esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Obtained by Experiment and Their Formal Interpretation
#### 2.2.1 Statistical Analysis of Experimentally Obtained Results  
There were complications during the experiment in determining stress increments or drops \(\Delta\sigma_{t}\) and \(\Delta\sigma_{m}\) at the measurement point G1 to G3 due to pressure change \(\Delta p\). For technological and operational reasons, the pressure in the pipe \(p\) at time \(t\) could not be changed in the overall range from 0 to \(p_{max}\), but only in a relatively small range of 5.67 MPa to 6.32 MPa. In this way, discrete values were obtained experimentally in the form of \(n_{1}\) ordered pairs [\(p_{i}\), \(t_{i}\)], \(n_{2}\) ordered pairs [\(\Delta\sigma_{tt}\), \(\Delta p_{i}\)] and \(n_{2}\) ordered pairs [\(\Delta\sigma_{mi}\), \(\Delta p_{i}\)], for \(n_{1}=18\) and \(n_{2}=20\). The stresses \(\Delta\sigma_{ti}\) and \(\Delta\sigma_{mi}\) were recalculated from the data measured with strain gauge rosettes. Considering the type, nature, and character of the obtained results and with regard to their objective interpretation, it was appropriate to process them by statistical means, in particular by regression and correlation analysis methods.  
From the point of view of statistics, the specified ordered pairs \(n_{1}\) and \(n_{2}\) form the multivariate statistical files. The question of finding, investigating, and evaluating the connection or dependence between two quantitative statistical features is addressed by statistics using regression and correlation analysis. Regression analysis addresses the task of detecting the dependence and expressing it by mathematical means, i.e., the regression function. In an extreme case, it solves whether there is any dependence or connection at all. Correlation analysis addresses the task of determining the tightness of dependence or the degree of intensity with which the dependence being regressed manifests itself among the various interfering secondary factors, or assesses the degree of correctness of the regression analysis performed.
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 2 The Transmitting and Receiving System for the Composite Signal  
In this section, we present the composite signal model and introduce the structure of the transmitting and receiving systems.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 3 Architecture Proposal
### DTN Module  
The DTN Module is a userspace-executed logical entity that encapsulates all the essential components required to augment IPv6 with the capabilities necessary to support DTN communications. Although its internal implementation may vary depending on specific use cases or environments, its functionality can be abstracted at a macro level. The module must implement all the features necessary for storing packets, thereby enabling the store-and-forward functionality essential for DTN. In addition, it must incorporate  
Figure 1: The DTN-enabled node components.  
the logic required to process incoming signaling traffic related to DTN operations and, when necessary, generate new signaling messages. This combination of packet storage and signaling processing ensures that the node can effectively participate in DTN communications while maintaining compatibility with existing IPv6 infrastructures. Figure 2 shows the elements of the DTN Module and a non-exhaustive example of the most fundamental interactions between them.  
For the internal implementation of these functions, it is suggested to follow a Network Function Virtualization [25; 26] approach, and to implement them as independent and heavily specialized entities. Based on this methodology, three primary candidates have been identified to fulfill the key functionalities of the module: the DTN Controller, responsible for overseeing and coordinating DTN operations; the Routing Function, which manages dynamic routing decisions to support intermittent and delay-prone links; and the Storage Function, which provides the necessary buffering capabilities to support the store-and-forward paradigm. Figure 2 shows the interactions between the DTN Controller and the rest of the architecture elements and functions.
## 5 Conclusions and Future Plans
Since the MSA-LMS algorithm proposed in this paper has excellent performance for signals with complex frequency spectra, it can be applied to active vibration and noise control systems for automobiles, aircrafts, ships, and other mechanical systems. In particular, it can be applied to next-generation powertrains with periodic characteristics, air duct structures with openings, rotors and power transmission devices of rotor blade aircrafts, and complex transmission paths with high frequency spectrum. Future work includes the stability proof of the proposed algorithm as well as the validation of this algorithm with more realistic signals (such as modulated signals and measured signals from real mechanical devices), in order to be applied directly to real life applications. [PERSON], [PERSON], and B.K. initiated and developed the ideas related to this research work. [PERSON], [PERSON], and B.K. developed novel methods, derived relevant formulations, and carried out performance analyses and numerical analyses. [PERSON] wrote the paper draft under B.K. 's guidance and [PERSON] finalized the paper. This paper was supported by the National Research Foundation of Korea (NRF) grant funded by the government of Korea (Ministry of Science, ICT & Future Planning) (No. 2016R1C1B1010888). The authors declare no conflict of interest.
