esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
###### Abstract  
Acoustic Emission is a non-invasive technique with potential applications in Structural Health Monitoring (SHM), particularly for assessing historic masonry structures. However, its use in this field is complex due to the heterogeneous nature of masonry, where variations in density, mortar joints, and internal discontinuities influence signal propagation, leading to attenuation and distortion that complicate damage detection and localization. Nonetheless, AE can offer qualitative insights into damage initiation and progression, serving as a complementary approach to traditional monitoring methods. This study explores the feasibility of AE through an in-field test conducted on the historic Santa Maria delle Grazie complex, assessing its ability to capture qualitative indicators of structural behaviour. By integrating AE results with data from conventional monitoring instruments, a comprehensive interpretation of the load test outcomes was developed despite the challenges posed by the irregularities of ancient masonry. The findings contribute to the ongoing evaluation of AE as a diagnostic tool and highlight its potential role in heritage conservation strategies.  
acoustic emissions; instrumentation; in situ tests; load tests; masonry structure; Structural Health Monitoring; architectural heritage 2025  
12 May 2025  
Revised: 12 June 2025  
Accepted: 20 June 2025  
Published: 24 June 2025  
**Citation:** [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application. _Appl. Sci._**2025**, 15, 7111. [[https://doi.org/10.3390/app1513711](https://doi.org/10.3390/app1513711)]([https://doi.org/10.3390/app1513711](https://doi.org/10.3390/app1513711))  
**Copyright:** 02025 by the authors.  
Lousence MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Multiscale Feature-Based Infrared Ship Detection
## 4 Discussion  
In order to quantitatively evaluate the performance of each algorithm, two evaluation indicators, true positive rate (TPR) and false positive rate (FPR), are selected for comparison. TPR and FPR are calculated as follows:  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}},\ \ \text{FPR}=\frac{\text{FP}}{ \text{FP}+\text{TN}}, \tag{28}\]  
where TP and FN represent the number of the detected and undetected pixels of the real targets, with FP, the number of misjudged pixels as the target, and TN, the number of correctly judged pixels as the target.  
In order to quantitatively evaluate the detection performance of each algorithm, a reference image containing the ground truth labels for real targets and background pixels is manually annotated. The test results of each method are compared with the reference image and calculated by Equation (28). Table 3 presents the comparison results. The definition of the TPR and FPR indicates that the larger the calculated TPR value and the smaller the FPR value, the better the performance of the detection algorithm. That is, it has a low false-alarm rate while having high detection accuracy. The comparison results in Table 3 and Figure 12 demonstrate that the top-hat performs well in scenarios 4-6, with a relatively flat background, but shows a higher false-alarm rate in the more complex scenarios 1-3. The calculation results of TPR indexes obtained by K-means and FCM are similar, both being close to 1, indicating that both methods can completely extract targets, but there still exists the problem of a high false-alarm rate, especially for the KCM. The mean-shift has a small FPR across all the selected scenarios, but its TPR indicator is also small, especially in scenarios 4 and 5, where targets are missed, in which the TPR values are close to 0. The GC algorithm shows unstable TPR and FPR values; although the detection rate is high in some scenarios, some targets are still missed in other scenarios, and the TPR value is close to or equal to 0, which proves that there exists instability because manual selection is inevitable for the GC. In contrast, the proposed method consistently achieves both ideal TPR and FPR across different scenarios, indicating that it can accurately detect ships in both simple and complex sea surface scenarios, unaffected by various disturbances. In summary, our method has significant advantages over the other five methods in terms of both detection accuracy and low false-alarm rate and can complete the task of accurate infrared detection of ships under a complex sea surface background. Moreover, the calculation of the selected feature quantities is simple; thus, it can achieve highly efficient detection.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **Top-Hat** & **K-Means** & **FCM** & **Mean-Shift** & **GC** & **Our Method** \\ \hline \multirow{2}{*}{S1} & TPR & 0.4992 & 0.9558 & **0.9973** & 0.9225 & 0.9010 & 0.9817 \\  & FPR & 0.0215 & 0.0177 & 0.1376 & 0.0138 & **0.0022** & 0.0038 \\ \hline \multirow{2}{*}{S2}
## 3 Results and Discussion
### Parameter Optimization and Verification Experiment  
The multi-objective parameter optimization module in Design-Expert software was employed to optimize the above indicators, yielding the optimal parameter combination: the treatment voltage is 18.05 kV, the treatment time is 20.21 s, and the gas flow rate is 12.79 L/min. The resulting potato germination rate was 31.76%, and the weight loss rate was 2.09%. For the convenience of operation, the parameters of the validation experiment were determined as a treatment voltage of 18 kV, treatment time of 20 s, and gas flow rate of 13 L/min. The resultant potato germination rate was 31.26%, and the weight loss rate was 2.29%. Five repeated trials were conducted under this experimental parameter combination, and the average value of the experimental validation results was calculated. The experimental results showed that the potato germination rate was 31.42%, and the weight loss rate was 2.15%. The experimental validation results were consistent with the optimization results, with the relative errors between the experimental and optimized values being less than 5%. The small relative error suggests that the model was highly accurate.  
