esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparison Between Sentinel-1 and GPS Datasets  
In order to perform a reliability check and cross-validate the results obtained with the proposed methodology applied to MT-InSAR Sentinel-1 data, we analyzed the available GPS dataset for the study area by considering the results in terms of vertical ground deformation measured in the same time interval (i.e., 2016-2021) with the two different technologies. The results obtained by GPS data and Sentinel-1 data were correlated by a regression analysis. Because the two datasets differ greatly in their numbers of data (n = 21 for GPS and n = 15,900 for Sentinel-1), we selected the 21 data within Sentinel-1 that are located in the nearest position (within a radius of 58 m) of GPS stations for comparison. In this way, we obtained a dataset of 21 control points located near the GPS stations (Figure 8) and characterized by both GPS and Sentinel-1 measurements of vertical ground deformation during the same time interval.  
The results of the correlation analysis for measurements in these control points indicate a strong correlation with very similar data. The linear regression of the couple of values (GPS and Sentinel-1 measurements) for each point (Figure 17) gave very high values of correlation, with an R\({}^{2}\) value of 0.9926.  
Figure 16: Details of the main area of anomaly between Monte di Procida and Baia using the best fitting fourth-grade polynomial curve.
# Laser Technology in Photonic Applications for Space
###### Abstract  
The registered history of laser technologies for space application starts with the first laser echoes reflected off the Moon in 1962. Since then, photonic technologies have become very prominent in most technical development. Their presence has also dramatically increased in space applications thanks to the many advantages they present over traditional equivalent devices, such as the immunity against electromagnetic interference, as well as their efficiency and low power consumption. Lasers are one of the key components in most of those applications. In this review, we present an overview of the main technologies involving lasers that are currently deployed in space, before reviewing the requirements for lasers to be reliable in that environment before discussing the advantages and drawbacks of replacing standard technologies by newly developed photonic laser-based devices.  
Keywords:photonics in space; space lasers; laser communication; LIDAR; quantum +
Footnote †: journal:
## 1 Introduction  
There have been many vibration energy harvesters proposed in the literature, either as only a single transducer or as a transducer with an additional structure to increase the operation frequency band, as reviewed in [1,2]. A highly flexible P(VDF-TrFE) (poly(vinylidenefluoride-co-trifluoroethylen)) film-based energy harvesting device on a PDMS (polydimethylsiloxane) substrate was introduced in [3]. The fabricated device powered a set of 27 light-emitting diodes (LEDs) after several minutes of charging. Some structures of transducers, such as the M-shaped transducer in [4], the zigzag-shaped in [5], the V-shaped in [6], and the T-shaped in [7], include empty gaps of a considerable volume compared with the volume of the active material in the ribs of the transducer. An L-shaped energy harvester was investigated in [8], where it was presented that the frequency range over which energy can be harvested from such structures was much larger than using a cantilever beam, but the power density was much lower. The considerable volume of the broadband structure lowers the power density of the energy harvesters. In [9], the performance of the array energy harvester was investigated in comparison with a single reference harvester of the same volume as the entire array. It was presented that the advantage of using the array over the reference harvester was very limited to a small additional bandwidth to the detriment of a large drop in the power output. In [10], a parametric study on piezoelectric energy harvesters considering a constant volume was introduced. In order to optimize the power output, the geometrical parameters (length, width, thickness, and tip mass) were recommended to be maximized within the available volume. In [11], the dynamic responses of a nonlinear vibration energy harvester (a flexible beam moving like an inverted pendulum between amplitude limiters) had multiple solutions, which correspond to various levels of power output. There have been a variety of broadband structures, which mostly require additional volume, cost, and a complicated structure compared with the single transducer. This highlights the necessity of finding the criteria to distinguish between the proposed structures. Possible metrics to appraise the proposed vibration energy harvesters will be reviewed in the following two sub-sections under two main categories: the efficiency and the figure of merit.
