esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Uncertainty in geotechnical properties is mainly promoted by the following three primary sources: measurement error, transformation uncertainty, and inherent variability [1; 2]. Measurement error and transformation uncertainty can be minimized by employing advanced measurement techniques and analysis models [3; 4]. The inherent variability as the most significant source of uncertainty needs to be addressed in reliable prediction and design of geostructures, because its evaluation reflects sedimentation and formation history.  
Soil consolidation, as dissipation of excess pore pressure, is characterized by the coefficient of consolidation \(\mathrm{C_{v}}\). The spatial variability in \(\mathrm{C_{v}}\) has been observed in the site investigation profile. It was quantified with statistical parameters, such as coefficient of variation, correlation length, and anisotropy in spatial correlation [5]. The analysis of numerical simulation has been conducted to explain the field settlement during consolidation process. The 1D Terzaghi's consolidation has been widely used to investigate the dissipation process of pore water pressure under multi-layers, and thus the results showed that non-homogeneous soil medium causes a different degree of consolidation compared to homogeneous [6; 7; 8; 9; 10; 11; 12]. In particular, an extensive consolidation simulation analyzed with stochastic input parameters and single-valued consolidation coefficient calculated from arithmetic mean, harmonic mean, and geometric mean revealed that the consolidation in heterogeneous soils cannot be substituted for an equivalent homogeneous soil, and recommends that it should be taken into consideration spatial variation of consolidation parameters that reflect the location of very thin and very impermeable layers [13; 14]. However, the previous studies have not explained how the spatial variability quantitatively influences the consolidation process. This study explores the statistical characteristics of excess pore water pressure dissipation in heterogeneous soils, with the binary layers,and spatially correlated random fields. In particular, all the numerical results obtained from spatially correlated random fields are analyzed with consolidation times \(\mathrm{t}_{50}\) and \(\mathrm{t}_{90}\), that correspond to the average degree of consolidation 50% and 90%, respectively. This paper starts with random field generation, presents stochastic finite element modeling, followed by numerical results and analysis.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 4 Data Processing
### Video Data Processing  
#### 4.1.1 Augmented Reality  
The system integrates the streaming framework ZLMediaKit [32], deployed on a cloud server with HTTP-FLV protocol support. This enables the low-latency transmission and cross-platform visualization of visual sensor data. Sensors stream FFmpeg-encoded (H.264/AAC) audio/video content via RTMP to the cloud while the web platform uses flvjs and MSE for FLV parsing and browser-based plugin-free playback. When a fixed-point visual sensor streams data to the server, it generates an FLV stream. The FLV stream URL is composed of the server's IP address, followed by a specific path identifier related to the FLV stream, such as \"/flv/[stream_name]\", where \"[stream_name]\" is set by the stream pusher to accurately differentiate between streams from different origins.  
The system creates a Video2D object by accessing the stream address of the visual sensor, assigning geographic coordinates (latitude and longitude) and configuring style and container attributes for rendering. Using camera parameters (angle, heading, and pitch), the system adjusts viewing perspectives and distances.  
In this study, a building at Xiamen University of Technology served as the experimental site. Figure 4 demonstrates the 3 DRS augmented reality effect through the integration of live monitoring video and a 3D model of the deformed region.  
Figure 4: An example of 3D real-scene (3 DRS) augmented reality.  
Figure 3: Flowchart of data processing.
