# Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads
## 4 Results: Optimal Power Flow Solution Considering Heuristic and Analytical Algorithms
### Fifty Seven-Node IEEE System  
#### 4.2.1 Fifty Seven-Node IEEE System Description  
The IEEE 57-bus test system is used to evaluate the optimization problem of this study. Based on details given in [4] for system buses and branches, the data of the system have been structured in the MATPOWER [6] data format. Branch thermal limits were defined based on reference values given in [5]. A summary of the characteristics of the test system are: seven generators, 42 loads, 63 line/cables, 15 transformers step/wise, two transformers fixed taps and three shunt compensation binary On/Off. Figure 5 below shows the 57-node system on which solar, wind, run-of-the-river generators, controllable loads and batteries were mounted.
## 3 Results
### Germination Inhibition of Conidia by PEI-AuNP@Van  
A germination inhibition assay was performed at different time intervals using voriconazole and PEI-AuNP@Van against the conidia of _A. fumigatus_ and _A. flavus_. As mentioned earlier, this study demonstrated that the germination of the conidia was attenuated by PEI-AuNP@Van; variable dynamics against both fungal strains were demonstrated (Figures 4, S1 and S2). A germination inhibition study was conducted for 48 h at 4, 8, 12, 24, and 48 h intervals against the conidia of _A. fumigatus_ and _A. flavus_. The results indicated that PEI-AuNP@Van attenuated the germination of _A. fumigatus_ by 93%. In contrast, voriconazole inhibited growth by 56% after 48 h of treatment at its respective MIC values (as shown in Figures 4 and S1). Similarly, _A. flavus_ conidial germination was inhibited by PEI-AuNP@Van by up to 35%; voriconazole inhibited _A. flavus_ conidial germination by only 42% (as shown in Figures 4 and S2). Thus, PEI-AuNP@Van showed considerable potential as a germination attenuator against _A. fumigatus_ compared with voriconazole.  
Figure 3: Antifungal evaluation plates (**a**) against _A. fumigatus_ and (**b**) _A. flavus_ (NC = PEI-AuNPs control; NP = PEI-AuNP@Van; PC = positive control). (**c**) MIC values of non-functionalized AuNPs (PEI-AuNPs), vancomycin-functionalized AuNPs (PEI-AuNP@Van), positive control (voriconazole), vancomycin, and negative control (DW) against _A. fumigatus_ and _A. flavus_.
## 3 Results
### Use Case 1: Use Case Coverage  
As mentioned above, one key challenge is to find already implemented solutions for integrating data-driven methods in product development. This is due to the fact that no standardised documentation of applied use cases can be found in the industry or research. AI4 PD can link the domains and allows a description of relevant use cases by linking the available data sources. Relevant data are primarily the following:  
* A description of the product development process;
* Used tools and toolboxes as well as the applied data-driven method;
* Required input and corresponding output data.  
The power of the use case coverage is shown in the example of the self-learning assistance system (SLASSY) [40]. SLASSY supports the design engineer during the development of sheet-bulk metal parts with added functional elements that are manufactured by sheet-bulk metal forming. A key aspect of this system is the self-learning component, which allows knowledge to be automatically extracted from existing data in the form of meta-models. This extraction is performed by a knowledge discovery in database (KDD) process based on simulation and experimental results. The process terminates in a regression-based prediction model. The system itself is divided into a synthesis and an analysis module. Since they aim at different goals, only the analysis module is further considered in the contribution. Figure 7 shows the formal representation of SLASSY.  
Figure 7: Formal setup of the SLASSY use case. (core concepts: yellow diamonds, relationships: blue arrows, individual instances of a concept: purple diamonds, properties: green squares)The description of the use case encompasses the properties _Name_, _DOI_ and _Company_ with their defined values. SLASSY is assigned to the replication of interrelationship _TaskCluster_. The explicit _SubProcess_ is modelled by a SubProcess_[ID] instance and not named since several tasks or _SubProcesses_ are possible for the application of SLASSY. Figure 7 shows that the _Data_ instances connect the _Tool_, _Process_ and _UseCase_ instances. Since two different types of _Data_ entities exist, the input and output streams are modelled separately. The Data_[ID1] represents the input set, formatted as a comma-separated values (CSV) file. In this file, the test data results are stored. Consequently, Data_[ID2] represents the output data in JavaScript object notation (JSON)-format. The result predicts a manufacturing possibility for the given CAD file. Finally, SLASSY uses the _Tool_ RapidMinerRegressionModel, which is part of the RapidMiner _Toolbox_ and implements the _DataDrivenMethod_ polynomial regression.  