## 2 Basic Theories
### Acoustic Cavity Modal Theory  
The acoustic cavity mode represents a specific instance of the acoustic mode, typically focusing on the acoustic wave phenomena within enclosed or partially enclosed cavities, and the split-stream rushing exhaust muffler under discussion in this paper falls into this category. Consequently, the acoustic wave resonance within this muffler will be investigated via an acoustic cavity mode analysis.  
The mode shape characteristics of acoustic cavity modes are characterized by the spatial distribution of sound pressure. Resonance amplification of the intracavity sound pressure will occur when the external excitation frequency approaches or coincides with the eigenfrequency of a specific acoustic cavity mode [17]. Particularly within the lower-order mode frequency spectrum, acoustic cavity modes and structural modes exhibit a higher susceptibility to coupling, consequently influencing both the acoustic performance and structural properties of the muffler.  
Within acoustic cavity modes, the sound pressure in a uniform, source-free medium is governed by the following wave equation:  
\[\
abla^{2}p-\frac{1}{c^{2}}p=0 \tag{8}\]  
where \(c\) denotes the speed of sound and \(\
abla^{2}\) is the Laplacian operator, signifying the second-order spatial derivative of a function, and its formula is given by  
\[\
abla^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial y ^{2}}+\frac{\partial^{2}}{\partial z^{2}} \tag{9}\]By incorporating the acoustic boundary conditions and discretizing Equation (4), the acoustic finite element equation for a non-viscous fluid medium is derived as follows:  
\[[M^{p}]\{\breve{P}\}+[C^{p}]\{\breve{P}\}+[K^{p}]\{P\}=F \tag{10}\]  
In which \([M^{p}]\) denotes the acoustic mass matrix, \([K^{p}]\) denotes the acoustic stiffness matrix, \([C^{p}]\) denotes the acoustic damping matrix, \(\{P\}\) denotes the sound pressure vector at the element nodes, and \(F\) denotes the generalized load vector. If there is no sound wave attenuation in the sound field, then \([C^{p}]\) = 0. Furthermore, when computing the eigenvalues and eigenvectors for acoustic cavity modes, the generalized load vector should be zero. Consequently [18], Equation (10) simplifies to  
\[[M^{p}]\{\breve{P}\}+[K^{p}]\{P\}=0 \tag{11}\]  
The characteristic equation is then given by  
\[\left\{[K^{p}]-\omega^{2}[M^{p}]\right\}\{P\}=0 \tag{12}\]  
where \(\omega\) represents the acoustic cavity mode frequency. The linearly independent eigenvectors \(p_{i}\)(\(i\) = 1, 2, 3...) and eigenvalues \(\omega_{i}\)(\(i\) = 1, 2, 3...), which correspond to the mode shapes and modal frequencies of the acoustic cavity modes, can be obtained from the preceding equation.
## 2 Materials and Methods
### Measurement of \(A\)-Light Curves  
From May to November 2012 and from April to October 2013, the \(A\)-light curves of the seedlings were measured monthly using an infrared gas analyzer system (LI-6400, LI-COR Inc, Lincoln, Nebraska, US). Two to four seedlings from each chamber were randomly selected to be measured. The seedlings were moved from the OTCs into an experimental room and the measurements were made using three LI-6400s in the room to avoid the influences of weather conditions at the measurement time. The measurements were conducted on one leaf from each leaf type per seedling for the 1 st flush leaves of _F. crenata_, the 1 st and 2 nd flush leaves of _Q. serrata_ and _Q. mongolica_ var. _crispula_ and the early and late leaves of _B. plathythla_ var. _japonica_ seedlings. Because the leaf emergence of _B. plathythla_ var. _japonica_ is heterophyllous [31] and the late leaves emerge successively, the measurements were conducted for the 4 th and 5 th, 9 th and 10 th, 15 th and 16 th (only in 2013) and 18-20 th late leaves, as counted from the base of the 1 st or 2 nd branch from the top of the seedlings. The last measurement day of each growing season was the time when the leaves were almost yellowed at the end of the growing season. When we did not find target leaf for the measurement of \(A\)-light curve due to leaf abscission by the exposure to O\({}_{3}\), we did not measure it. The \(A\)-light curves were determined at an atmospheric CO\({}_{2}\) concentration of 390 \(\upmu\)mol CO\({}_{2}\) mol\({}^{-1}\) and a relative air humidity of approximately 