esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental
### Preparation, Quality Tests and Physical Properties
#### 2.1.4 Thermo-Physical Properties  
To identify the influence of the nanoparticles on the thermo-physical properties of the base fluid, a series of experiments were performed with instruments to measure the thermo-physical properties of the nanofluids including the density, the thermal conductivity and the heat capacity of the nanofluid. To measure the thermal conductivity and the heat capacity of the system, a KD2 pro manufactured by Decagon (with the accuracy of \(\sim\)5% and \(\sim\)8% of the reading value were used. To measure the viscosity, the viscometer manufactured by Brookfield DV I series with accuracy of \(\sim\)6% was used. As can also be seen in Figure 7, adding more nanoparticles to the carrying fluid, thermal conductivity of the fluid increases. For example, at 40 \({}^{\circ}\)C, at wt. % = 0.025, the measured thermal conductivity was \(\sim\)0.21 W/(mK), while for the same temperature, at wt. % = 0.1, the thermal conductivity increased to \(\sim\)0.25 W/(mK), increasing by 19%. Notably, the thermal conductivity of acetone was 0.18 W/(mK), while those of CNP-acetone were larger than pure acetone. Interestingly, with temperature increment, the thermal conductivity of the nanofluid increased. This can be attributed to the Brownian motion boosting within the bulk of the nanofluid, which enhances the collision and the conduction-conduction  
Figure 5: Results of XRD test conducted on carbon nanoparticles.  
Figure 6: (**a**) Scanning electron microscopic image of the CNPs, (**b**) TEM image of CNPs.  
heat transfer between the particles. The maximum thermal conductivity was 0.27 W/(mK) at the largest temperature of 50 \({}^{\circ}\)C and wt. % = 0.1.  
Figure 8 represents the influence of temperature on the viscosity of CNP-acetone nanofluid for different mass fractions of CNP. According to the figure, an increment in the mass fraction of the nanofluid increases the frictional forces between the layers of the base fluid increases resulting in higher viscosity of the nanofluid. In addition, temperature slightly reduces the viscosity of the nanofluid. The highest viscosity was 0.00048 Pa.s and belonged to the nanofluid at wt. % = 0.1 and T = 20 \({}^{\circ}\)C.  
Figure 9 shows the impact of the temperature on the heat capacity of the nanofluid for various mass fractions of CNP in acetone. With temperature increment, the heat capacity boosts which is owing to the augmentation in the heat capacity of acetone. In addition, with growth in the number of nanoparticles dispersed within the nanofluid, the heat capacity of the nanofluid increases. This is because the heat capacity of carbon by itself is larger than acetone. Thereby, raising the mass fraction of the nanofluid promotes the heat capacity of the nanofluid.  
Figure 8: Dependence of viscosity of nanofluids on temperature for different concentrations of CNPs in acetone.  
Figure 7: Variation of the thermal conductivity of the prepared nanofluids on temperature for various mass fractions of CNPs in acetone.
## 3 Results
### Measurement Result of the System under Outdoor Conditions  
#### 3.2.1 Validation of Methane Concentration during Outdoor Operation  
Subsequent to determining the best feed ratio and reactor operating temperatures, the performance of methanation was examined. The methanation data to be analyzed were under operating conditions on a clear, sunny day between 10:00 a.m. and 2:00 p.m. The target reactor temperature for both reactors was 260 \({}^{\circ}\)C. Gases were collected and calibrated each hour.  
Figure 5a shows \(GR_{\text{H2}}\) compared to DNI. Figure 5b shows the internal temperatures of the reactors compared to the concentration of CH4. A CO2 conversion efficiency of 97.6% was maintained for approximately 4 h. Although the operating temperature was set to 260 \({}^{\circ}\)C, the inner temperature of reactor 1 increased over 260 \({}^{\circ}\)C. The cooling system was not utilized in this system. Therefore, the heat was released during the reaction. However, the temperature of reactor 2 was stable at the operating temperature (260 \({}^{\circ}\)C). This was because of the low reaction rates in reactor 2. Most of the reaction was performed in reactor 1 and thus the temperature of reactor 1 was higher than that of reactor 2 as a result. The average \(GR_{\text{H2}}\) was 1.19 NL/min in the experimental period (10:00 a.m. to 2:00 p.m.). CO2 gas applied to the methanation system, which corresponds to one-quarter times the fed H2 gas, was 0.30 NL/min. The total mixture gas, which fed to the system, was 1.48 NL/min. Due to the changing DNI during the experimental period, the generated H2 gas changed. Therefore, mixture gas fed to the system and space velocity also changed. The degradation of catalysis could not be seen in the experimental period.  