## 2 Materials and Methods
### Berry Skin Phenylpropanoids  
At t\({}_{0}\), t\({}_{1}\), and t\({}_{2}\), flavonoid, hydroxycinnamic acid, and anthocyanin contents were determined in berry skins (5 samples/treatment, each composed by a pool of 30 berries collected from 2 of the 10 vines/treatment). The samples were collected randomly within the clusters of the same vine at each sampling time, also assuming that all the vines of the same treatment and irrigation regime were at the same developmental stage. After freezing berry samples in liquid nitrogen, skins were peeled and lyophilized (Lio-5P, Cik solution, Karlsruhe, Germany), then, phenylpropanol contents were detected with the same protocol of extraction, calibration, and quantification reported in [PERSON] et al. [46] and expressed as umol g\({}^{-1}\) of dry weight (DW). Briefly, 0.7 g/sample of lyophilized berry skin were ground in a mortar and extracted with 75% of aqueous ethanol acidified to pH 2 by HCOOH (3 \(\times\) 5 mL). The supernatant was partitioned with 3 \(\times\) 5 mL of n-hexane, the extracts were added together and reduced to dryness under vacuum, then rinsed with MeOH/H\({}_{2}\)O (50/50, pH 2). The analytical determinations were conducted using a liquid chromatograph equipped with a quaternary 200Q/410 pump and an LC 200 diode array detector (DAD) (all from Perkin Elmer, Waltham, MA, USA). Individual anthocyanins and flavonoids were quantified at 530 and 330 nm, respectively, using calibration curves of authentic standards (Extrasynthese, Genay, France).  
In order to investigate how SWE treatments affected anthocyanin composition, the percentage of metabolized anthocyanins on total amount of 3-glucoside anthocyanins and the ratio of methoxylated to nonmethodoxylated derivatives were estimated by the individual anthocyanin content.  
Moreover, in order to compare the global balance of the biosynthesis generating anthocyanins in SWE and CTRL berries in both irrigation regimes, three key enzymatic activities, flavonoid 3',5'-hydroxylase (F3'5'OH), 3'-O-methyltransferasease (3'-OMT), and 5'-O-methyltransferasease (5'-OMT), linked to known structural genes involved in the synthesis of anthocyanins, were investigated following [PERSON] et al. [19].
## 4 Discussion
### Non-Targeted LC-QTOF-MS Based Metabolomics Data Analysis
#### 4.2.3 Fatty Acids Derivates  
Fatty acids are often esterified in form of phospholipids, glycerolipids, or sterol backbones. Their structure consists of a long chain of hydrogen-bonded carbons, with a terminal carboxyl group (-COOH) [11]. This functional group is key in their function as energy reservoirs. In this regard, they provide structure to and energy for cells in the absence of glucose and participate in the response to low-temperature tolerance. Finally, they are involved in the production of cholesterol as precursor for the biosynthesis of hormones such as estrogen, testosterone, vitamin D hormone, steroids, and prostaglandandins [55]. These functions also explain their high nutritional value and pharmaceutical potential.  
Fatty acids are synthesized in plastids and assembled by glycerolipids or triacylglycerols in the endoplasmic reticulum [81]. Fatty acid synthesis is a complex process involving three main phases: de novo synthesis of fatty acids in the plastidial compartment from acetyl CoA, desaturation in the chloroplast and elongases, modified reactions such as hydroxylation, and epoxidation, which take place in the endoplasmic reticulum [82]. Figures 5 and 6 describe in general terms the metabolism of fatty acid biosynthesis.  
About 22% of metabolites detected in this non-targeted LC-QTOF-MS metabolomic analysis of a _C. sativa_ sample are involved in different reactions related to the fatty acid biosynthesis. As products of de novo fatty acid synthesis, palmitoleic acid and other linolenic acids (13-Hydroxyoctadecatrienic acid, octadecetraenoic acid, and trihydroxy-octadecadiene acid) were identified. It has been reported that increasing the dietary intake of these fatty acids reduces the risk of coronary heart disease [83] due to inhibition of coagulation, improvement of glucose homeostasis, and attenuation of inflammation. On the other hand, fatty acids metabolized via modifiable reactions increase the production of vitamin E, prostacyclin, prostaglandandins, leukotrienes, and hydroxy and hydroperoxy fatty acids, which have been reported to be involved in the modulation of cell growth, angiogenesis, inflammation, thrombosis, immune response, inhibition of carcinogenesis and tumor growth, and stimulation of cancer cells apoptosis, among others [84; 85].