Therefore, all the necessary information is available so that the ontology can answer the competency question given in use case 1 in Section 2.3.
###### Abstract  
In recent years, due to the rise of the Internet of Things (IoT), various sensors have come to be in great demand for IoT devices. Analog-to-digital converters (ADCs) act as an important part of receivers in sensors. To improve the uptime of IoT devices, a bridged-switch energy-efficient switching scheme for successive approximation register (SAR) ADCs with a low-complexity capacitor drive circuit is proposed. The technique of top-plate sampling and closed-loop charge recycling is used in the proposed switching scheme so that neither the first nor the second comparison consumes switching energy. The third comparison uses bridge switches to connect the subarray to the main array, effectively reducing switching's energy consumption. Only the least significant bit (LSB) is dependent on the accuracy of V\({}_{\textrm{cm}}\); thus, the last comparison consumes little switching energy. The proposed switching scheme achieves an average switching energy value of 47.5 CV\({}_{\textrm{ref}}^{2}\), which is 96.52% lower than that of the conventional capacitor switching scheme and reduces the area by 75%. The other major circuit modules employed are bootstrapped switches, a fully dynamic comparator, and dynamic SAR logic. The proposed ADC was simulated under the conditions of 180 nm CMOS process and 1 MS/s, resulting in a 9.8-bit effective number of bits (ENOB), a signal-to-noise and distortion ratio (SNDR) of 60.76 dB, a spurious-free dynamic range (SFDR) of 69.85 dB, a power consumption of 14.7 uW, and a figure of merit (FoM) of 16.55 fJ/conv.-step.  
IoT; SAR ADC; switching scheme; energy efficient; low complexity
## 4 Simulation and Experiment Results
### Simulation Results  
The simulations are simulated by Simulink of MATLAB 2016b. Solver is ode3, fundamental sample time is 0.00001 s, reference speed \(\omega^{*}\) = 1000 rpm, the load torque change from 0.1 N/m to  
Figure 3: Diagram of feedforward-based PMSM system.  
0.3 N/m at \(T\) = 1.5 s, the proportional gain and integral gain for current loop are 100 and 1000, respectively. \(\alpha_{1}\) = 10,000, \(\alpha_{2}\) = 50,000, \(\alpha_{1q}\) = 3000, \(\alpha_{2q}\) = 100,000, \(\varepsilon\) = 0.5.  
The estimated resulted of ESO for speed loop and \(q\)-axis current loop are shown in Figure 4, respectively. It can be seen from Figure 4 that the estimated value of ESO is similar to the theoretical value of the disturbance, which indicates that ESO can accurately estimate the disturbance of the system. This provides a basis for compensating the disturbances. The speed curves of single-ESO method and double-ESO method are shown in Figure 5, respectively. It can be seen that the system using double-ESO method has batter disturbance rejection ability. In Figure 6, disturbance compensation improves the dynamic response of \(q\)-axis current loop.
# Large-Scale Experimental Static Testing on 50-Year-Old Prestressed Concrete Bridge Girders
## 2 Case Study
### Material Properties  
The archive research on the test laboratory provided the mechanical properties of strands and concrete. The test certificates released in the 1970s by the Testing Laboratory of the Politecnico di Torino show average yielding and ultimate strength values for prestressing steel of 1486 and 1722 MPa, respectively. The compressive strength recorded at the 28-day of the precast I-beams averaged 50.3 MPa over 4 samples, whereas the compressive strength of the cast-in situ slab averaged 40.5 MPa over 7 samples.  