Figure 4: **(a) Effect of reactor temperature on concentration of methane and (b) effect of ratio (CO2:H2) on CO2 conversion to CH4.**
## 2 Versatile Video Coding (VVC)
### In-Loop Filtering  
The adaptive loop filter (ALF) is a new feature for VVC; the other filters, DBF [31] and SAO [32], show a high degree of similarity to those of HEVC. VVC has adopts DBF, SAO and ALF [33] to handle visible artefacts such as flickering, colour shift, blocking, blurring, and ringing in the reconstructed samples. In VVC, ALF two diamond-shaped filters are applied: one of \(7\times 7\) diamond shapes with thirteen different coefficients to luma blocks and the other of \(5\times 5\) diamond shapes with seven different coefficients to chroma blocks. Based on the vertical, horizontal, and 2 diagonal gradients, one of the twenty-five classes was chosen for each \(4\times 4\) block. Therefore, Wiener filters were computed for each class. However, before in-loop filtering was initiated, luma mapping with chroma scaling was applied to adjust the input luma signal.
# Behaviour of Steel Tubular Knee Joint in Aluminium Frames with Tension-Tie Element
## 3 Numerical Analysis
### Numerical Parametric Analysis  
For prefabricated aluminium halls with spans greater than 10 m, a high-strength steel tension tie element is used (commonly \(\O\) 16, 6 \(\times\) 37+FC, 1770 MPa [17]) to reduce bending in the knee and to allow for greater spans. Due to the limitations of the laboratory equipment, the circular tube CHS 100 \(\times\) 4.75 (S 235 JRH) was selected, resulting in much stiffer overall behaviour of the steel knee joints subjected to bending compared to one in a real structure, when high-strength wire rope would be used. That kind of behaviour can be contributed to three reasons:  
1. The cross-sectional area of the tie element used in the laboratory testing is 14.2 cm\({}^{2}\), which is much higher compared to that of a high-strength steel wire rope that is equal to 1.10 cm\({}^{2}\) for commonly used \(\O\) 16, 6 \(\times\) 37 FC steel wire rope.
2. Due to spatial limitations of the laboratory equipment, the length of the tie element was approx. 1 m, which is much shorter compared to real aluminium portal frames with a tie element (usually spanning between 10 and 20 m).
3. High strength steel wire ropes in general have a lower modulus of elasticity compared to that of a structural steel. The modulus of elasticity for various types of steel wire ropes with fibre core can vary in the range from 68.7 to 98.1 GPa according  
Figure 14: Failure mode comparison between: (**a**) laboratory specimen; (**b**) numerical model.  
to [24]. For this numerical analysis, a modulus of elasticity of 80 GPa was chosen and implemented.  
To investigate the influence of the tie element stiffness on the behaviour of the steel knee joint, numerical analyses were conducted, varying the stiffness of the tie element. Since the geometry of the steel wire rope is very complex, a simplification was made in the numerical model where the geometry of the specimen was left unchanged (Figure 12), but the modulus of elasticity of the tie element was reduced by the nondimensional factor, \(\mathrm{f_{red}}\), which represents the stiffness ratio of the tie element used in the laboratory test and the tie element used in practice. Taking into consideration the difference in cross-sectional area, modulus of elasticity and the length of the tie element between the laboratory specimen and real structure, the reduction factor, \(\mathrm{f_{red}}\), can be defined with the following expression:  
\[\mathrm{f_{red}}\ \approx\ 17.2\ \mathrm{L}, \tag{6}\]  
where L represents the span of the portal frame in meters. Six different spans where taken into consideration ranging from 1 to 20 m (Table 3). Considering that the common span of the aluminium portal frames with the tie element ranges between 10 and 20 m, 5-, 2.5- and 1-m spans were considered for academic purposes only. A knee joint without the tie element was considered as well. In Table 3, reduction factors \(\mathrm{f_{red}}\) along with their corresponding reduced modulus of elasticity are given for the considered span values.
# Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
###### Abstract  
With the development of artificial intelligence (AI) and high-performance computing (HPC), the microelectronic industry is challenged with increased device integration density. Chiplet architecture can break through a variety of limitations on the system-on-chip (SoC) to create a high-computility system. However, chiplet heterogeneous integration suffers from high heat flux and serious thermal interaction problems. The factors affecting thermal interaction are not clear. In this paper, a collective parameter model and a distribution parameter model are developed to clarify the optimization method to mitigate thermal interaction. The trends predicted by the parameter model are consistent with the finite element method (FEM) simulation results. Furthermore, to cool the chiplets, a thermal test vehicle is designed and fabricated, and the cooling performance of the test vehicle with different flow rates, different TIMs (Thermal Interfacial Materials) (DOW5888 vs. liquid metal), and different heat modes is experimentally investigated. Compared with DOW5888, the utilization of liquid metal TIM can mitigate thermal interaction by 56% and 76% at flow rates of 0.2 L/min and 0.8 L/min, respectively. Consequently, at a temperature rise of 60 \({}^{\circ}\)C and a flow rate of 0.8 L/min, using liquid metal TIM can achieve heat fluxes of 330 W /cm\({}^{2}\) and 520 W/cm\({}^{2}\) for two chiplets, respectively.  
thermal interaction; chiplet 2.5D integration; near-chip cooling; TIM; liquid metal
## 5 Conclusions  
In conclusion, we have compared the resonance-induced enhancement of single harmonic emissions during the propagation of ultrafast pulses through tin-containing laser-induced plasmas. The analysis of the dynamics of LIPs produced on a Sn bulk target and a Sn NP target has shown the range of the optimal delays between heating and driving pulses (130-180 ns), at which the maximal harmonic yield can be achieved. The similarity of this parameter for two plasmas allows us to suggest the mechanism of energy transfer during ablation, resulting in similar kinetic energy of each ingredient of ejected species.  
The enhancement of the 17 th and 18 th harmonics of 806 nm pulses was analyzed in the case of single-color and two-color pumps of plasma, showing up to a 12-fold enhancement of even harmonics compared with the neighboring orders in the latter case. We have compared the single-atomic Sn and Sn nanoparticles-containing plasmas to demonstrate a distinction in the enhancement factor of the single harmonic in the case of tunable near-infrared pulses. We have shown the enhancement of a single harmonic in the vicinity of the 4d\({}^{10}\)5s\({}^{2}\)5p\({}^{2}\)P\({}_{3/2}\)\(\rightarrow\)4d\({}^{9}\)5s\({}^{2}\)5p\({}^{2}\) transitions of Sn II ions and demonstrated how this process depends on the constituency of the plasma components at different conditions of the target ablation.  
The application of tunable (1280-1440 nm) radiation from the optical parametric amplifier allowed us to demonstrate the variations of single harmonic enhancement using a tunable two-color pump of Sn-containing plasma. Our studies using tunable NIR pulses at the fluence of an HP of F = 1.6 J cm\({}^{-2}\) did not reveal that resonance-related processes lead to the enhancement of the harmonic yield, which suggests that the tuning of ionic transitions should not be the main factor leading to the cancellation of the resonance-related effect. Meanwhile, the presence of tiny NPs and clusters and single-particle species appeared in LIPs during the disintegration of large (70 nm) original aggregates of Sn atoms allows for the formation of conditions for the involvement of tin ions in the enhancement of single harmonics in the vicinity of ionic transitions possessing strong oscillator strength.  