Figure 4: The interaction of various factors on the weight loss rate of potatoes. Effect of treatment voltage and gas flow rate on weight loss rate of potatoes (**a**); effect of treatment voltage and treatment time on weight loss rate of potatoes (**b**); effect of gas flow rate and treatment time on weight loss rate of potatoes (**c**).
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
###### Abstract  
In experiments, flame surface density (FSD), defined as flame area per volume, is usually approximated by its two-dimensional (2D) value, as flame length per area. However, this approximation may underestimate the flame wrinkle due to the presence of a fluctuating component outside the measurement plane. Obtaining a three-dimensional (3D) flame surface density (FSD) from experiments is challenging, but it can be estimated from low-dimensional measurements under certain assumptions. Models used to estimate 3D FSD can be significantly affected by ambient pressure, as high pressure can cause a substantial decrease in small flame front scales. In this study, a CH\({}_{4}\)/air pre-mixed turbulent flame is stabilized on a Bunsen burner and measured using the OH-PLIF technique at pressures up to 1.0 MPa. The flame front is extracted with an in-house auto adaptive threshold binarization code. Different models estimating 3D FSD with the corresponding assumptions are summarized from the definition of FSD. The reliability of the assumptions under different pressures is investigated and analyzed. The models are compared through analyzing the assumptions, and are tested in terms of global fuel consumption. The pressure's effect on the reliability of the models could provide an essential improvement in the context of modeling turbulent combustion.  
turbulent premixed flame; flame surface density; 2D laser diagnostic; global fuel consumption rate +
Footnote †: journal: Physics Letters B  
1
Footnote 1: _Applied_  
2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Controller Switching Mechanism for Resilient Wireless Sensor-Actuator Networks
## 4 Controller Switching Mechanism
### LWB Network Delay  
In WSANs, network delays significantly affect the performance and stability of the control system. A network delay in the scheduling technique considering LWB controller switching was analyzed, and delay analysis in the discrete domain was conducted. As shown in Figure 4, the delay mainly comprised the delay from the sensor to the controller, \(\tau_{sc}\), and that from the controller to the actuator, \(\tau_{ca}\).  
\(\tau_{sc}\) and \(\tau_{ca}\) comprise a queuing delay, a frame delay, and a propagation delay, and values that vary depend on the protocol's design of the protocol. Additionally, a computation delay occurs in controller \(\tau_{c}\); typically, such delays are very small compared with communication delays, and can thus be neglected. Thus, the total delay is \(\tau_{ca}+\tau_{sc}+\tau_{c}\). If a controller fails or packet dropout occurs, the time delay is \(\tau_{ca}+\tau_{sc}+\tau_{c}+d\tau_{s}\), where \(d\)  
Figure 4: Network delay in WSANs.  
Figure 3: Process of proposed controller switching mechanism (**a**) before and (**b**) after controller switching.  
is the number of dropouts. The time delay should be less than the MADB to ensure the stability of the control system. Delays in LWB networks are bounded and constant because data transmission is achieved within a predefined time interval. \(\tau_{ca}\) and \(\tau_{sc}\) comprise a given amount of time required to process the data received from the controller and the plant and to generate the data to be transmitted. In the proposed design, data are processed at \(T_{gap}\).  
Figure 5 illustrates details of the controller switching mechanism. As shown in Figure 5, \(on\_slot\_pre()\) and \(on\_slot\_post()\) callback functions were executed between the two data slots, \(T_{gap}\) for preparing the payload and process the received payload. The repetition of transmission occur at \(T_{data}\). \(\tau_{ca}\) and \(\tau_{sc}\) is \(T_{gap}+T_{data}\) in LWB networks. Therefore, the total delay in the LWB becomes \(2(T_{data}+T_{gap})+d*T_{s}\). For instance, if the controller does not function, a switch to the backup controller occurs in the current round, and the total delay becomes \(3(T_{data}+T_{gap})\).