# Correlation of Plastic Strain Energy and Acoustic Emission Energy in Reinforced Concrete Structures
## 1 Introduction  
Reinforced concrete (RC) structural elements develop several damage mechanisms when subjected to static/dynamic, monotonic/cyclic loadings. Before the onset of yielding of the reinforcing steel, concrete damage is associated with: (i) the opening of new cracks or the extension of existing ones when the tension stress exceeds the tensile strength of the concrete; (ii) the friction between the planes of fracture after cracking; or (iii) the concrete cracking under compressive stress. When cyclic loading is caused for example by earthquakes, concrete degradation due to cracking eventually results in cumulative damage (low-cycle fatigue damage) to the structural RC components and leads to a state in which repair becomes necessary. One serious consequence of concrete degradation under cyclic loading is the slip between the reinforcing steel bars and the surrounding concrete.  
Evaluating this damage in concrete is not easy because RC structures are commonly covered up by non-structural elements (brick veneers, casings, cement plasters, stuccos, _etc._). Such elementsmake simple visual inspection complicated. Moreover, visual inspection provides only qualitative information on the \"apparent\" damage, but no information on the \"cumulative\" damage resulting from numerous cycles of imposed deformations. It is here where non-destructive techniques play an important role. Among them, the measurement, recording and analysis of Acoustic Emission (AE) signals prove very effective [1; 2; 3; 4]. When applying AE techniques to damage evaluation, it is of paramount importance [5; 6; 7; 8; 9; 10; 11]: (i) to discriminate relevant signals from spurious AE records; and (ii) to correlate AE with a well-established index of mechanical damage. The AE may be characterized in terms of the so-called MMSE (Mean Measured Area under the Rectified Signal Envelope) energy [2]. Meanwhile, the energy dissipated by a structure through plastic strain deformations (irreversible deformations) is commonly recognized as a good indicator of damage. This paper summarizes several research initiatives that prove there is a very good correlation between AE energy calculated from the AE signals, and the plastic strain energy dissipated by concrete. Based on this correlation, the level of damage in an RC structure can be assessed from the AE records. The correlation is demonstrated under relatively simple loading conditions, _i.e._, static and monotonic loadings [12; 13], yet also under extremely cumbersome loads such as random dynamic cyclic loading caused by earthquakes [13; 14; 15; 16; 17; 18; 19]. The AE signals recorded during a seismic event are extraordinarily complex; and unveiling their relation with the damage accumulated on the structure requires considerable post-processing work [13; 16; 18; 19; 20].
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
###### Abstract  
Vehicle -to-vehicle (V2V) communications are crucial for enhancing road network safety and efficiency. With the increasing demand for bandwidth in V2V services, exploring innovative solutions has become imperative. This study explores a comparative analysis of mmWave and WiFi transmission technologies, with a specific focus on line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios in both 2D and 3D modeling environments. The use of stochastic geometry tools allows a realistic modeling of the random positioning of vehicles within the V2V system framework, resulting in accurate expressions for the successful transmission probability (STP) and average throughput (AT) for both communication systems. To validate our analytical findings, Monte Carlo simulations have been employed, offering a comprehensive evaluation of mmWave and WiFi performance. Simulation results highlight that mmWave systems outperform in scenarios with short transmission distances and low vehicle density while WiFi systems demonstrate greater efficiency for longer transmission distances.  
