esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Comparison between U and U\({}_{\mathit{eff}}\)  
The results of the effective thermal transmittance \(U_{\mathit{eff}}\) of opaque building elements from numerical calculations are presented in Table 5 and in Figure 9, together with the difference to the one-dimensional \(U\)-value, the linear thermal transmittance \(\psi\) contribution of the steel studs, and the temperature factor \(f_{\mathit{Rsi}}\). Figure 9: _Cont._Figure 9: _Cont._  
Numerical modelling resulted in significantly greater \(U\)-values when comparing the thermal performance of the walls with and without the steel studs (Table 5), with a discrepancy up to 28.4% and higher in the case of smaller stud spacing. The decrease in the steel stud spacing from 600 mm to 300 mm in the wall resulted in an increase in the \(U_{\mathit{eff}}\) of up to 41.6% compared to the \(U\)-value without the studs. Even though the increase was expected due to the increase in the number of steel studs per unit area of the wall, the magnitude of the increase is surprising. The heat flux distribution plots shown in Figure 9 illustrate the heat flux magnitude through the steel studs for the models analysed in this research. It can be observed that, because the thermal insulation is placed on the external side of the LSF structure and is not penetrated by it, an effective thermal break is provided, without any continuous easy paths for heat flow to occur. It is also evident from the results of the modelling (Table 5, Figure 9) that a comfortable environment can be guaranteed, since the thermal envelope resulted in temperatures as uniformly distributed over the internal surfaces as possible. This is evaluated by the \(f_{\mathit{Rsi}}\), which is very close to indoor air (\(f_{\mathit{Rsi}}\) very close to 1). The resultant internal surface temperatures also have non-critical values regarding the appearance of mould growth or even condensation. The MUZA's LSF walls can be considered a variant of hybrid frame construction, with additional continuous internal thermal insulation (in installation layer). The results presented in Table 5 indicate that the discrepancies in \(U\)-value (neglecting the steel frame by assuming homogeneous layers) and \(U_{\mathit{eff}}\)-value (walls with steel studs) are generally consistent with some similar studies found in the literature on hybrid frame construction. For instance, [PERSON] and [PERSON] [28] concluded that only the interruption of the heat flow by an intermediate or external insulation layer (acting as a \"thermal break\") leads to a relevant limitation of the \(U_{\mathit{eff}}\) values. In their case, \(U_{\mathit{eff}}\) is about 25-30% higher than the baseline \(U\)-value when a greater insulation thickness of 12 cm is used for a hybrid wall with a steel stud spacing of 400-600 mm. It has also been confirmed in similar studies [23; 45] that the main element responsible for improving the thermal performance of the walls is the continuous layer of external thermal insulation. The differences in the hybrid frame walls in these studies are up to 25.59% for different thermal insulation
## 3 Results and Discussion
### Effect of Growing Medium on the Quality of Lettuce
#### 3.3.2 Effect of Growing Medium on the Leaf Antioxidant Compounds and Antioxidant Activity
_IMP_ lettuce displayed higher antioxidant activity in terms of radical scavenging activity on the DPPH free radical compared to VDI, but the first cultivar did not show an increase of antioxidant activity when grown in a growing medium containing compost differently from the second. This agrees with the content of antioxidant compounds that have been found. It has been reported that flavonoids, a class of polyphenols, are selectively absorbed by roots and are able to move long distances within the plant, so the content of phenolic compounds in the compost may play a role in the transport and synthesis of antioxidant molecules in lettuce [88; 89]. [PERSON] et al. [88] conducted a study to assess the effect of compost polyphenols on the phenolic composition, vitamin C, and carotenoid contents of lettuce and reported that the yield and phenolic compounds of lettuce are significantly  
Figure 6: Effect of the cultivar (C) and growing medium (GM) interaction (significant at \(p<0.001\)) on the leaf antioxidant activity. Vertical bars represent the standard deviation of mean values. Different letters mean that the values are significantly different according to [PERSON]’s test at \(p\leq 0.05\). TE: Trolox Equivalent; IMP: Imperiale lettuce cv; VDI: Verde d’Inverno lettuce cv; TC: Peat-based substrate; C1-60: 60% Compost 1 + 40% Peat; C1-30: 30% Compost 1 + 70% Peat; C2-60: 60% Compost 2 + 40% Peat; C2-30: 30% Compost 2 + 70% Peat; C3-30: 30% Compost 3 + 70% Peat; C3-60: 60% Compost 3 + 40% Peat. influenced by the type of compost and that the lettuce's response to the growing medium is highly dependent on genetics. This agrees with our findings which highlighted a higher total polyphenol content in the VDI cultivar when grown in compost-containing media, indicating a cultivar-specific activation of the phenylpropanol biosynthetic pathway. Interestingly, the VDI cultivar in compost-containing media, while showing a total chlorophyll content not different from the control, had a lower chlorophyll a to b ratio indicating a stress condition. It should be pointed out that cardoon biomass is particularly rich in polyphenols [90] and since the three composts, C1, C2, and C3, were obtained using 50%, 68%, and 33% of cardoon biomass, respectively, they may have a different polyphenol content, potentially impacting differently on lettuce yield and quality. It should be noted that for the _VDI_ cultivar, in general, the lowest values of the growth components were shown by C3, which was obtained with the lowest amount of cardoon.