Conceptualization, R.A.G., H.K.; methodology, R.A.G.; formal analysis, R.A.G., H.K.; investigation, R.A.G., H.K.; writing--original draft preparation, R.A.G.; writing--review and editing, R.A.G., H.K.; visualization, R.A.G.; supervision, H.K.; funding acquisition, R.A.G., H.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS KAKENHI (grant number 24760048), ERDF (grant number 1.1.1.5/19/A/003), State Assignment to Higher Educational Institutions of Russian Federation (grant number FZGU-2020-0035), grant and the Common Research Facility at the American University of Sharjah (grant number FRG AS1801).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Stressing State Analysis of SRC Column with Modeling Test and Finite Element Model Data
## 6 Conclusions  
In this paper, the feasibility of modeling and analysis of the test and finite element model data of SRC columns by correlation modeling analysis method and the reasonableness of the failure starting point is verified by citing the data from literature [27] and establishing the finite element model of SRC columns. Based on the structural stressing state theory, the correlation modeling analysis is performed on the test strain data of the SRC column and the output element strain energy (\(ESE\)) data of the finite element model, and the slope increment criterion is applied to reveal the characteristic point P of the SRC column. The reasonableness of the modeling method and the starting point of failure is further verified through the stressing state modeling and cloud diagram analysis of the finite element model output internal energy (\(IE\)) and element strain energy (\(ESE\)) data. The main research contents and conclusions of this paper are summarized as follows:  
* A finite element model corresponding to the SRC column test in the literature [27] was developed, and the reasonableness of the model was verified;
* A correlation stressing state modeling analysis method applicable to modeling SRC column tests and finite element model data is proposed;
* Based on the correlation characteristic pair, the stressing state modeling analysis, and applying the slope increment criterion, the characteristic points in the failure process of the SRC column are revealed and defined as the starting point of failure.  
The failure point obtained from the modeled SRC column test and finite element data are compared, and the cloud diagram characteristics of the SRC column finite element model near the failure point are observed to verify the feasibility of predicting the failure point of the test SRC column by the finite element model.  
Conceptualization, Z.S. and B.L. methodology, Z.S.; formal analysis, Z.S.; investigation, Z.S.; resources, Z.S.; writing--original draft preparation, Z.S.; writing--review and editing, B.L.; visualization, Z.S.; supervision, G.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 9: Cloud diagram around failure starting point: (**a**) F = 733 kN; (**b**) F = 750 kN; (**c**) F = 766 kN; (**d**) F = 782 kN; (**e**) F = 797 kN.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### The Superiority of the Multi-sensory Data Fusion System  
The proposed multi-sensory data fusion system for non-destructive MC detection compares favorably with previous methods for the monitoring of field standing trees.  
Firstly, all the characteristic parameters required for the detection are collected in a non-destructive way, without causing irreversible damage to the standing trees.  
Secondly, the structural differences between the surface and interior of the standing trees result in significant noise when the AE signal propagates inside the trunk with its  
Figure 15: Trunk MC variations of four trees during 48 h: (**a**) MC records of metasequoia; (**b**) MC records of lodgepole pine; (**c**) MC records of hovenia acerba; (**d**) MC records of cherry–apple.  
inhomogeneous, porous, and anisotropic nature. Here, we used Vaseline to couple the acquisition sensor and live tree trunk during installation. On the one hand, the acquisition system is highly sensitive, the distance between the AE probes and the sound source is very close (10 cm/20 cm), and the three sensing devices are mounted in one straight line, which reduces the interference noise at the physical level and fully retains the effective AE signal. On the other hand, when the AE signal propagates along the tree bark, the interference for the original signal is further averted and reaches the optimal propagation path. At this point, the impact of AE signal noise can be weakened as much as possible. This is also the reason why AE signals on the bark of the trunk were collected.  