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 3 Si-Based Photodetectors
### Basic Terminology  
We first present some terminologies related to the PD characteristics. The quantum efficiency is the ratio of the number of generated electron-hole pairs to the number of incident photons. Since the quantum efficiency directly determines the sensitivity of the entire receiver, designing a PD to have a high quantum efficiency is crucial. This quality is generally expressed as:  
\[\eta=\frac{I_{ph}/q}{P/h\upsilon} \tag{1}\]  
The internal quantum efficiency is sometimes defined by de-embedding the loss occurring at the detector interface to evaluate the detector performance alone. The responsivity is a more frequently used quantity by engineers with a similar meaning to the quantum efficiency, and can be expressed as:  
\[R=\frac{I_{ph}}{P}=\frac{\eta q}{h\upsilon}=\eta\frac{\lambda(nm)}{1240} \tag{2}\]  
The bandwidth of the PD is determined by two time constants: the transit time and the RC time constant which arises from the series resistance and the junction capacitance of the PD. Hence, the total time constant should be considered for the estimation of the bandwidth as follows:  
\[\tau=\sqrt{\tau_{transit-time}^{2}+\tau_{RC}^{2}} \tag{3}\]
## 1 Introduction  
Recently, high-speed machines have been studied extensively with great interest due to the advantages of higher power density, increased reliability, and miniaturization than conventional medium speed or low speed electric machines [1]. Among them, in the case of high-speed permanent magnet (PM) motors, eddy current causes demagnetization owing to heat, which can have a serious effect on efficiency of the high-speed machine. Therefore, eddy current loss in the high-speed rotation region is one of the important research areas of research [2]. The primary causes of this eddy current loss are space harmonics of magneto-motive force (MMF) due to winding distributions and time harmonics of stator current due to motor driver carrier harmonics [3, 4]. The harmonics of the carrier harmonics have a great influence on the electromagnetic losses when compared with the sinusoidal currents. Therefore, when analyzing high-speed PM motors, electromagnetic loss analysis considering carrier harmonics is essential. In addition, MMF due to winding distribution is an important factor in reducing back EMF harmonics, torque ripple, eddy current loss, saturation, noise and vibration [5, 6]. In this paper, the effects of the MMF and time harmonics of the stator current according to the winding distribution of a high-speed PM motor on the electromagnetic characteristics were comparatively analyzed using analytical methods and FEM.  
For most high speed motors, a two-pole cylindrical magnet rotor, which is advantageous in terms of control perspective, is frequently used [7]. Therefore, in this paper, a combination of two poles and six slots was selected and the electromagnetic characteristics according to the concentrated and the distributed winding patterns were comparatively analyzed. In addition, the final model was proposed by analyzing the relationship between the magnet usage according to the rotor shape and the electromagnetic and mechanical properties according to the winding patterns. Finally, the optimal model was manufactured and the validity was experimentally verified. Figure 1a shows the 3D structure of two different winding patterns, Figure 1b shows the 3D rotor shape structure along the hollow shaft, and Figure 1c shows the manufacturing model to which the hollow shaft and the concentrated winding pattern are applied.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Fortified Anonymous Communication Protocol (FACP)  
The _FACP_[34] technique uses intervals and variable-size sub-intervals to divide the time of the network. It provides high anonymity as the technique generates dummy packets in sub-intervals as needed to maintain the average number of dummy packets throughout the network, authors of [34] argued. However, the protocol works in a way that causes a very low and measured overhead and a higher lifetime for the network. Another advantage of this technique is that it can handle the transmission of multiple packets at once through the network efficiently. In addition to anonymity, it also handles routing privacy, timing privacy, and rate privacy. All the packets whether are real or fake are encrypted. It provides a unique methodology for generating new keys for every transmission so there is no one key used more than once. The framework provides data integrity as well as privacy. The unique contribution is addressing the temporal and rate privacy by using fake messages. This is by making sure that every sensor node has similar transmission rate whether it is close to the sink or at the edge. Calculating a key for every transmission will be time and power consuming but it provides high source anonymity.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 6 Evaluation of 1-D CNN Models for Untrained Damage Cases
### Damage Classification by 1-D CNN Models Using SVD Responses  
For SVD responses, three partially untrained datasets were generated similarly to time-history responses as shown in Table 3. Figure 24 shows the loss values of the CNN model trained using partially untrained SVD databanks. In both Case 1 and Case 2, the training and validation losses exhibited a sharp decrease in the first ten iterations, followed by convergence to near-zero values by the 40 th iteration. For Case 3, the training and validation losses showed a slower initial decline over the first 20 iterations, followed by minor fluctuations until the end of training.  
