esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 2 Materials and Methods
### Volatile Compound Analyses  
Volatile compounds were analyzed using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), following a modified protocol [21]. Briefly, 1.0 g of sample and the internal standard (1,2-dichlorobenzene, 100 ng/\(\mu\)L, 4 \(\mu\)L) were placed into a 20 mL headspace vial. After equilibration at 100 \({}^{\circ}\)C for 5 min, volatile compounds were extracted using a PDMS/DVB/CAR-coated fiber at 100 \({}^{\circ}\)C for 15 min, followed by thermal desorption at 250 \({}^{\circ}\)C for 5 min. The GC-MS system (Agilent 7000C, Agilent Technologies, Santa Clara, CA, USA) equipped with a DB-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 um, GL Sciences Inc., Tokyo, Japan) utilized helium (1.0 mL/min) in splitless injection mode. The oven temperature was programmed as follows: held at 40 \({}^{\circ}\)C (3.5 min), increased to 100 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, then to 180 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min, and finally to 280 \({}^{\circ}\)C at 25 \({}^{\circ}\)C/min, maintained for 5 min. Electron ionization (EI) was set at 230 \({}^{\circ}\)C, with mass scans ranging from 35 to 550 \(m/z\). Compound identification was based on mass spectral matching with the National Institute of Standards and Technology Library 14 (NIST 14) (similarity \(\geq\) 70%), retention index (RI) comparison with literature values, and the mass-to-charge (\(m/z\)) ratios of characteristic fragment ions.
## 2 Literature Review
### Industry 4.0  
Since its introduction at the Hannover Fair 2011, the phrase \"Industry 4.0\" (I4.0) has gained significant popularity both in academia and in business [26; 29; 53]. However, the absence of a precise definition has made it a fuzzy catchphrase, complicating communication and impeding the development and application of I4.0 solutions [9; 29]. The concept of I4.0 is still highly theoretical, as there is a lack of empirical data, resulting in a vast number of definitions in the literature with differences in semantics and content. This ambiguity in the definition makes it harder for practitioners to understand and align with research in the area, leading to the empirical testing of an imprecise concept [21; 27; 28; 54]. Etymologically, the term I4.0 is a reference to the fourth industrial revolution. Precoded by the first industrial revolution, which was characterized by the mechanization of production through water power and steam engines, the second saw the introduction of mass production and electricity, and the third revolution introduced automation and the introduction of computers and information technology [55]. The increasing processing power of hardware has led to the fourth industrial revolution and a rise in research on I4.0, digitization, digitalization, and digital transformation [27; 28]. I4.0, a transformational concept, defines a future-oriented vision for the production industry. Here, the focus lies on the comprehensive use of modern digital technologies to develop and realize intelligent and networked production facilities through their powerful capabilities [9; 29]. The vision is to create a network of real-time data so that networked systems enable optimized, sometimes autonomously controlled, and dynamic production [9; 22; 46]. It is common to use terms such as digitization, digitalization, or digital transformation synonymously to describe the process that ultimately leads to the introduction of I4.0 [28; 56; 57; 58]. To provide clarity, [PERSON] et al. [28] have defined a distinction between these terms, where digitization refers to the conversion from analog to digital, digitalization involves the use of digital data and technology to optimize processes, and digital transformation involves the creation of new business opportunities using digital data and technology [27; 28]. Digitalization consequently signifies the development of industrial manufacturing towards networked production systems [59]. It creates a system where people, machines, and products interact to increase efficiency and transparency [21; 22; 26; 27; 31; 53; 60]. This is achieved through the use of digital data and the application of modern information technologies with the aim of accomplishing the vision of I4.0. These technologies, known as I4.0 technologies, here include the Internet of Things (IoT) for data collection, cyber-physical systems (CPSs) for the networking and aggregation of the data of all systems, and the ability to process and analyze data efficiently [26; 61; 62].