V2V communication; three-dimensional system; mmWaves; successful transmission probability; average throughput +
Footnote †: journal: Electronics Electronics  
18 February 2024  
Revised: 29 March 2024  
Accepted: 1 April 2024  
Published: 3 April 2024
# Particle Formation Mechanism of TiCl\({}_{4}\) Hydrolysis to Prepare Nano TiO\({}_{2}\)
## 4 Results and Discussion
### Effect of Reaction Temperature on Particle Formation Behavior  
The impact of varying reaction temperatures, from 15 \({}^{\circ}\)C to 55 \({}^{\circ}\)C, on particle formation was explored under specified conditions: 15 mL of H\({}_{2}\)O, 30 mL of C\({}_{2}\)H\({}_{5}\)OH, a pH of 7, and a 60 min duration. Interpretations drawn from Figures 4 and 5 yield the following insights. As per the data from Figure 4, the highest concentration of nuclei is observed at 25 \({}^{\circ}\)C, with a maximum particle count of 195 per cubic micron. Below this temperature, there is an apparent lack of nucleation. Conversely, above 25 \({}^{\circ}\)C, the nucleation period shortens, and the quantity of nuclei diminishes. This trend becomes increasingly pronounced with rising temperatures. A further analysis of the findings from Figure 5a,b reveals that at a reaction temperature of 25 \({}^{\circ}\)C, the D\({}_{50}\) value is 2.44 nm. The Particle Size Distribution (PSD) curve within the 0-15 nm range centers vertically at 2.44 nm, indicating a normal distribution and a notably concentrated particle size. The mass fraction at D\({}_{50}\) hits a peak at 6.2%. At 15 \({}^{\circ}\)C, despite the low temperature favoring heat dissipation from the reaction, thereby boosting hydrolysis, quick crystal growth occurs. This rapid growth leads to increased viscosity in the solution, consequently inhibiting nucleation and growth. Consequently, at this temperature, the D\({}_{50}\) value increases, the mass fraction shrinks, and the particle distribution spreads out, with a D\({}_{50}\) value of 31 nm and a mere 0.8% mass fraction. When the reaction temperature exceeds 25 \({}^{\circ}\)C, a pattern emerges: the higher the temperature, the faster the hydrolysis reaction. The resulting shortened nucleation and growth periods cause particle sizes to enlarge and the distribution to broaden, with the mass fraction reducing accordingly.  
Figure 3: Variation curve of TiO\({}_{2}\) particles with reaction time.
## 3 Results
### Characterization of The Synthesized AgNPs
#### 3.3.4 Energy Dispersive X-ray (EDX) Analysis  
For highlighting the presence of silver, the EDX spectra of the obtained nanoparticles were recorded and can be found in Figure 13.  
Figure 11: Comparative FTIR spectra of initial extracts and of their derived nanoparticles: AgNPs-H\({}_{2}\)O (**A**) and AgNPs-EtOH (**B**).  
Figure 12: TEM images of AgNPs-H\({}_{2}\)O (**a**) and of AgNPs-EtOH (**b**).  
The elemental composition of the synthesized AgNPs analyzed by EDX showed in both cases the signal peak at 3 keV corresponding to Ag, but also signals corresponding to other elements, mainly to C, O, and N.  
For the two samples taken into study, the proportions in which the elements can be found is different. Consequently, for AgNPs-H\({}_{2}\)O, the following proportions were observed: Ag--77.23%, C--10.35%, O--6.59%, and N--1.32%; while for AgNPs-EtOH, Ag represents 13.4%, C--69.81%, O--10.84%, and N--5.57%.
## 2 Methods and Data
### Data Source and Pre-Processing
#### 2.2.7 ESMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6)  
In this study, 22 ESMs from the CMIP6 project were employed to predict the future trend of LAI and GPP. The data of LAI and GPP from 22 CMIP6 ESMs have a spatial resolution of 10-100 km and a monthly temporal resolution, and both of these datasets range from 2015 to 2100. Four future Shared Socioeconomic Pathways (SSPs) scenarios were SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, in which SSP1-2.6 is the sustainable development pathway scenarios, SSP2-4.5 represents the middle development pathway between low and high emissions, SSP3-7.0 refers to the scenarios of the regional competition development pathway, and SSP5-8.5 is named after a scenario of a high-emission development pathway [14; 50]. The data were freely obtained from the official website of the World Climate Research Programme (WCRP, [[https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)]([https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)), (accessed on 25 August 2023)).