# Practical, Low-Cost Fault Injection Attacks on Personal Smart Devices
## 4 Discussion
### Toward Malicious FRUs  
[PERSON] et al. [13] showed how malicious field replaceable units (FRUs) on a smart phone can attack software running on the phone's main CPU. We believe that our work opens a path to a new class of attacks, in which fault injection devices are placed inside replaceable units of smart devices. In such a setting, they gain the capacity to inject faults to the main processor, compromising the security of the system while the victim is unaware of the attack. In our work, we show that such a low-cost fault injection device can be assembled and is able to disrupt the regular execution of a powerful processor. The gap between our EM fault injector and a malicious FRU can be described in two parts.  
The first part is reducing the size of the device to allow placing it inside of a small device, such as a smart phone. The circuit described in this work can be simple and can be printed to a very small printed circuit board (PCB). Small hardware has been demonstrated to be capable of creating high voltage spikes in devices such as the USB Killer, which is a device designed to collect power from the USB port and send high voltage pulses on the data lines, damaging the host PC [25]. In regard to the placement of the PCB inside an existing FRU, a malicious aftermarket device may contain a lot of free real estate inside for the attacker to use. For example, an attacker compromising a charging case can take advantage of the large surface to place larger capacitors. Another example of a malicious external device is a charging surface, which already emits EM energy and with various changes to its circuitry may act as our EM fault injector. Batteries are also large and can be made with malicious components inside. Establishing a covert channel between the malicious FRU and the user-space program allows the attacker to carry out attacks remotely on demand, resulting in a stealth attack.  
The second part is using precise injection of faults to attack a specific target, both temporally and spatially. To address the temporal challenge, as shown in [20], a malicious actor who is free to execute code inside its unprivileged environment can simply ask the privileged service to perform an operation over and over again. In [20], such a method was used to perform DFA on the AES encryption algorithm and extract the encryption key. The spatial challenge may not be difficult to address in the FRU setting, since a component such as a replacement phone screen is always fitted precisely in the same place related to the phone's main CPU. The gap between our work and final malicious FRU product amounts ultimately to technical engineering work.  
While performing a physical attack such as FI remotely is powerful, the attacker has to be able to overcome the engineering challenges but also to adjust the malicious FRU to fit specific smart devices; as the malicious FRU is powerful, its primary limitation is being tailored to a specific target device.
## 2 Materials and Methods
### Antioxidant Activity on Caco2 Cells
#### 2.6.3 RNA Extraction and Real-Time-PCR  
After treatment with the extracts from SD5, the expression of _TNF-\(\alpha\)_ and _IL-1\(\beta\)_ from Caco2 cells was investigated through RT-PCR. When ca. 80% confluence was reached, Caco2 cells were harvested with trypsin/EDTA, seeded, at a density of 1 \(\times\) 10\({}^{6}\) cells per well, into 12-well (Becton Dickinson France S.A., Meylan Cedex, France) plates and incubated at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\), for 24 h. Cells in DMEM GLUTAMAX medium and DMEM GLUTAMAX with lipopolysaccharide (LPS) (10 \(\mu\)g/mL) were used as the controls. Freeze-dried methanolic extracts from SD5 at a concentration of 10 \(\mu\)g/mL were added to 80% confluent Caco2 cells with the same LPS concentration (10 \(\mu\)g/mL) and incubated at 37 \({}^{\circ}\)C for 24 h. For quantitative real-time PCR (RT-PCR), total RNA from Caco2 cells was extracted using Tri Reagent (Sigma Aldrich), as described by [PERSON] and [PERSON] [24]. The cDNA was synthesized from 2 \(\mu\)g RNA template in a 20 \(\mu\)L reaction volume, using the PrimeScript RT-PCR kit (Takara, Japan).  
The cDNA was amplified and detected by the Stratagene Mx3000P Real-Time PCR System (Agilent Technologies Italia S.P.A., Milan, Italy). The amplification of cDNA was conducted using the following Taqman gene expression assays: TNF-\(\alpha\) H80174128_m1 (tumor necrosis factor alpha), IL-1\(\beta\) H801555410_m1 (interleukin-1-beta), and Hs999999 m1 (human glyceraldehyde-3 phosphate dehydrogenase, GAPDH). GAPDH was used as a housekeeping gene. PCR amplifications were carried out in a 20 \(\mu\)L of total volume. The mixture of reaction contained 10 \(\mu\)L of 2\(\times\) Premix Ex Tao (Takara, Kusatsu, Japan), 1 \(\mu\)L of 20\(\times\) TaqMan gene expression assay, 0.4 \(\mu\)L of RoX Reference Dye II (Takara, Japan), 4.6 \(\mu\)L of water, and 4 \(\mu\)L of DNA. PCR conditions were the following: 95 \({}^{\circ}\)C for 30 s followed by 40 cycles of 95 \({}^{\circ}\)C for 5 s and 60 \({}^{\circ}\)C for 20 s. PCR reactions were performed in duplicate using an MX3000P PCR machine (Stratagene, La Jolla, CA, USA). Analyses were carried out in triplicate. The average value of target gene was normalized using _GAPDH_ gene and the relative quantification of the levels of gene expression was determined by comparing the \(\Delta\) cycle threshold (\(\Delta\)Ct) value [25]. Results were expressed as percent ratio to cells only treated with LPS.