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 5 Simulation Verification
### Simulation Verification of the Electric Field in the IPT System  
As shown in Figure 9, the radii of the two coils are both \(a=100\) mm, the distance between them is \(h=100\) mm, and they both have \(N\) turns. The coordinates of points \(B_{1}\), \(B_{2}\) and \(B_{3}\) are (0, 0, \(h/3\)), (0, 0, 5\(h/9\)), and (0, 0, 2\(h/3\)), respectively. The circuit topology is shown in Figure 2, and in the circuit, \(R_{L}=5\)\(\Omega\) and the system resonant frequency is \(f_{0}=500\) kHz when the effective value of the current \(I_{P}\) in the primary coil is 1 A. The obtained moduli of the electric field, changing with \(\rho\) at \(\rho_{1}\), \(\rho_{2}\) and \(\rho_{3}\), are given in Figure 10a,b.  
Figure 8: Electric-field intensity at different positions. (**a**) is the electric field curve when a = 100 mm, \(I\) = 1 A and \(f=500\) kHz, (**b**) is the electric field curve when a = 80 mm, \(I\) = 1 A and \(f=500\) kHz, (**c**) is the electric field curve when a = 100 mm, \(I\) = 1.5 A and \(f\) = 500 kHz, (**d**) is the electric field curve when a = 100 mm, \(I\) = 1 A and \(f\) = 800 kHz.  
Figure 7: The three points are in \(\rho\) axis.  
The symbols MAT and COM have the same meanings as those in Figure 8. The maximum deviation between the results obtained with these two methods is 4.3% under the condition that the number of turns of both coils is 8 and that the test point on \(\rho_{3}\) is the closest point to the secondary coil. When the number of coils turns increases, the total height of the coil becomes larger compared with the results when \(N=4\), and the current distribution in the coil becomes much more complex, where the relative error turns out to be larger when the test point is nearer to the coil. However, it is still an acceptable relative error. Hence, Figure 10 verifies that the formula \(E=E_{P}+E_{S}\) can be used for the calculation of total electric field between coupled coils in an IPT system. Similarly, for the magnetic field strength, \(H=H_{P}+H_{S}\) can also be verified. In the next section, an IPT experimental system is set up to further validate the analytical solution.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 5 Simulation Results and Performance Evaluation
### Simulation Results
#### 5.2.2 Delay  
Delay of proposed and existing schemes is shown in Figure 10 and it mainly depends upon underwater channel and distance from the source to destination. As expected, delay of LMPC in both scenarios is less as compared to FLMPC-One and FLMPC-Two, respectively, because of one hop information which leads to high number of active nodes as shown in Figure 9. As the number of active nodes increases, the probability of finding \(PFN\) in less time also increases. Figure 10 shows delay of all schemes in scenario one is higher than scenario two. The difference in sensor nodes density of both scenarios is one of the reasons. Scenario one is 70% less dense as compared to scenario two.  
Due to single path routing, the delay is very less in the both scenarios of DBR. The classic depth based algorithm only considers distance while making forwarding decisions. The selection of always shortest path irrespective of energy consumption results in minimal delay. It is evident from the Figure 10, that as number of layers increases, the number of active nodes also increases resulting in further decrease in the end-to-end delay. At layer 6, the minimal delay is 0.51 and 0.53 s in scenario one and scenario two, respectively.  
In scenario one, the delay of FLMPC-One is almost 0.2 s higher than LMPC because of processing time for potential node selection at every node. Meanwhile, FLMPC-Two's delay is almost 0.6 s higher than LMPC due to less number of active nodes and three hops neighbor calculation. Less number of active nodes results in less number of possible paths to advance data packet reliably towards surface which increases processing delay.  
In scenario two, delay of FLMPC-One is almost 0.3 s higher and FLMPC-Two is 0.5 s higher as compared to LMPC. The reason to locate \(PFN\) using two hops or three hops information is to avoid energy holes. When a data packet reaches in void region, there is no way out and the current forwarder drops it which needs to be retransmitted. Retransmission increases energy consumption and delay.  