60% inside the leaf chamber. As much as possible, the target air temperature inside the leaf chamber was the 10-year average of the monthly mean of the daily maximum air temperature inside the OTCs used in the present study. The actual air temperatures inside the leaf chamber differed among the month and ranged from 17.5 \({}^{\circ}\)C to 26.0 \({}^{\circ}\)C in 2012 and from 19.0 \({}^{\circ}\)C to 28.9 \({}^{\circ}\)C in 2013. For detailed information, please refer to the Table S5. To obtain the response of net photosynthetic rate to PPFD, after acclimation of the leaves to light-saturated conditions at a PPFD of 1500 \(\pm\) 1 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), PPFD was gradually reduced to zero and the net photosynthetic rates were recorded under PPFDs of 1500, 1,000, 500, 250, 100, 50 and 0 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\). To obtain the \(A\)-light curve, a non-rectangular hyperbolic function was fitted to the response of the net photosynthetic rate to PPFD. Although photosynthetic parameters other than the \(A\)-light curve could offer a better indication of the overall damage of O\({}_{3}\) on photosynthesis and its mechanisms, we did not measure the parameters, because we focused on the effects of O\({}_{3}\) on the cumulative photosynthetic CO\({}_{2}\) uptake rather than the mechanisms underlying the O\({}_{3}\)-induced reduction in net photosynthesis.
###### Abstract  
In this paper the authors discuss the realization of a Long Range Wide Area Network (LoRaWAN) network infrastructure to be employed for monitoring activities within the marine environment. In particular, transmission ranges as well as the assessment of parameters like Signal to Noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) are analyzed in the specific context of an aquaculture industrial plant, setting up a transmission channel from an offshore monitoring structure provided with a LoRaWAN transmitter, to an ashore receiving device composed of two LoRaWAN Gateways. A theoretical analysis about the feasibility of the transmission is provided. The performances of the system are then measured with different network parameters (in particular the Spreading Factor--SF) as well as with two different heights for the transmitting antenna. Test results prove that efficient data transmission can be achieved at a distance of 8.33 km even using worst case network settings: this suggests the effectiveness of the system even in harsher environmental conditions, thus entailing a lower quality of the transmission channel, or for larger transmission ranges.  
LPWAN; LoRaWAN; IoT; marine monitoring; offshore data transmission +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Sensory Evaluations
#### 2.2.2 Sensory Analyses  
Sensory evaluations were performed according to DIN EN ISO 13299:2016. Gin samples were presented to the panel in 140 mL transparent, lidded glass jars (J. WECK GmbH u. Co. KG, Wehr-Offingen, Germany), each containing 15 \(\pm\) 1 mL of the respective sample. The orthonasal evaluations were performed in a sensory room at 21 \({}^{\circ}\)C, and each sample was labelled with a random three-digit code for anonymization purposes. The aroma qualities of each sample were determined in an initial sensory session, and a subsequent consensus of the descriptors was made by the trained panel, with the attributes chosen as follows (with corresponding odorant references in parentheses): _citrus-like_ (citral), _orange-like_ ((R)+-limonene), _fruity_ (ethyl-2-methylbutanoate), _soapy/coriander-like_ (decanal), _anise-like_ (anise), _mality_ (3-methylbutanal), _ethanolic/pungent_ (ethanol), _eucalyptus-like_ (1,8-cineole), _juniper-like_ (juniper), _cola-like_ (cola soft drink) and _peach-like_ (\(\gamma\)-dodeclactone). The same descriptors were used for evaluating both gins.  
The samples were comparatively evaluated by rating the intensity of these individual odor qualities. Additionally, hedonic ratings of each sample on a scale from 0 (strong disliking) via 5 (neutral) to 10 (strong liking) were made; untrained assessors were included for the hedonic ratings. Analysis of variances (ANOVA) was performed on the mean intensity ratings across the panel to examine the significance in differences between the sensory profiles (significance level, \(p<0.05\)).