To integrate the material properties reported in the available technical documentation and to provide more accurate values, non-destructive tests were performed and reported in [30]. In Table 2, the results of some destructive tests performed by Material and Structures Testing Laboratory MastrLab of Politecnico di Torino and the Testing Lab for Materials, Buildings and Civil Structures of Politecnico di Milano after the large-scale experiments are reported. The following tests were performed:  
* Concrete of girders and slab: 15 compression tests on cylindric samples and 11 indirect tensile tests;
* Prestressing steel: 8 tensile stress tests.  
The extracted samples of the concrete girders refer to the B3-P47/46, B8-P47/46 and B4-P47/46 beams; the samples of the slab were retrieved from the B3-P47/46 and B9-P47/46 beams. The samples of prestressing steel were taken from the B4-P47/46 and B9-P47/46 beams. Table 2 contains a list of the obtained results in terms of mean value and standard deviation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Load Test** & **Beam Code** & **Loading Phases** & **Concrete Cross-Section** \\ \hline
1 & B3-P47/46 & 2 & Composite \\
2 & B8-P47/46 & 2 & Composite \\
3 & B4-P47/46 & 1 & Composite \\
4 & B9-P47/46 & 2 & 1-beam \\ \hline \end{tabular}
\end{table}
Table 1: Load test protocol.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Location** & **Material** & **Property** & **Test** & **No.** & **Unit** & **Mean Value** & **Standard Deviation** \\ \hline Slab & Concrete & Compressive strength & Compression & 5 & MPa & 21.9 & 4.7 \\ \hline Slab & Concrete & Tensile strength & Indirect tensile test & 2 & MPa & 2.4 & 0.9 \\ \hline Girder & Concrete & Compressive strength & Compression & 10 & MPa & 33.6 & 6.7 \\ \hline Girder & Concrete & Tensile strength & Indirect tensile test & 9 & MPa & 3.4 & 0.5 \\ \hline Girder & Prestressing steel & Yield strength & Tensile & MPa & 1521.5 & 68.0 \\ Girder & Prestressing steel & Ultimate tensile strength & Tensile & 8 & MPa & 1763.4 & 60.1 \\ \hline Girder & Prestressing steel & Elongation at maximum load & Tensile & \% & 3.4 & 1.1 \\ \hline \end{tabular}
\end{table}
Table 2: Material properties of the destructive tests.
## 2 Negative-Stiffness Device and Its Mechanical Model
### Parameter Impact Analysis
#### 2.3.3 Influence of Preload \(\Delta l\) of Belleville Spring on Negative Stiffness  
In accordance with Formula (7), the relationship between negative stiffness, \(K_{NSD}\), and displacement, \(x\), under different spring preload values, \(\Delta l\), is shown in Figure 11.  
It can be seen from Figure 11 that, the greater the initial compression of the Belleville spring, the greater the negative stiffness provided by the device. However, too much initial compression leads to the reduction of the action range of negative stiffness. It shows that the preloading shrinkage, \(\Delta l\), has a significant impact on the negative stiffness of the device.
# Sustainable Approach of Generating Water and Energy:
## 2 System Description  
A photovoltaic/thermal hybrid (PVT) powered system is a reliable source of energy where heat and electricity can be generated simultaneously. The proposed hybrid system shown in Figure 1 comprises primarily of PVT, a direct contact membrane desalination unit (DCMD) for water purification, a small absorption chiller, and a typical residential unit. The heat generated from the PVT (H\({}_{1}\)) is used to heat the working fluid (i.e., Brackish water). In this case, the brackish water is heated in PVT before it enters the DCMD unit. The storage tank is used to collect the heated water and auxiliary heating is utilized if the temperature in the tank drops below 70 \({}^{\circ}\)C to ensure a constant energy source for the DCMD. The heat rejected from DCMD (\(c_{2}\)) is used as a regenerative source for a small-scale absorption chiller. Cold feed water obtained from the absorption chiller (\(c_{1}\)) is pumped to DCMD.  
The electric energy generated by PVT (\(E_{1}\)) is used to cover the partial load of a residential building. If the electric energy produced by the PVT exceeds house electric demand, the extra energy will be sold to the local grid (\(E_{3}\)). Power imported from the grid (\(E_{4}\)) is used when the PVT is not capable of meeting the house's load.