## 3 Model
### Typical Durations of the Processes of Filament Creation and Relaxation of Its Energy
Therefore, it seems highly probable that \(t_{e\text{-}i}\), as well as \(t_{E}\) in (6), may have a value in a rather wide range 10\({}^{-12}\)-3 \(\times\) 10\({}^{-6}\) s depending on the material, a level of laser pumping of the filament and the mentioned contribution of long-living states to the energy balance. Similarly to Equation (4), the time for the formation of the critical impact zone with radius \(R\) around the filament due to thermal conduction is estimated as \(t_{T2}\approx R^{2}/(4\chi)\). In the LFC conditions, the estimate \(R\approx s_{1}/2\) is valid for \(R\), which gives for \(t_{T2}\):  
\[t_{T2}\approx{s_{1}}^{2}/(16\chi). \tag{7}\]  
For example, for the step \(s_{1}\) = 2.5-5 um in the fused silica glass when \(\chi\) = 5\(\cdot\)10\({}^{-3}\) cm\({}^{2}\)/s, one get \(t_{T2}\) = 750-3100 ns, and for sapphire, when \(\chi\) = (1.5-2.4)\(\cdot\)10\({}^{-2}\) cm\({}^{2}\)/s, for the same step range one has \(t_{T2}\) = 150-1000 ns. It is seen that various situations \(t_{E}<<t_{T2}\), \(t_{E}\approx t_{T2}\), or even \(t_{E}>t_{T2}\) are possible depending on the value of \(t_{E}\). Pulse repetition time (in the discussed here case of creating each filament with a single pulse) \(t_{rep}\) is:  
\[t_{rep}=1/f. \tag{8}\]  
For typical PRR \(f\) in LFC from tens of kHz to several MHz, \(t_{rep}\) is numerically of the order of 0.3-50 \(\upmu\)s = 300-50,000 ns. Combining the given numerical estimates, we find that for these nine characteristic times of the LFC process at the studied here submicron radii \(r_{f}\) = 0.25-0.5 \(\upmu\)m, for some values of \(t_{E}\) a chain of time constants may have in particular the following form:  
\[\tau\sim t_{eq}<<t_{in1},t_{in2}<<t_{T1}<<t_{r},\text{ or }\approx t_{r}<<t_{E}<t_{T2},\text{ or }\approx t_{T2}<<t_{rep},\text{ or }\approx t_{rep}. \tag{9}\]  
This increasing sequence of time constants shows that the characteristic durations of the processes involved in the LFC sometimes differ very significantly, by orders of magnitude. It makes possible to significantly simplify multi-scale modelling LFC by considering the constituent processes of radiation absorption, energy release, and impact zone formation sequentially and separately, rather than acting altogether at the same time, which would be a too complicated mathematical task both for analytical and numerical computer modelling. On the other hand, increasing the filament radius several times (from the mentioned \(r_{f}\) = 0.25-0.5 \(\upmu\)m to, for example, \(r_{f}\) = 1.5-2 \(\upmu\)m, which are not considered here), or the use of the material and laser pumping level with another value \(t_{E}\) (much greater or much smaller) can change the sequence of characteristic times compared to (9). As results of modelling depend on the sequence of time constants, we consider below for simplification the situations when:  
\[t_{E}<t_{T1},t_{T2}, \tag{10}\]  
and also when:  
\[t_{E}>t_{T2},t_{T1}. \tag{11}\]
## 3 Results and Discussion
### Biomechanical Performance of Stems  
Force-displacement diagrams were plotted for both bulk Ti and the porous stem structures of the stem-eboxy model illustrated in Figure 4 and the results were compared with the intact bone [42] force-displacement response (Figure 9). The stiffness of the porous stem was increased as the volumetric porosity decreased. It is also obvious from the force-displacement curves (Figure 9) that the stems designed with 70% volumetric porosity (functionally graded and homogeneous) were found to offer the best match with the force-displacement diagram of the intact bone. Designs D12 and D14 presented stiffnesses of 361 N/mm and 417 N/mm, respectively, as compared to the intact bone stiffness (375 N/mm) and the bulk Ti stem (2149 N/mm). However, yielding for all stems with an average volumetric porosity of 70% at an applied force of more than 1500 N was observed, in agreement with the findings of previous studies [7; 34]; this is attributed to the stresses applied at 2250 N exceeding the yield stresses of the equivalent porous material.  