# A Dual-Gas Sensor Using Photoacoustic Spectroscopy Based on a Single Acoustic Resonator
## 4 Conclusions  
In this paper, we proposed a PAS-based dual-gas detection method, which combines two DFB lasers at different far wavelengths. By adjusting the current and temperature of the two DFB lasers, the absorption lines of NH\({}_{3}\) and CH\({}_{4}\) were recorded on the left and right halves of one periodic current scan. Since the generated acoustic waves are indistinguishable for the microphone due to the light absorption of different gases, the two gases can be distinguished by time discrimination. In this way, an identical current signal was used to control the two DFB lasers. The experiments showed that the linearity of methane and ammonia can both reach 0.999, and the detection limit can reach 0.3 and 0.1 ppm, respectively, in an integration time of 100 s. According to the American Conference of Industrial Hygienists, the maximum permissible exposure limit of NH\({}_{3}\) is 25 ppm. Thus, the NH\({}_{3}\) limit of detection (LoD) of 0.1 ppm for our PAS sensor is sufficient for this industrial application. The portable PAS instrument is suitable for detecting gas leakage in the chemical industry and environmental protection. In the case of a tight power budget, to avoid the 3 dB loss of the power combiner for the two laser beams at 1512 and 1653 nm, the beam splitter shown in Figure 1 can be replaced with a WDM (wavelength division multiplexer) device [34].  
Figure 9: (**a**) Continuous measurement of CH\({}_{4}\) for 4000 s. (**b**) Continuous measurement of NH\({}_{3}\) for 4000 s. (**c**) Allan deviation plots of CH\({}_{4}\) as a function of integration time. (**d**) Allan deviation plots of NH\({}_{3}\) as a function of integration time. (**e**) Concentration distribution of the retrieved CH\({}_{4}\) with a concentration of 46 ppm with Gaussian profile fit. (**f**) Concentration distribution of the retrieved NH\({}_{3}\) with a concentration of 270 ppm with Gaussian profile fit.  
Author Contributions: [PERSON] and [PERSON] designed/modified the experimental setup. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] wrote the software. [PERSON] and [PERSON] wrote the manuscript. [PERSON] supervised the work and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Zhejiang Province (grant number 2021C03178), NingboTech University (grant number 1141257B20200538), the Ningbo Science and Technology Plan Project-Key Core Technology Emergency Tackling Plan Project(2020G012), the Zhejiang University NGICS Platform and the National Natural Science Foundation of China (11621101).  
Not applicable.  
The data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors are grateful to [PERSON], [PERSON], [PERSON] and [PERSON] for valuable discussion and help.  
The authors declare no conflict of interest.
## Appendix A  
The following appendix contains a list of results for primary energy savings, implementation cost, assumed lifespan, and the resulting cost-effective parameter (CEP) for single renovation actions, proposed in [37]. The Net Present Value (NPV), resulting from the LCC analysis presented in Figure 2, is also shown for each action. For ease of the reader, each building part is presented in a separate table, accompanied by a brief explanation of the considered renovation action types and their variations. The names of the actions are kept as presented in [37] and are consistent with the accompanying data files provided in the Data Availability Statement. The name consists of an abbreviation of the building part, followed by a number and letter, representing the type of solution and variation of insulation thickness (energy class for windows), respectively. Table A1 shows data for external wall solutions. Three different renovation types consider removal of the existing lightweight facade and external insulation of the load-bearing brick wall. * External wall type 1--Insulation, new lightweight timber construction, and wooden plank facades. * External wall type 2--Insulation, new external brick facade. * External wall type 3--Commercially available ROCKWOOL REDArt solution. Detailed calculations of the implementation costs presented in Table A1 can be found in the Data Availability Statement. Investigation of attic renovation is examined for two overall scenarios. The first is a scenario where the function of the attic is kept the same (common storage and drying facilities). This case considers removal and re-establishment of the wooden floor and a number of insulation types and thicknesses, which are all presented in Table 14. The results for the second scenario presented in Table 14 include the same insulation types and thicknesses but exclude re-establishment of the floor. This changes the space to unusable but reduces cost considerably. The different attic renovation types are based on the type and class of insulation as follows:  