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## Appendix B Summary of Weighted Fusion Method  
The final visibility estimate A is obtained by the Equation (A1):  
\[A=\sum_{m=1}^{n}v_{m}\times\omega_{m}\] (A1)  
where \(n\) (\(n=5\)) represented the number of subregions, \(v_{m}\) is the estimated visibility of the \(m\)th subregion and \(\omega_{m}\) is the visibility fusion weight of the \(m\)th subregion. It represents the correlation between the estimated visibility value of the \(m\)th subregion and the final visibility estimate.  
The fusion weights of the \(m\)th subregion is given by Equation (A2).  
\[\omega_{m}=\frac{\frac{1}{\sigma_{m}}}{\sum_{1}^{n}\sigma_{m}}\] (A2)  
where \(\sigma_{m}=\delta_{m}+\beta_{m}\) represents the predicted variance of \(m\)th subregions which is the sum of the distribution variance \(\delta_{m}\) and the fitted variance \(\beta_{m}\).  
\(\delta_{m}\) is used to assess the uncertainty of the joint distribution of the data. The uncertainty of the training and test set data could be calculated by the covariance matrix \(K\), which was given by Equations (A3) and (A4).  
\[\delta_{m}=K_{(i,i)}-K_{(Z_{m},i)}^{T}K_{(Z_{m},Z_{m})}^{-1}K_{(Z_{m},i)}\] (A3)  
\[Z_{m}=\left\{\left\{\lambda_{1},y_{1}\right\},\cdots\left\{\lambda_{p},y_{p} \right\},\cdots\left\{\lambda_{N},y_{N}\right\}\right\}\] (A4)  
where \(i\) Set of the feature vector of the \(m\)th subregion of the test image and the corresponding visibility estimated  
\(Z_{m}\) Set of eigenvectors (\(\lambda_{i}\)) of the \(m\)th subregion and the corresponding true values of visibility (\(y_{p}\)),  
\(K_{(i,i)}\) Autocovariance.  
\(K_{(Z_{m},i)}\) Covariance matrix of \(Z_{m}\) and \(i\).  
Further, the covariance matrix \(K_{(Z_{m},i)}\) could be calculated from Equation (A5).  
\[K_{(Z_{m},i)_{p}}=\left[\begin{array}{ccc}k_{11}&\cdots&k_{1p}\\ \vdots&\vdots&\vdots\\ k_{p1}&\cdots&k_{pp}\end{array}\right]k_{uv}=Cov(h_{u},h_{v}),\ u,\ v=1,2, \cdots,p\] (A5)  
where \(p\) Total No. of samples in sets \(Z_{m}\) and \(i\).  
\(h_{u}u^{\text{th}}\) data sample in the set \(P\)  
\(h_{v}v^{\text{th}}\) data sample in the set \(P\).  
\(Cov(h_{u},h_{v})\) Covariance of \(h_{u}\) and \(h_{v}\)  
The fitted variance \(\beta_{m}\) of mth subregion is given by (A6).  
\[\beta_{m}=\frac{2}{\left(C_{m}\right)^{2}}+\frac{\left(\varepsilon_{m}\right) ^{2}(\varepsilon_{m}C_{m}+3)}{3(\varepsilon_{m}C_{m}+1)}\] (A6)  
where \(C_{m}\) Penalty term (e.g., \(C_{m}=100\))  
\(\varepsilon_{m}\) Limit error after training support vector regression  
Finally, \(\sigma^{m}\) can be determined by (A7).  
\[\sigma_{m}=\delta_{m}+\beta_{m}\sigma_{m}=K_{(i,i)}-K_{(Z_{m},i)}^{T}K_{(Z_{m},Z_{m})}^{-1}K_{(Z_{m},i)}+\frac{2}{\left(C_{m}\right)^{2}}+\frac{\left( \varepsilon_{m}\right)^{2}(\varepsilon_{m}C_{m}+3)}{3(\varepsilon_{m}C_{m}+ 1)}\] (A7)The predicted variance of each subregion was considered as the weights for model fusion, multiplied separately with the visibility estimates of each subregion. The obtained weighted visibility values were then summed to obtain the final visibility estimated value.