Figure 25 shows the damage classification results of the CNN model with partially untrained SVD databanks. For Case 1 (Figure 25a), all results for trained levels showed no misclassification. The damage levels D2, D4, and D6 were classified to nearby levels D1, D3, and D7, respectively. Case 2 (Figure 25b) demonstrated no misclassification in the results for trained levels (i.e., D0, D2, D4, and D6). The untrained damage levels D1, D3, and D7 were classified into nearby levels D2, D4, and D6, respectively. The untrained damage level D5 was classified into D4 and D6.  
In Case 3 (Figure 25c), results for trained levels D0 and D7 showed no misclassification, but 50% of the data for D3 and D4 was misclassified. The untrained damage levels D1 and D2 were classified to the nearest damage level D3; untrained damage level D5 was classified to the nearest damage level D4; and D6 was classified to the nearest damage level D7.
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
###### Abstract  
Irrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral imaging has been widely used as a nondestructive measurement method in agriculture to obtain plant biological information. The objective of this research was to establish an approach to obtain the tomato leaf water status--specifically, the relative water content (WC) and equivalent water thickness (MC)--for five different tomato cultivars in real time by using hyperspectral imaging. The normalized difference vegetation index (NDVI) and two-band vegetation index (TBI) analyses were performed on the tomato leaf raw relative reflection (RAW), the inversion-logarithm relative reflection (LOG), and the first derivative of relative reflection (DIFF) from wavelengths of 900 nm to 1700 nm. The best regression model for WC assessment was obtained by TBI regression using DIFF at wavelengths of 1410 nm and 1520 nm, and the best regression model for MC assessment was obtained by NDVI regression using RAW at wavelengths of 1300 nm and 1310 nm. Higher model performance was obtained with MC assessment than with WC assessment. The results will help improve our understanding of the relationship between hyperspectral reflectance and leaf water status.  
hyperspectral imaging; tomato leaf; water status; normalized difference vegetation index; two-band index +
Footnote †: journal: Applied Sciences
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Beaconless Opportunistic Routing Protocol
#### 4.4.2 Adaptive Context-Aware Beaconless Opportunistic Routing Protocol (CABR)  
[PERSON] et al. proposed an adaptive context-aware beaconless opportunity routing protocol based on QoE [52], which is used for video propagation on FANET. The authors think about a variety of environmental information to calculate dynamic forwarding delay (DFD) [53], including information obtained locally (contains node energy, buffer state, and three-dimensional geographical location) and information obtained from received packets (contains link quality and UAV neighbor movement information). The DFD of node \(v_{i}\) is quantified by  
\[DFD(v_{i})=min(DFD(v_{i}))=DFD_{max}\times(\sum_{i=1}^{n}c_{i}\times metric_{ i}). \tag{12}\]  
where \(metric_{i}\) represents the value of context infomation \(i\), such as link quality, energy, buffer and UAV mobility in 3D space. Then, the Bayesian Network (BN) was introduced to define the weight vector of each metric, that can help to identify which metric has the greatest impact on overall performance. Moreover, the author considered an Analytic Hierarchy Process (AHP) to adjust the behavior of the protocol by changing the priority of the environmental information, this made the routing decision more realistic. Finally, this paper proposed a position prediction mechanism which integrated nodes' 3D position information and chose nodes with little change in velocity, thus reducing the probability of routing failure.  
**Advantages:** (1) CABR combines the three-dimensional spatial information to predict the position of nodes, then select a node with a small change in the speed or direction as the next hop, and the possibility of routing failure is small. (2) CABR transmits data packets in uni-cast mode, avoiding the disadvantages of multi-cast transmission.  