Thirdly, the power consumption of the proposed WASN in this paper mainly includes three parts: AE signal source (i.e., the vibrating motor), AE signal acquisition, and LoRa wireless communication. The rated operating voltage and current of the micro-vibration motor are 3.3 V and 85 mA, respectively, which means its power consumption is about 280.5 mW. The LoRa module SX1278 has a maximum transmitting power of 100 mW. The power consumption of the analog-to-digital converter AD7356 and the preamplifier OPA627 chip is about 111 mW. Hence, the energy consumption of the whole WASN system in a working state is about 492 mW. Moreover, the WSAN nodes have two modes: working and sleeping. Their duty cycle is usually set at about 3%, and considering that the node only needs to collect data every 6 h, it can be calculated that the power consumption is about 2000 mW per day. When using a 3.7 V 30 Ah lithium battery pack, one WASN node can operate normally for 55.5 days. Therefore, the designed WASN basically meets the application requirements for forestry field monitoring.  
Meanwhile, the soil sensor node adopts a 5 V rechargeable lithium battery, and it also has two modes of work and sleep. After data collection, the node will enter sleeping mode immediately. The soil node can work normally for 15 h under full voltage through lab testing, and the working time is about 8 min per day; thus, the soil node can last for 112 days after installation, which fully shows its reliability and stability in the forest IoT scenario.
# Highly Compact 4 \(\times\) 4 Flower-Shaped MIMO Antenna for Wideband Communications
## 3 2-Port MIMO Antenna Design
As well, the realized gain and the radiation efficiency with and without the decoupling stubs are displayed in Figure 6c,d, which show that the antenna has gain fluctuated between 1.5 dBi and 3.3 dBi for both cases while the efficiency fluctuates from 80% to 90% with stubs and from 92% to 95% without stubs. Figure 5: Layout of the 2-port (face-to-face) MIMO antenna: (**a**) without decoupling stubs; (**b**) with decoupling stubs. Figure 6: The 2-port (face-to-face) MIMO antenna: (**a**) S\(-\)parameters outcomes without decoupling stubs; (**b**) S\(-\)parameters outcomes with decoupling stubs; (**c**) gain; (**d**) radiation efficiency. From the introduced outcomes of the two-port MIMO antenna, it is worth noting that the two MIMO arrangements (side-by-side and face-to-face) provide distinct performance in terms of improved isolation in both cases after using decoupling stubs, besides their stable impact on the return loss outcomes, which are approximately similar to the outcome of a single antenna element. Consequently, the modified 2 \(\times\) 2 MIMO antenna has a compact size with enhanced outcomes and can be extended to work with multiple ports utilizing the same two arrangements of isolation stubs due to their distinguished impact on the antenna performance. The forthcoming section introduces the 4 \(\times\) 4 MIMO antenna with and without the isolation stubs, in addition to the implementation of the investigated arrangements for size reduction purposes with enhanced performance.
# Improving Text Recognition Accuracy for Serbian Legal Documents Using BERT
Our approach envisions the development of a BERT-based model for each specific domain that it is applied within. According to the reported OCR error detection confidence [9], we take advantage of this information at the word level to optimize and enhance our approach. Compared to similar approaches [8], we consider our approach to be simpler and more efficient, and it can achieve excellent results when focused on a specific domain. In a demonstration of the previously described advantages, LexBERT autocorrects the results of the optical character recognition process applied upon scanned legal documents written in Serbian Cyrillic and generate high-quality legal text corpora for the Serbian language. To do so, LexBERT relies on a BERT-based deep neural network model we have developed specially for these purposes, named SrBERTa v2. LexBERT detects lower OCR accuracy and generates correction suggestions through the means of SrBERTa v2. Generatedcorrections are compared to OCR output using a similarity measure we have developed for Serbian Cyrillic letters. We present validation results obtained using publicly available scanned legal document corpora gathered from the Official Gazette of the Republic of Serbia [10], originating between the years 1970 and 2002. The rest of this paper is organized as follows. In Section 2, we present the state-of-the-art large language models and the possibility of combining them with OCR capabilities. Section 3 contains a detailed description of the approach we present in this paper. It includes a description of the process of generating legal text corpora for the Serbian language along with a description of the key components: a BERT-based language model for Serbian legal texts, a word-level similarity measure, and OCR engine usage. An evaluation of the approach is presented in Section 4 which demonstrates the capabilities of our approach determined based upon publicly available scanned legal documents containing Serbian legislation. In Section 5, we conclude with an outlook on enhancements we will implement in future research and development.