esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of SAR Average in Rats during 5G NR Chronic Exposure
## 2 Materials and Methods
### Numerical Simulations  
To evaluate the SAR under the approximated experimental conditions presented in Figure 3, numerical simulations of the experiment were carried out for 2 and 10 rats (Figure 4) in the CST Microwave Studio software package. A primitive 3d rat model was used from [29] as a rat phantom, with the dielectric parameters of blood (\(\epsilon^{\prime}=58.34\) and \(\epsilon^{\prime\prime}=18.76\) ).  
The rat phantom was 16 cm long and weighed 180 g. In the real world, a rat is a heterogeneous body, which is composed of many types of tissues. We believe that it is sufficient to use a homogeneous dielectric model instead of an heterogeneous one for qualitative evaluation. In addition, the characteristics of the simplified phantoms, in terms of body mass to the maximum length ratio, are close to the ratio of the experimental animals.  
The cage size was set to 480 \(\times\) 375 \(\times\) 210 mm, with a wall thickness of 10 mm (\(e=3\) and \(tg(\epsilon^{\prime\prime}/\epsilon^{\prime})=0.001\), polycarbonate). On top, the case was closed with a metal lid (cage top equivalent) and a metal shielding on the back side. The distance of the shielding to the nearest phantom is 2 cm. The shortest distance from the antenna to the shielding is 43 cm.  
The EMF was calculated using the default finite integration method in the time domain of the CST microwave studio package. The algorithm IEEE/IEC 62704-1 was used to calculate SARs. This is an accepted averaging method according to IEC/IEEE 62704-1:2017 on \"Determination of Peak Spatial Averaged Specific Absorption Rate (SAR) in the Human Body from Wireless Communication Devices, 30 MHz to 6 GHz--Part 1: General Requirements for Use of Finite-Difference Time Domain Method (FDTD) for SAR Calculations\" [2,3,25].  
The mesh partitioning in the case for 10 rats was 226,735,740 cells and for two rats was 65,486,652 cells. All calculations were performed on AMD Ryzen 7 3800X (8-core) 3.89 GHz CPU and NVIDIA GeForce RTX3060 GPU.  
When the cages were located in front of the antenna (Figure 5), its VSWR did not exceed 1.8 in the operating frequency range (Figure 6). The values of the dielectric parameters for the frequency of 2.4 GHz were also used from the tables of the software package, which were consistent with the ones generally accepted.
## 4 Speaker Tracking
### Speaker Identification  
The second stage of our speaker tracking system performs a conventional speaker identification test on the segments and target speakers as shown in Figure 3. The goal is to answer _to which target speaker, the segments belong?_ We propose the use of RBM vector representation for both the target speakers and segments generated in the segmentation stage. The MFCC features are extracted both for target speakers and segments. Then, RBM vectors are extracted and all the segments are tested against target speakers using cosine and PLDA scoring. Assume that \(S_{Tm,Sn}\) represents the cosine/PLDA score for testing the target speaker \(T_{m}\) against the segment \(S_{n}\). For a segment under test, first we select a potential candidate among all the target speakers. The target speaker with the maximum score is a potential candidate for the segment. Then, if the maximum score is greater than a threshold, the identity of that target speaker is assigned to that segment. Generally, the identity of the target \(T_{m}\) is assigned to the segment \(S_{n}\) according to:  
\[Id_{S_{n}}=\operatorname*{arg\,max}_{m}(S_{Tm,Sn})\quad\text{if}\quad max(S_ {Tm,Sn})>\lambda \tag{8}\]  
where \(\lambda\) is a threshold to decide whether the segment under test does not belong to any of the target speakers. If the score is less than \(\lambda\), the segment is not assigned to any of the target speakers. This is reflected as a Missed Speaker Time (MST) error for the target speaker which the segment actually belongs to. There are no speakers that should be rejected by the system because we consider all the speakers as possible target speakers. We have performed experiments with different values of \(\lambda\) in order to analyze the effect of the proposed RBM vectors at all possible working points.