## 3 Grid-Based Event Detection  
Detecting events at each grid cluster alone in a grid-based wireless sensor network might be easy for a relatively large event region (e.g., \(m\) = 1). In that case, the majority of the sensor nodes in at least one grid are likely to report a 1, and thus voting schemes, such as the majority voting, can easily satisfy the requirements on detection accuracy and false alarm rate. For a relatively small event region (e.g., \(m\geq 2\)), however, the number of sensor nodes reporting a 1 might be considerably small compared to the number of sensor nodes in a grid. Hence poor event detection performance might be unavoidable unless some measures are taken to significantly lower the threshold without increasing the false alarm rate.  
As fault probability increases, the number of nodes reporting a 1 in the case of no-event also increases. Hence a voting scheme may have difficulty in distinguishing between faults and events using a threshold.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(n\) & \(m\) = 1 & 2 & 3 & 4 \\ \hline
10 & 31.4 & 7.9 & 3.5 & 2.0 \\
15 & 47.1 & 11.85 & 5.25 & 3.0 \\
20 & 62.8 & 15.8 & 7.0 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(n\cdot\frac{A_{ER}}{A_{G}}\) for various values of \(m\) and \(n\).  
The negative impact of faults can be greatly lessened by effectively reducing the fault probability. To realize it most of the erroneous readings due to transient faults are first corrected by employing a filter. In addition, nodes with permanent faults or reporting incorrectly for some extended periods of time are identified and isolated. Each cluster head maintains confidence levels of its member nodes indicating their records in reporting correctly. Sensor nodes with a permanent fault lose their confidence levels gradually, and they eventually reach the lower bound to be isolated from the rest. This fault management reduces the number of incorrect reports, allowing us to lower the threshold for event detection without sacrificing performance even for a relatively small event region. Two performance metrics, detection accuracy (DA) and false alarm rate (FAR), will be used in evaluating the effectiveness of the proposed scheme. DA is defined to be the ratio of the number of times that events are detected to the total number of event occurrences. FAR is defined as the ratio between the number of grids reporting an event, in the case of no event, and the total number of grids.
## 1 Introduction
[27] adopted the Bayesian approach coupled with the Gaussian process model to quantify the uncertainty parameter in the stochastic problem. In the research of [PERSON] et al. [28], a novel identification method for a steady-state heat transfer problem based on fuzzy uncertainty was presented. To the present, probabilistic-based parameter identification methods have been researched widely. However, a serious problem needs to be noticed, that is, the premise of applying these methods is that the probability distribution function is known, which is difficult to obtain accurately for the practical engineering systems. In recent years, the non-probabilistic method based on interval theory has been proposed to describe the uncertainty [29; 30; 31; 32], which avoids the calculation of the probability density function and only needs the lower bound (LB) and upper bound (UB) of the sample interval. [PERSON] et al. [33] presented a novel parameter identification method for structure system with the technology of sensitivity analysis of the Kriging meta-model. To avoid the effects of interval expansion, [PERSON] et al. [34] put forward an improved parameter identification method by modifying the response surface model. [PERSON] et al. [35; 36] studied polynomial response surface and Radial Basis Function neural networks model, respectively, and proposed a novel interval parameter identification strategy. Besides, [PERSON] et al. [37] constructed an interval identification method for heat transfer problems based on the nested-loop strategy and chaos expansion. In the research of [PERSON] et al. [38], an augmented Fourier polynomial surrogate model was proposed and applied to the parameter identification of structural systems with novel optimization strategy. Although the method based on interval theory has received extensive attention and research, some issues remain. For instance, the computational cost of the system's original implicit-based model is immeasurable in practical engineering systems which require a simple and efficient surrogate model. In addition, most of the identification strategies adopted in the above study are too complicated and cannot be effectively applied to the interval problem well. Additionally, for the evaluation of measurement intervals, the traditional extremum method may cause these intervals biased. Hence, firstly, this paper presents a novel modelling methodology based on the Voronoi tessellation to simulate the geometry concisely and factually for low porosity metal-foam materials. Additionally, for the uncertainty of parameters in the heat transfer problem, an improved interval theory-based parameter identification strategy is designed, which adopts the novel augmented trigonometric series polynomial model (ATP) and an unbiased interval estimation method. Eventually, an engineering heat transfer system is provided to verify the feasibility of the proposed method.
# Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics
## 1 Introduction  
### Background  
Deep learning-based segmentation models have significantly enhanced a variety of medical procedures, including brain tumor detection [1; 2], breast cancer screening [3; 4], organ segmentation [5; 6], and skin lesion analysis [2; 7; 8]. Furthermore, these models contribute to the synchronized monitoring of medical devices and patients, exemplified by the detection of artificial ventilation usage [9]. U-Net based models, in particular, have shown exemplary performance in the domain of computed tomography (CT) segmentation [5; 6; 10]. These advanced models, however, are not immune to adversarial attacks [11; 12; 13], a vulnerability that extends to automatic CT segmentation models as well [14; 15; 16].
# Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
## 3 System Identification  
### Frequency Domain System Identification by PSO  
In this paper, the system identification results were based on sine chirp approach. The outputs (red line) of velocity and position loops corresponds to chirp sine signals were obtained from the linear scale shown in Figure 5, and input (blue line) was generated by TNCopt. To enhance the efficiency of identification, a two-stage approach for frequency domain was proposed; at first the velocity loop was considered to obtain motor parameters (\(J_{m},B_{m}\)), and then the position loop identification was done for lead-screw and table parameters (\(K,M_{t},C_{t}\)). The procedure can be summarized as follows.  
Figure 4: The flow chart of the particle swarm optimization (PSO) algorithm.  
Figure 5: Cont.  
Figure 5: Sine chirp input signals and the corresponding outputs for system identification. (**a**) \(X\)-axis (**b**) \(Y\)-axis (**c**) \(Z\)-axis (**d**) \(A\)-axis (**e**) \(C\)-axis.  
**Procedure 1: System Identification**  
(Step 1) Data Acquisition:  
Obtain corresponding data (velocity and positon results) from chirp sine response.  
(Step 2) Data Transformation:  
Convert data to get the corresponding frequency responses [gain versus frequency, (\(A_{i}\), \(\omega_{i}\))]  
(Step 3) Estimation of \(J_{m}\) and \(B_{m}\):  
Velocity Loop Identification via PSO:  
\[\min_{J_{m},B_{m}}E=\sum_{i}\left[\left|G_{p}(j\omega_{i})\right| -\left|\hat{G}_{p}(j\omega_{i})\right|\right]^{2}\] \[\text{s.t. }J_{m},B_{m}>0\]  
where \(\left|\hat{G}_{p}(j\omega_{i})\right|\) denotes the corresponding estimation of velocity gain at \(\omega_{i}\).  
(Step 4) Estimation of \(K\), \(M_{t}\), and \(C_{t}\):  
Position Loop Identification via PSO:  
\[\min_{K,M_{t},C_{t}}E=\sum_{i}\left(\left|G_{p}(j\omega_{i}) \right|-\left|\hat{G}_{p}(j\omega_{i})\right|\right)^{2}\] \[\text{s.t. }K,M_{t},C_{t}>0\]  
where \(\left|\hat{G}_{p}(j\omega_{i})\right|\) denotes the corresponding estimation of position gain at \(\omega_{i}\).  
As above, Steps 3 and 4 used the PSO technique for finding system parameters (\(J_{m}\), \(B_{m}\)) and (\(M_{t}\), \(C_{t}\), \(K\)). The PSO was adopted to minimize the square error of estimated frequency response function (FRF). Subsequently, application of the velocity loop resulted in a position loop with unknown parameters (\(M_{t}\), \(C_{t}\), \(K\)). The same optimization process by PSO could find the parameters (\(M_{t}\), \(C_{t}\), \(K\)). In order to improve the efficiency of the calculation, the stop criterion was an iteration with a number greater than 4000 and with a particle number of 35, according our experience. Small values of the iteration number and population size resulted in worse optimization results, large values had the problem of complexity and computation effort. The overall identification process is shown in the Figure 6.  
Figure 6: Two-stage approach for system identification.