In existing and proposed schemes, multiple copies of a data packet reach at the destination. So, there are more than one delays for one data packet. In our proposed schemes, we consider the worst scenario and count the highest delay among them. While calculating delay, we are taking into consideration transmission delay, propagation delay and processing delay of \(PFN\) at each node. In proposed schemes, if there are more than one, two or three hops neighbors exist then the one farthest  
Figure 10: End-to-end Delay.  
among them from current forwarder is selected. The farthest node selection affects delay because of high propagation time.
## 2 Background
### Interacting with Real Objects in AR  
AR systems need to focus on providing realistic interactions with real objects such as toys, dolls, and robots for providing a successful user experience [14; 15; 16; 17; 18]. To enable virtual objects to interact with real objects, AR systems often use a virtual proxy corresponding to the real object. Since virtual proxies should have similar characteristics to real objects (e.g., appearance, location in the real world), commercial apps usually model real objects in advance [11; 12; 13; 18; 19; 20]. Then, to accurately place the virtual proxy in the 3D space, AR apps place a marker for reference [17; 18]. These markers help the AR Apps deploying proxies and coordinate (i.e., overlay) them with real objects [21]. After deployment, these proxies are rendered transparently in AR views to prevent users from being aware of proxies. Thus, virtual humans interact with these invisible virtual proxies, but users perceive that the virtual human is interacting with real objects [6].  
Such an illusion by physical-virtual interactions can affect users positively [6; 7], but modeling real objects and importing them on the system are not trivial tasks [16; 18]. This is because commercial systems often add new objects or new interactions to keep users interested. The addition of new real objects requires their modeling, but each time it is costly and labor-intensive. Furthermore, if a real object is deformed: shape and/or size (e.g., a moving animal, fallen doll, user's hand) at runtime, the system may either lost track or provide an incorrect interaction [22; 23; 24]. Incorrect interactions may reflect poorly on the user experience [5; 25; 26]. In the case of research on virtual human, [PERSON] et al. [6] reported that the incorrect interactions negatively affects the social/co-presence with an augmented virtual human in the shared space.  
To solve these limitations, several AR studies [27; 28; 29; 30] reconstruct real objects and space at runtime by using advanced depth cameras with other spatial sensors. However, these systems are still bulky and these tasks should be completed using only a single device. Single-camera-based approaches can be widely used in our daily lives because they can ensure the usability of the system. Nevertheless, these approaches often require multi-view scanning [27] or user interventions (e.g., look around, select) for the segmentation of target objects from an image [31; 32], making it challenging to support interactions in real-time. Therefore, creating the virtual proxies at runtime is still challenging for usability in the AR system. Our proposed method does not need any spatial sensors, multi-view scanning, or user intervention while creating a virtual proxy.
## 4 Experiments
### Experimental Results
#### 4.4.5 Model Comparision Result
In comparison to traditional Transformer models, the RPM-ETC model introduces a Self-temporal Compression Mechanism to compress time series data, significantly improving the model's prediction speed and efficiency. Additionally, the model incorporates a feature embedding layer that maps discrete data into dense latent vectors, reducing the difficulty of learning and improving generalization. To address the potential loss of sequence information caused by increasing network depth, the model employs an Enhanced Positional Encoding mechanism. Furthermore, the partial autocorrelation function is used to evaluate the correlation between different features, enhancing the model's ability to recognize and understand the temporal dependencies among features. These improvements enable the RPM-ETC model to outperform other Transformer-based models in fault detection tasks. In summary, the RPM-ETC model's high accuracy, processing efficiency, and moderate model complexity position it as the optimal choice for elevator fault prediction tasks.
## 4 Results: Monitoring and Slope Stability
### Slope Stability with Measured Data  
The parametric study was carried out to evaluate the change in the factor of safety as a function of slope angle and groundwater table in Section 3.3. In this section, the slope stability analysis for the man-made slope was carried out applying the measured data, i.e., pore pressure, groundwater table, air and ground temperature, to evaluate a change of stability with time. The average air temperature  
Figure 13: Slope stability analysis with measured data from test slope at \(\mathcal{O}\)ysand site, Norway.  
for 3 days was applied from November to March in Figure 13. Note that the effect of precipitation was not applied in this study.  