# Expected Impacts of the Massive Increase in Electric Vehicles in Slovakia
## 1 Introduction
### Energy Balance in Slovakia  
In terms of electricity consumption, Slovakia is the smallest market compared to the surrounding countries. Since 2008, the Slovak Republic has been a net importer of electricity, but after the launch of new units 3 and 4 of the Mochovce Nuclear Power Plant, it will become an electricity exporter. The construction of new units represents the largest private investment in the history of Slovakia. The completion of units 3 and 4 of the nuclear power plant in Mochovce (MO34) is unique not only in Slovakia but also in the EU, where only four new nuclear units are currently being built--two of them in Mochovce. The output of each unit will be 471 MWe and one reactor will cover 13% of electricity consumption in Slovakia [1]. The annual production of completed units will save more than 7 million tons of CO\({}_{2}\) emissions, which will significantly contribute to the EU's plan to be carbon neutral by 2050. The construction project is already finalized and the deadline for the initial introduction of nuclear fuel into the reactor is approaching.  
The estimate of the development of electricity consumption assumes its approximately 1.2% year-on-year increase by 2030. In such a case, the consumption within the Slovak Republic will reach 35.2 TWh in 2030, while up to 87% of electricity will come from low-carbon sources [2]. Electricity has a specific position within energy sources (1). This position is due to the fact that the growth of its production and consumption may not be accompanied by a negative impact on the environment, as is the case with other types of fuels and energy. Electricity can be considered clean if it is produced and consumed with high efficiency, if it replaces the production of energy from the combustion of low-energy fuels or if it is produced from renewable energy sources (Figure 1).
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 2 Materials and Methods
### Usability of WSN Technology during Product Assembling  
Sensor networks contain several wireless sensing devices, which communicate via wireless technology. This communication is usually performed by IEEE 802.15.4 standard, which is also known as Zigbee technology. This communication protocol works at 2.4 GHz, and the communication is point-to-point.  
The structure of the assembly line can be described as a series of work stations at which the assembling process is performed [17]. Along the assembly line, the assembling process runs sequentially (Figure 3).  
At first workstation, the base part is placed, usually in equal time intervals. After the last workstation, the product is fully assembled. To be able to use WSN technology for monitoring purposes, the sensor has to be mounted on the base part of the assembly. Therefore, the usability of WSN technology largely depends on the product itself, i.e., on the overall dimensions of the product. Due to the fact that nowadays wireless sensors are active instruments, i.e., they have their own power supply, their own dimensions are relatively large relative to, for example, RFID tags. Therefore, one of the criteria for implementing WSN is that the base part should be larger than the sensor. Today, the smallest available sensor modules have a diameter of 25 mm (e.g., \"Mica2 dot\") [22]. Generally, for appropriate use of WSN, the base part has to be twice the size of the sensor.  
During the research, Crossbow Iris wireless node was used for the proposed model. It uses 2.4 GHz frequency using the IEE 802.15.4 standard and is using the Zigbee communication protocol stack [22]. The Iris sensor nod can be used for different applications, because it has a 51-pin connector that allows for expansion of the base with different sensor units, i.e., temperature sensor, photo sensor, microphone, accelerometer, magnetic sensor, humidity sensor, pressure sensor, etc.  
The sensor, which is mounted on the base part of the product, must not interfere with the defined assembling procedures performed either by humans or by manipulators and robots. Therefore, part lists and other documentation prepared for the assembling process must be thoroughly analyzed to be able to define the right position for mounting the sensor at the first workstation.  
By this, the sensor becomes an integral part of the product during the assembling process. At the end of the process, the sensor has to be un-mounted. Therefore, not only the mounting procedure, but also the un-mounting one has to be defined to be as simple and fast as possible, for minimizing the influence on the assembling procedure. Another  
Figure 3: Assembly line structure example.  
possibility is to integrate the wireless sensor module into the base part holder. By this the mounting and un-mounting of the sensor are avoided, but the holder becomes more complex.
## 4 Dynamic Analysis of the PIM System
### Sensitivity Analysis of the Boundary Conditions of System Parameters  
The primary factors affecting the outcome of deploying large floating structures using a catenary method include water depth, as increased depth amplifies the impact of water  
Figure 12: (**a**) Time history diagram of horizontal velocity at the structure’s center of mass. (**b**) Vertical velocity at the center of mass.  