* Attic 1--Flexi batts insulation class 37: a 70--h 245 mm
* Attic 2--Flexi batts insulation class 34: a 95--c 145 mm
* Attic 3--Rolled batts insulation class 37: a 38--d 170 mm
* Attic 4--Blown-in granule of mineral wool class 40: a 70--d 150 mm
* Attic 5--Blown-in granule of paper wool, class 40: a 50--d 150 mm
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 3 Results
### Protocol Composition
#### 3.1.2 Specification of the Causal Protocol
In this case, this dependency can be removed from \(CI\). A message received by a super peer that originated from an external group generally carries dependencies on other messages from an external group, as well as dependencies on messages from an internal group. The dependencies on messages from an external group are represented in a form of pairs (process identifier, and message dependency), but to be interpreted correctly in an internal group, they should be transformed to a bit vector form (see Listing 15). In addition, a super peer can receive messages that contain dependencies on messages that are not yet received. To deal with this, a super peer checks the message delivery condition as described previously to ensure that this message can be delivered by a super peer, and only then, transforms it to ensure that all of the message dependencies are resolved. This does not affect the message order in any way. If a super peer does not receive message \(m\), none of the peers in an internal group have received this message \(m\). Thus, a message \(m^{\prime}\) that requires \(m\) to be delivered before it cannot be delivered to any peer in an internal group. \(DV:=m_{ext}.Cl[id_{ext}]\) // Bit vector  
For each \(<id\), \(dep>\) in \(m_{ext}.Cl\)  
If (\(VTx[id]\) is an integer)  
If (exists \(<in\), \(out>\) in \(TT[id]\) where \(in=dep\)) then  
\(DV[out]:=1\)  
End if  
Else  
For each 1 in \(dep\) in position \(i\)  
If exists \(<in\), \(out>\) in \(TT[id]\) where \(in=i\) then  
\(DV[out]:=1\)  
End if  
End for  
\(m_{int}=(0,\)\(VTx[id_{ext}]\), \(0\), \(DV\), \(m_{ext}.Data)\)  
If \(TT[m_{ext}.id]\) is not empty and \(VTx[m_{ext}.id]\) is an integer then  
\(m_{int}.Last:=TT[m_{ext}.id][\)last\(].out\)  
End if  
\(TT[m_{ext}.id]:=TT[m_{ext}.id]\cup<m_{ext}.SN\), \(VTx[id_{ext}]>\)  
\(I[VTx[id_{ext}]]:=1\)
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 4 Precise Statement and Solution of the Discrete-Time Control Problem  
In order to design a suitable discrete-time controller, we extend the ideas of the continuous-time general regulation theory (see, e.g., [13; 14]). The essential feature of this approach is the assumption that the reference signal is generated by a known dynamical system, called the exosystem. This system is aggregated with the plant model. We first derive the continuous-time exosystem, then we develop the discrete-time exosystem by applying the same method as in the case of the plant.
## 4 Experiment
### Parameter Setting  
We trained our model using the Adam optimizer with a learning rate of \(1\times 10^{-4}\), a batch size of 64, and 20 epochs. The number of embedding dimensions was set to 64, and we used a two-head multi-head attention mechanism.  
**Threshold Selection** To construct the region-level graph, we applied rule-based clustering with spatial and temporal thresholds selected to balance granularity and interpretability. A stay point ratio of around 50% offers a practical trade-off--aggregating about half of the check-ins ensures structural richness without overgeneralization. For NYC, we used \(D_{\text{thresh}}=3000\) m and \(T_{\text{thresh}}=900\) min (49.98%, avg. 48.3 per user), and for TKY, we used \(T_{\text{thresh}}=1020\) (50.01%, avg. 88.3) and the same spatial threshold.  
**Global-Level Clustering** We applied a spatio-temporal DBSCAN algorithm with time_weight = 0.5, eps = 0.05, and min_samples = 20. These parameters were selected to produce fewer global-level clusters than region-level ones, maintaining a clear hierarchical structure. The resulting clusters were both temporally and spatially consistent, averaging 11.67 per user with a temporal standard deviation of 4.07 days.  
We summarize the average training time per epoch, total training time, and peak GPU memory consumption for each dataset in Table 2. The experiments were conducted with an NVIDIA RTX 4090 GPU. The results show that our model is both computationally efficient and scalable to large-scale datasets.