The full thermal model (TEMP/W) was applied to simulate ground temperature distribution along with the depth, and the saturated/unsaturated model (SEEP/W) was applied to simulate water flow into the soils, as shown in Figure 14. The average air temperature for 3 days was applied to the surface of the ground because the ground temperature was not changed immediately as the air temperature changes. The boundary at the slope surface was modeled with temperature, which is supposed to be transmitted to the ground corresponding to the measured temperature. Coupled seepage analysis was carried out to model groundwater table and pore water pressure, which are matching to the measured pore pressure. The input parameters used for the slope stability analyses have been verified by measured data. Those parameters will be revised by the continuous field monitoring and the data from instruments for the whole year.  
Consequently, the factor of safety of slope stability was calculated to a range of 2.24 to 2.61 from November 2019 to March 2020, considering the measured pore water pressure, groundwater table, air, and ground temperature.  
In a short period, the factor of safety does not change dramatically depending on the temperature changes in the air because the ground temperature was slowly affected by temperature transmission. But it is necessary to observe the slope behaviors, i.e., pore water pressure, ground temperature, inclination, soil suction, surface movement, over the year by the installed monitoring system. Those measured data can update soil parameters and provide much of the information necessary to monitor slope behavior for mitigation and remediation.
## 5 Comparative Analysis  
### Stability Test of the Displacement Data and Evaluation of Model Performance  
#### 5.1.1 Stability Test of the Mointored Displacement Data  
In order to investigate the deformation pattern of the time-series displacement data within and outside the deformation area of the Huanglianshu landslide after local sliding, we conducted a stability test on all monitoring points using the augmented Dickey Fuller (ADF) test [41]. The ADF test determines whether there is a unit root in the time series. If the time series is stationary, there is no unit root. Therefore, the ADF test assumes the existence of a unit root; and if the obtained test statistic \(p\)-value is less than significance levels 10, 5, or 1%, it is equivalent to having 90, 95, or 99% certainty against the original hypothesis, respectively. In this paper, we adopted 5% as the significance level, i.e., the situation of the \(p\)-value > 5% indicated that the time-series was nonstationary.  
As are listed in Table 5, the displacement data at monitoring points FJ003, FJ004, FJ005, FJ006, and FJ009 were stationary, and the displacement data at monitoring points FJ010 and FJ011 within the deformation area were not stationary. The above results indicate that the displacement data within the deformation area had more complex nonlinear relationships.
# Nondestructive Evaluation of Aluminium Foam Panels Subjected to Impact Loading
## 4 Conclusions  
The impact behaviours of six different types of aluminium foam panels were investigated by means of LVI tests at different impact energies, ranging from 35 to 400 J. The following findings can be outlined:  
* The response of different types of AFS panels with skins was similar at the same impact energy, while the AF panels without skins had a different impact behaviour with lower values of \(F_{MAX}\) and higher values of \(w\) and \(SAE\);
* AFS panels highlighted similar values of maximum \(SAE\) compared to AHS panels with 3 mm cell diameter;
* The maximum \(SAE\) showed a linear trend with respect to the total thickness of the panels;
* The LVI behaviours of AFS panels under repeated impacts were similar to that observed for the AHS panels. The unloading stiffness after an impact was almost equal to the loading stiffness of the next impact. As the impact number increased, the \(F_{MAX}\) and the loading stiffness increased, while the increment of the displacement decreased;
* Ten repeated impact tests at the impact energy equal to 35 J on the same panel produced lower values of \(F_{MAX}\) and \(w\) with respect to the single impact test at 350 J;
* UPA represented an effective technique for damage detection in AFS panels with aluminium skins, while DR was able to detect foam densification and skin fracture due to the impact event. Both techniques can be applied, with some limitations, for damage detection of real foam-based structures;
* The results of this comprehensive experimental investigation are useful for determining impact performance and optimization design of lightweight AF and AFS panels used in various engineering applications.  