pressure and buoyancy on the object. Additionally, sea conditions such as currents and waves can influence the descent path and velocity of the object. The weight and shape of the floating structure determine its motion characteristics in the water. Heavier or irregularly shaped objects may experience more complex dynamic behaviors. The flexibility, elasticity, and length of the cable directly affect the catenary deployment process of the object. Longer cables may bend or kink due to their own weight and the influence of water currents, affecting the accuracy of the deployment. Therefore, this study conducts a sensitivity analysis on these key factors.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels
### Analysis of the Influencing Factors of Earthquake Disasters in Portal Sections of Highway Tunnels
#### 3.2.2 Portal Cracking and Open Cut Tunnel Breaking  
Portal cracking and open cut tunnel breaking are caused by the direct action of seismic force and the indirect action of secondary damage caused by soil collapse on the side slope. The structure of Gengda Tunnel was damaged due to the excessive length of the open cut tunnel and the large seismic inertial force during the Wenchuan Earthquake. The Baiyunding Tunnel and Taoguan Tunnel are located on thick, covered soil. When seismic waves pass through the thick soil covering the tunnel portal section, the tunnel portal section bears more seismic inertial force, resulting in end wall cracking, fracture and arch ring damage. Longxi Tunnel tends to collapse and slide due to the extroversion trend of the side slope under the action of earthquakes. Under the combined action of the active earth pressure and the seismic inertial force behind the wing wall, the external force on the portal structure and open cut tunnel structure is greatly increased. In addition, the structure of the tunnel portal and open cut tunnel is plain concrete, with low tensile and shear strength, which causes shear and tensile damage. Moreover, the tunnel entrance section and exit section are located in the landslide area of the side slope, and the impact of a large number of falling stones causes damage to the tunnel portal structure. The portal section of the Taoguan Tunnel is located at the interface between the soft and hard rock layers. The structure of this section is affected by the forced displacement, seismic inertial force and terrain bias during the earthquake, which causes the tunnel portal to crack. Moreover, the structure of the portal section is weakly constrained by mountains during an earthquake, resulting in a \"whipping effect\", and the large structural deformation causes damage to the open cut structure.  
As shown in the above cases, the tunnel entrance and section exit section are located in an engineering environment with large risks and hidden dangers, such as landslide areas and rockfall areas, and it is easy to crush the structure after side slope collapse. The side slope collapse increases the earth pressure of the portal structure. The special properties of the surrounding rock of the portal section lead to the expansion of the effect of seismic force on the portal section, resulting in the cracking of the tunnel portal and the destruction of the open cut tunnel, which are engineering geological factors. The irrational design of tunnel portal structures increases the influence of seismic shear force. The structure of the tunnel portal and open cut tunnel is plain concrete with low tensile and shear strength, which easily causes damage under the combined effect of seismic inertial force and earth pressure, which are tunnel structural factors.
# Modified Pseudo-Dynamic Bearing Capacity of Shallow Strip Foundation near Slope on Non-Uniform Soil
## 4 Results and Analysis
### Convergence Analysis  
As presented above, \(n\) represents the number of wedge blocks. According to previous studies, when the number of wedge-shaped blocks is higher, the bottom rupture surface of the failure mechanism is smoother, and the calculated bearing capacity is more accurate. To take an appropriate value of \(n\), this subsection carries out a convergence analysis of the foundation bearing capacity with respect to \(n\), as shown in Figure 5. The specific values of the parameters are \(B_{0}=1\) m, \(\gamma=20\) kN/m\({}^{3}\), \(c=10\) kPa, \(q=10\) kPa, \(\varphi=30^{\circ}\), \(\beta=20^{\circ}\), \(k_{h}=0\), where seismic forces are not considered.  
Figure 5 illustrates the trend of foundation bearing capacity with the number \(n\) of wedge blocks. The bearing capacity decreases with increasing \(n\) until it stabilizes. There is a sudden decrease in the bearing capacity between \(n\) equal to 3 and 7, and when \(n\) is more than 7, the bearing capacity performance approaches a steady state. Therefore, the bearing capacity has convergence for \(n\). In addition, when \(n\) increases from 2 to 20, the bearing capacity decreases by about 18%, whereas when \(n\) increases from 20 to 30, the bearing capacity only decreases by about 0.04%. Therefore, to balance the accuracy and efficiency of the calculation results, in this paper, \(n\) is taken to be equal to 20.
## 3 Numerical Simulations
### Computational Models  
Figures 6 and 7 display the schematic of the computational models according to the design. The near-field heat extraction zone is composed of surrounding rock and a tunnel with an annular structure located at the center of it. The cold working fluid flows in the annulus of the tunnel, and it absorbs geothermal energy from the surrounding rock. In order to efficiently represent the long tunnels, the 1D non-isothermal pipe flow model was adopted [22; 23]. The accuracy and efficiency of the 1D model for long tunnels were verified by an experiment in a longitudinally ventilated high-temperature tunnel [24; 25]. The computational model of the near-field heat extraction zone has a radius of 200 m and a length of 2000 m.  