Figure 6: Validation of porous structures against FEA results of [PERSON] et al.  
Figure 8: Effective yield strength and effective Young’s modulus with varying porosities obtained from the finite element simulation.  
Figure 7: Stress–strain diagram for the designed porous cubes with relevant volumetric porosities.  
Figure 9: Force–displacement diagram for stem designs with (**a**) 30%, (**b**) 50%, and (**c**) 70% volumetric porosities, respectively, along with intact bone and bulk Ti stem.  
FE models of stem implanted in the bone were validated with the model of [PERSON] et al. [34]. Bulk Ti stem was implanted inside the bone, as illustrated in Figure 10, and subjected to a concentrated load of 3000 N applied in ten increments. The displacement was tracked at a selected point on the bone head. Force-displacement data were plotted and compared with [PERSON] et al.'s experimental results; a strong agreement was noticed between the two models, as shown in Figure 10. It can be concluded from this validation that the difference in implant geometry between [PERSON] et al. and our model has less impact on the results.
# A Survey on Parameters Affecting MANET Performance
## 5 Related Work
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference Name** & **Throughput** & \begin{tabular}{c} **Average** \\ **End-to-End** \\ **Delay** \\ \end{tabular} & \begin{tabular}{c} **Packet Delivery** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Packet** \\ **Loss** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Routing** \\ **Overhead** \\ **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Normalized** \\ **Routing Load** \\ \end{tabular} & **Network Load** \\ \hline Analyzing the Impact of the & & & & & & & & \\ Number of Nodes on the & & & & & & & & \\ Performance of the Routing & x & x & x & - & - & - & - \\ Protocols in a MANET & & & & & & & & \\ Environment [70] & & & & & & & & \\ \hline A Performance Study of Various & & & & & & & & & \\ Mobility Speed on AODV & & & & & & & & & \\ Routing Protocol in & x & - & x & - & - & - & - & - \\ Homogeneous and & & & & & & & & & \\ Heterogeneous MANET [71] & & & & & & & & & \\ \hline Logistic Regression Based & & & & & & & & & \\ Reliability Analysis for Mobile & & & & & & & & & \\ Ad Hoc Network with Fixed & x & - & - & - & - & - & - & - & - \\ Maximum Speed and Varying & & & & & & & & & \\ Pause Times [72] & & & & & & & & & \\ \hline A Performance Review of Intra & & & & & & & & & \\ and Inter-Group MANET & x & x & x & - & x & - & - & - \\ Routing Protocols under Varying & & & & & & & & \\ Speed of Nodes [73] & & & & & & & & \\ \hline Energy Analysis of AODV & x & - & x & - & - & - & - & - \\ Routing Protocol in MANET [74] & x & & & & & & & \\ \hline Performance Comparison of & & & & & & & & \\ Modified AODV-ETX with & x & x & x & - & - & - & - & - \\ AODV and AODV-ETX Routing & & & & & & & & \\ Protocol in a MANET [75] & & & & & & & & \\ \hline \hline \end{tabular} (x) parameter is used, (\(\cdot\)) parameter is not used.  
\end{table} 
Table 10: _Cont._  
Figure 9: Percentage of evaluation metrics usage in MANETs.