Conceptualization, E.L., V.C. and G.E.; methodology, F.D. and G.E.; investigation, F.D. and G.E.; resources, E.L.; data curation, F.D. and G.E.; writing--original draft preparation, V.C., G.E. writing--review and editing, V.C., G.E., E.L., H.M.; visualization, H.M. and F.D.; supervision, V.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Cyst Detection and Motion Artifacts Elimination Algorithm
(**A**) An image containing only cysts. (**B**) The resulting binarized images after erosion and dilation. (**C**) An image containing both cysts and a motion artifact. (**D**) The resulting binarized image after erosion and dilation. The final step of the algorithm computes the total area of the cysts, by counting the number of black pixels and multiplying it by the spatial resolution of a single pixel. Finally, the black and white images obtained by the proposed method can be used as masks for the enface flow images, in order to correctly evaluate microvascular parameters such as perfusion density (PD). The exclusion of these areas from the computation of PD is of crucial importance, since they can be erroneously identified as areas without vascularization, resulting in an underestimation of PD. For the methodology validation, authors visualized the intermediate results of all the algorithm steps. These intermediate results have been also used to perform fine-tuning on the different parameters, whose values have been given in the previous section. Medical doctors have validated the results in terms of cyst-delineation and motion artifact-removing. For all the analyzed images, the cysts' borders where correctly delineated. Moreover, all the motion artifacts were correctly identified and removed. The considered dataset includes both images where the cysts are clearly visible and easy to delineate, and images where the cysts are big and with a complex geometry. The proposed algorithm has been validated also for images containing complex geometries. The quality of the detection has also been quantitatively evaluated, by comparing the ground truth maps produced by the expert medical doctors with the results of the proposed method. Authors adopted different indexes to evaluate the detection. In particular, the Jaccard index has been adopted to measure how the detection performed by the proposed method is similar to the medical doctor's analysis. The index is defined by Equation (1):  
\[J(A,B)=\frac{|A\cap B|}{|A\cup B|} \tag{1}\]  
where \(A\) is the ground truth produced by the medical doctor, while \(B\) is the output of the algorithm. The cyst detection has also been evaluated in terms of Sensitivity, Specificity, Accuracy, Precision and F\({}_{1}\) score, defined by Equations (2)-(6):  
\[Sensitivity=\frac{TP}{TP+FN} \tag{2}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
Figure 2:0  
where \(TP\) are the true positives, \(FN\) the false negatives, \(FP\) the false positives, and finally \(TN\) are the true negatives. In this case, a \(TP\) is a pixel, identified by both the medical doctor (MD) and the proposed method, as a cyst, while a \(TN\) is a pixel classified as not a cyst by the MD and the algorithm. On the other hand, a \(FP\) is a pixel classified as not a cyst by the MD but identified as a cyst by our method. Finally, a \(FN\) is a pixel identified as a cyst by the MD but classified as not a cyst by our algorithm.
## 2 Differential Radar Interferometry for Deformation Monitoring in Cultural Heritage Sites
### Local-Scale Structural Deformation Monitoring for Heritage Monuments  
Although the above space-borne MT-InSAR methods can monitor deformation at the local scale, they are limited by the geometric distortion caused by shadow and layover, particularly where high-resolution SAR data (for space-borne SAR data, ground resolutions better than three meters are generally recognized as high resolution) are used. In addition, the height information retrieved from SAR interferometry measurement is associated with the effective phase center of all scatterers within theresolution cell, but the distribution of scatterers in height is unknown [33]. Therefore, the above methods cannot separate interfering targets in the vertical direction, losing the displacement information of scatterers distributed over different heights. All of these deficiencies limit the space-borne MT-InSAR's efficiency as a tool for detailed deformation monitoring and preventive diagnosis for heritage structures. In recent years, ground-based radar interferometry and differential SAR tomography have been developed to overcome these limitations and are increasingly used in local-scale structural deformation monitoring for monuments in heritage sites [34, 35, 36].