# Sustainability-Based Analysis of Conventional to High-Speed Machining of Al 6061-T6 Alloy
## 5 Analysis of Experimental Data: High-Speed Machining (HSM)
### Confirmatory Experiments  
The cutting parameters identified in the main effect plots (Figures 8 and 10) were used to perform confirmatory experiments (results presented in Table 15). A very low SCE value (0.33 J/mm\({}^{3}\)) was observed at \(=2000\) m/min, \(f~{}=~{}0.4\) mm/rev, and \(d~{}=~{}4\) mm. Such low values of SCE were also reported in authors' earlier work on orthogonal machining [11; 30]. Owing to the high significance of feed for SCE and Ra, a conflicting setting of cutting parameters was observed, with the best value of Ra and worst value of SCE being achieved at exactly the same level of cutting parameters.
## 2 Results and Discussion
### Mineral Element Content
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment Time**} & **Se Spray** & **Cu** & **Zn** & **Mn** & **Fe** & **K** & **Ca** & **Mg** \\ \cline{3-8}  & **mg L\({}^{-1}\)** & \multicolumn{5}{c}{**mg kg\({}^{-1}\)**} & \multicolumn{5}{c}{**mg kg\({}^{-1}\)**} & \multicolumn{5}{c}{**mg kg\({}^{-1}\)**} & \multicolumn{5}{c}{**mg kg\({}^{-1}\)**} \\ \hline \multirow{3}{*}{Flowering} & 0 & 14.3 a & 67.1 a & 95.0 a & 550 a & 1320 a & 628 a & 1074 a \\  & 1.0 & 15.4 a & 67.0 a & 94.3 a & 580 a & 402 a & 594 a & 1054 a \\  & 1.5 & 9.8 b & 44.3 c & 68.9 a & 320 b & 1355 a & 516 b & 851 b \\ \hline \multicolumn{8}{c}{Immature green phase} & 0 & 14.5 a & 67.4 a & 94.9 a & 556 a & 1327 a & 630 a & 1072 a \\  & 1.0 & 8.9 b & 39.8 c & 55.2 a & 392 b & 1469 a & 462 b & 785 b \\  & 1.5 & 10.5 b & 53.8 c & 84.1 a & 366 b & 1508 a & 603 a & 908 b \\ \hline \multicolumn{8}{c}{Variance analysis} \\ \multicolumn{8}{c}{Treatment time (A)} & \multicolumn{2}{c}{**} \\ \multicolumn{8}{c}{Se dosage (B)} & \multicolumn{2}{c}{**} \\ \multicolumn{8}{c}{Interaction A \(\times\) B} & \multicolumn{2}{c}{**} \\ \hline \hline \end{tabular}  
* Significance is as follows: ns, not significant;
* significant at 1%; **
* significant at 0.1%. Different letters in each column correspond to significantly different values for \(p<0.05\) according to the LSD (least significant difference) test.  
\end{table} 
Table 3: Macro and microelements concentration (mg kg\({}^{-1}\)) in tomato fruit of the second truss treated with sodium selenate (Na\({}_{2}\)SeO\({}_{4}\)) at different concentrations of Se at FL and fruit IG stages.
## 4 Results and Discussion
### Discharge Calibration  
As described in the method section, a regression between instantaneous discharge data from the ADCP transects and the vertically averaged velocities by fixed ADCPs (the index-velocity) is required to calculate the time series of discharge. The correlation between the two variables is quite significant, as shown by the linear regressions presented in Figure 5. The coefficient of determination is close to unity: R\({}^{2}\) is equal to 0.990 for the TRP and 0.988 for SN, therefore, we can assume the index-velocity as a reliable proxy of discharge for both the investigated sections. Furthermore, calibration values cover the typical range of discharges, including those measured during storm surges. As an example, in Figure 6, an excerpt of the discharge time series is reported, showing the second highest water level that occurred during the storm surge of 12 th November 2019. Maximum discharges reached 8687 m\({}^{3}\) s\({}^{-1}\) for the SN and 3672 m\({}^{3}\) s\({}^{-1}\) for the TRP during flood tide excursion.  
Figure 5: Linear regressions between the vertically averaged velocities by fixed ADCPs (the index-velocity) and instantaneous discharge data from the ADCP transects for the (**a**) Treporti (TRP) and (**b**) San Nicolo (SN) stations. Dotted lines are the best-fit regression.  
Figure 6: Time series of discharge calculated at TRP and SN channel sections, showing the period of 10–20 November 2019, which includes the second record storm surge of 12 November 2019.