## 3 Results and Discussion
### Correlation Analysis of Indicators
\(F_{1}\), \(F_{2}\), \(F_{3}\), \(F_{4}\), and \(F_{5}\) were the scores for the principal components and were calculated using Equation (2) and Table 3. In the first principal component (PC1), variance contributions of volume, density, firmness, \(Z\)s, and \(\theta\) occupied dominant positions with larger absolute values, indicating that this principal component was influenced mainly by volume, density, dielectric properties, and firmness. The second principal component (PC2) was strongly influenced by \(SSC\) and sugar-acid ratio. The third component (PC3) was influenced by many indicators, and no single indicator occupied a dominant position. The fourth component (PC4) was strongly influenced by \(TA\), and the fifth component (PC5) was strongly influenced by mass. Among the PCs, PC1 and PC5 represented physical properties of peaches and PC2 and PC4 represented chemical properties. PC3 represented comprehensive properties in which the loadings of indicators were closed. The comprehensive score for peaches was calculated using Equation (3) and Table 2. The PCA approach permitted integration of multiple data types. Compared with previous single-index detection methods, the effectiveness and reliability of the results obtained with PCA were improved.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 3 Materials and Methods
### Experimental Scenario  
The main challenge to implement a wireless ANDON tower for industrial monitoring and control systems is satisfying reliability and performance requirements, where some accepted indicators for the reliability of the physical layer in each node are the quality of the wireless link, based on PRR and BER [39]. Figure 1 shows the experimental scenario where the communication tests of the ANDON towers were carried out and where experimental data were acquired. The ANDON tower worked as the sensor node in the WSN, with its transceiver programmed to send 100 test data packets to the receiver node that worked as the coordinator node. The receiver node calculated the PRR from the correctly received packets. Data were saved in a computer connected to the coordinator node. The receiver-coordinator node was placed in a fixed position in the workshop, while the location of the sensor-transmitter node was changed according to the distances selected for the experiment. A star topology was used in the network.
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
###### Abstract  
The dried cubeb berries are widely used as medicinal herb and spicy condiment with special flavor. However, there is a significant definition discrepancy for cubeb berries. In this study, an efficient analytical method to characterize and discriminate two popular cubeb fruits (_Ltisea cubeba_ and _Piper cubeba_) was established. The aroma profiles of cubeb berries were evaluated by different extraction methods including hydro-distillation, simultaneous distillation/extraction, and solid-phase micro-extraction followed by gas chromatography-mass spectrometry-olfactometry (GC-MS-O). In total, 90 volatile compounds were identified by HD, SDE, and SPME combined with GC-MS. Principal component analysis was further applied and discriminated ambiguous cubeb berries by their unique aromas: _Ltisea cubeba_ was characterized by higher level of d-limonene (\"fruit, citrus\"), citral (\"fruit, lemon\") and dodecanoic acid; \(\alpha\)-cubebene (\"herb\") was identified as a marker compound for _Piper cubeba_ with higher camphor (\"camphoraceous\"), and linalool (\"flower\"). Flavor fingerprint combined with PCA could be applied as a promising method for identification of cubeb fruits and quality control for food and medicinal industries.  
cubeb berry; principal component analysis (PCA); solid-phase microextraction (SPME); hydro-distillation (HD); simultaneous distillation/extraction (SDE); gas chromatography-mass spectrometry-olfactometry (GC-MS-O)
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
###### Abstract  
The small-strain stiffness of soil is significant in the accurate prediction of the deformation caused by interactions between foundation soil and structures. Considering the whole range of small strain (\(10^{-6}\)-\(10^{-3}\)), a bending element-resonant column (BE-RC) combined test system was developed to conduct continuous tests on the shear modulus of unsaturated soil. Under the dehydration path, it was used to investigate the small-strain shear modulus of unsaturated silty-fine sand in Hangzhou Bay, China. The results show that the shear modulus under different net stresses and matrix suctions appeared to non-linearly decay with the increase in strain until stable values were reached at a large strain. At the beginning from the saturated state, the \(G_{max}\) value increased slowly with decreasing saturation and reached its maximum value at the optimum saturation (S\({}_{r}\))\({}_{opt}\); then, it rapidly decayed to the level in the saturated, once the saturation degree decreased to a level lower than (S\({}_{r}\))\({}_{opt}\). Additionally, an improved prediction model was proposed for the \(G_{max}\) of unsaturated sand, considering different saturations. Based on the mesoscopic evolution of internal pore water morphology and the variation in intergranular stress caused by capillary action, the variation in the \(G_{max}\) could be divided into three segments of saturation: the boundary effect stage, the transition stage and the unsaturated residual stage.  
unsaturated soil; sand; resonance column; bending element; small strain; shear modulus Article
## 2 Multi-Objective Optimization Model
### Cost Optimization Model  
The total cost of the project consists of the direct cost and indirect cost [23]. The direct cost of a project is the sum of the direct costs of all processes, and the direct cost of each process can be treated as a quadratic function of its duration [24], which is inversely related to time. This means that as time increases, the direct cost decreases. The indirect cost is a linear function of the total duration of the entire project and is positively related to the duration, which means that as the duration increases, so does the overhead cost. Therefore, it can be assumed that the overhead rate is a fixed value.  