**Disadvantages:** CABR does not consider the impact of node density on routing performance, if the node carrying the data moves to the edge of the network, it may not find a suitable next hop to transmit the data, then increasing the transmission delay.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 3 Computational Time Reversal for Structures  
In computational TR, at least one of the two steps, that is forward or backward step, is to be done numerically using some appropriate method, e.g., finite element method (FEM) or boundary element method (BEM). Here, we use the conventional FEM, used in the majority of structural dynamics problems, realizing the switched TR described in Section 2.2.1. However, it is worth mentioning that in specific cases, it could be that BEM would be the natural choice [35] for utilizing the TR approach described in Section 2.2.2.  
The methodology could be stated as described in Figure 1. We assume, in the forward step, that a source excites on some point \(x_{s}\) the structure and that the response \(u_{r}(t)\) is recorded on some other point \(x_{r}\) for the time duration \(T\). Then, in the backward step, the reversed in time response's signal \(u_{r}(T-t)\) is re-emitted as excitation on the \(x_{r}\) point. In that backward step, an energy refocusing will appear on the \(x_{s}\) point after the same duration of time \(T\). This main application is called a source localization approach. Another application is that of damage detection which considers damage location \(x_{d}\) as the point of a secondary source. This secondary excitation equals the signal scattered by the presence of the damage and could be given in relation to the original incident field emitted by the source at \(x_{r}\) as  
\[u_{r}^{d}=u_{r}^{t}-u_{r} \tag{17}\]  
in other words, \(u_{r}^{t}\) contains both primal and secondary source influence on response, \(u_{r}\) only that of the original one and \(u_{r}^{d}\) that of damage scattered field. Therefore, by re-emitting (in the backward step) the signal reversed in time \(u_{r}\), one could locate the original source; by re-emitting \(u_{r}^{d}\), one could locate the secondary source due to damage; and finally, by \(u_{r}^{t}\) re-emission, one could locate both the original and secondary sources.  
Figure 1: A schematic representation of the two distinct steps of time reversal: the forward (**left**); and backward-one (**right**).
## 2 Origin and Concept of DT  
One of the earliest notable applications of Digital Twin (DT) technology dates back to 1970, when NASA engineers used simulators as virtual twins for the command module and its electrical system to rescue the Apollo 13 mission. This life-saving intervention was executed in less than two hours, demonstrating the enormous potential of the technology at that early stage [10]. Today, NASA continues to employ DT technology in the development of next-generation vehicles and aircraft.  
The term 'Digital Twin' was first introduced in 2003 by Professor [PERSON] at the University of Michigan during a course on product lifecycle management [11]. The concept has since evolved significantly, with various scholars offering different definitions and developmental frameworks [12; 13; 14]. According to the Encyclopedia of Production Engineering, a Digital Twin is 'a representation of an active unique product, which can be a real device, object, machine, service, intangible asset, or a system consisting of a product and its related services' [15]. At its core, DTs are virtual models of physical objects or systems that can be analyzed and manipulated using real-time data [16].  
Although the concept of DT has been around for a while, its transition from theory to practical application has accelerated in recent years. It is anticipated that DT technology will integrate further with other technologies like voice recognition, augmented reality, the Internet of Things (IoT), and artificial intelligence (AI). Market research by MarketsandMarkets forecasts a meteor rise for DT technology, projecting its market value to grow from $6.9 billion in 2022 to an astounding $73.5 billion by 2027--a Compound Annual Growth Rate (CAGR) exceeding 60% [17]. This surge is primarily attributed to growing interest in the manufacturing sector, aimed at cost reduction and supply chain optimization.
# Measurements and Analysis of Sound Reflections from Selected Building Facades
## 2 Materials and Methods
### Sampling Points Selection
Distance markings were adopted in accordance with Figure B.2 of the standard [20]-Figure 8.  
Measurements at the building facade were conducted at two heights of 2 m and 6 m from the ground, mounting microphones on a portable measuring mast [44]. The fifth measurement point was fixed and located 4 m above the ground at a distance from the vertical reflecting surfaces. Bearing in mind the guidelines from Appendix B of the standard [19], it can be concluded that microphones M2 and M2F 3 m away and M3 50 m offset from the building facade were in the free sound field.