The cost optimization model is as follows:  
\[\min C=\sum_{i=1}^{N}C_{Di}+C_{I}=\sum_{i=1}^{N}\left[C_{Di}^{n}+ \alpha_{i}(t_{i}^{n}-t_{i})^{2}\right]+\beta\sum_{i\in L}t_{i}\] \[\text{s.t.}\left\{\begin{array}{l}\alpha_{i}=\frac{C_{Di}-C_{Di }^{n}}{(t_{i}^{n}-t_{i})^{2}}\\ t_{i}^{s}\leq t_{i}\leq t_{i}^{n}\\ -x_{i-1}+x_{i}-t_{i}\geq 0\\ x_{i}\geq 0\\ t_{i}\geq 0\end{array}\right. \tag{2}\]  
where \(C\) is the total cost; \(C_{Di}\) is the direct cost incurred by process \(i\); \(C_{I}\) is the total indirect cost; \(N\) is the number of all processes in the project; \(\alpha_{i}\) is the direct rate for process \(i\); \(t_{i}^{n}\) is the normal duration of process \(i\); \(\beta\) is the overhead rate for the entire project; \(C_{Di}^{n}\) is the direct
# High-Precision Tracking of Free-Space Optical Communication System on Mobile Platforms
###### Abstract  
We propose a new free-space optical (FSO) communication system for moving platform tracking, which can achieve high precision aiming and tracking. Our prototype system consists of three parts. As a coarse sighting structure, the electro-optical pod module is used for target searching and coarse sighting in the initial stage. As a precise aiming structure, the precise targeting loads module located inside the electro-optical pod module uses miniaturized tubular folding optical path technology for high-precision alignment and tracking. The bottom module of the system is used for communication. In the tracking process, the control unit uses spot offset collected by CCD to perform decoupling calculation and then compensates the offset by swinging tracking and aiming structure. We did track experiments on a mobile platform. The experiment successfully tracked a moving target at 100 m distance, and the tracking error was less than 1 mrad. The proposed system can provide stable communication links between the mobile platforms.  
free-space optical communication; optical communications; acquisition; tracking and pointing; mobile stations +
Footnote †: journal: _Photonics_
## 2 Data and Methodology
A conventional 3D seismic data interpretation method comprises cropped time slices at consecutive time values to detect and distinguish the development of geomorphic elements and structural directions in map views [7; 42]. Seismic attribute maps exhibited at certain travel times can be categorized with shady colored points. The negative segments of the seismic wavelets are overlapped by the time slice, and bright colors where the positive sections of seismic wavelet are overlapped by the time slice [43]. The eventual results in shape-visualized time slices derived from the 3D seismic data have been evaluated to recognize geomorphologic elements in map-view, for example, seismic geomorphology features. Few seismic attributes such as red-green-blue RGB color blending and spectral decomposition helped identify channel boundaries with limitations. Three different dominant iso-frequency values (15, 25, and 35 Hz) were utilized to generate the RGB color blending attribute. This attribute provided an excellent resolution of the channel geometries and channel boundaries, fill of channel, and inner thin bedding construction with thickness alteration of 10 to 15 m. In the studied successions, an average seismic velocity of 1500 m/s was used in the depth conversion. This velocity is an attribute of youthful inhomogeneous marine subsurface stratas, wealthy in sand and clay [44; 45]. Overall, fifteen wells and their geological reports have been utilized to acquire the information of the sequence stratigraphic lithologies presented in the seismic sections. A modulation and framework were perfectly established for Group A by manipulating and integrating the 2D view and 3D visualization of the 3D seismic volume and well logs using Petrel 2019 and Paleoscan 2019 applications. The internal reflection configurations have been employed to classify the typical seismic facies in the seismic volume. The seismic well tie tool allows integrated sonic calibration and synthetic generation within the well section window. The generated synthetic was utilized in turn to establish the sequence stratigraphy of the investigated succession. Moreover, the reflection terminations, seismic face variations, and well log information were used to establish sequence stratigraphy of the studied succession. All these factors have been utilized in the succession subdivision into the 3 rd, 4 th, and 5 th orders. This investigation offers the first incorporated study that integrates detailed deltaic deposition, seismic facies classification, sequence stratigraphy, and systems tract interpretation of the investigated succession.