Figure 5: Flowchart of heat transfer in EGS-E.  
Figure 6: Numerical discretization of the far-field zone for heat extraction.  
Figure 7: Numerical discretization of the near-field zone for heat extraction.  
The model of the far-field heat extraction zone consists of a stimulated reservoir volume (SRV) and its further surrounding rock. Notably, the modified HDR is known as SRV, which has a high porosity and permeability [26; 27]. The SRV is created by blasting or caving instead of hydraulic fracturing. The SRV is simplified as a cylinder with a length of 2000 m and a diameter of 60 m, located in the center of the surrounding rock. The computational models are sufficiently large to reduce the boundary effects during the geothermal energy extraction, which are demonstrated in subsequent studies. Moreover, other detailed parameters of the two computational models are shown in Table 1.
## 5 Conclusions  
In the presented study, a laser-based stereolithography technique was implemented, using an 465 nm industrial laser not typical for SLA technology to study the behavior of BT-based ceramic pastes in the process of photopolymerization. A series of tests was conducted concerning different process parameters. The achieved polymerization thickness was 10 times greater than provided by the previously existing technology. The BT powder loading in the ceramic paste was kept at the high 55 vol % level. The spectral change in SLA processing from 355/405 nm to 465 nm significantly improved the efficiency of the 3D-printing behavior of BT ceramics.  
Figure 6: (**a**) Refractive index of BT, extrapolated from [11], and (**b**) light transmission spectra of BT, recomputed from [12].  
The low light absorption of BT at wavelengths greater than 400 nm is a favorable factor for ceramic paste photopolymerization. Thus, a comprehensive study of the material's optical characteristics is often needed to formulate an efficient recipe for stereolithography-based 3D printing. Further research would be focused on the improvement of the experimental setup with a galvano-scanner unit instead of a robotic arm to increase the laser marking speed from 10 to 1000 mm/s and with additional optics to focus a laser beam to a small spot. Such an upgrade would allow for the usage of focused unmasked laser beam at large marking speeds to quickly 3D-print piezoceramic items with a good resolution. The parametric studies of polymerization process (including polymerized dots, lines, layers, and multi-layered structures) will be conducted in detail. Samples' microstructure will be studied, and thermal processing effects will be considered.  
Conceptualization, S.C. and A.T.; methodology, S.C., A.T. and O.D.; software, O.D.; validation, S.C., A.S., A.K., A.T. and O.D.; formal analysis, S.C., A.S. and I.S.; investigation, A.T., O.D. and A.K.; resources, S.C. and I.S.; data curation, S.C., A.T. and A.S.; writing--original draft preparation, S.C., A.S., A.K. and I.S.; supervision, S.C. and I.S.; funding acquisition, I.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation, grant number #20-19-00780.  
Not applicable.  
Data Availability StatementData available on request to the corresponding author.  
The authors declare no conflict of interest.
## 6 Conclusions  
This study developed a remote collaboration system based on MR. This system consists of three-unit systems: (1) Field Operator System, (2) Communication Server, and (3) Office Manager System. FOS was developed based on HoloLens 2 and consists of an MR Simulation module, Office Manager Information Management module, and FOS Communication module. Communication Server consists of Application Server and Relay Server and transmits and manages information between FOS and OMS. Finally, OMS for an office manager consists of a Field Operator Information Management module and an OMS Communication module. This paper confirmed the applicability of BIM and MR-based remote collaboration system.  
The limitations of this study are as follows. First, in the current system, auditory and visual information is transmitted from FOS to OMS, but only auditory information can be transmitted from OMS to FOS, which limits two-way communication. Additional methods need to be developed to enable an office manager to give instructions in a way that includes visual information. As demonstrated in several studies, the effects of visual communication cues improve remote collaboration efficiency [18; 19]. For example, if the text or annotation input by the office manager is displayed on the FOS view, the efficiency of remote collaboration will increase. Second, only FOS users can manipulate BIM virtual objects in MR2 CS. OMS users only give instructions and cannot move or rotate the BIM objects. However, if OMS users can manipulate BIM objects, more effective communication will be possible. That way, remote collaborators can manipulate the same virtual object simultaneously. Lastly, a method to quantitatively evaluate the effectiveness and efficiency of the system needs to be presented. In this study, we received surveys from the testers for qualitative evaluations; however, a quantitative evaluation method should be introduced in further studies.  
**Author Contributions:** Conceptualization, S.K. ([PERSON]) and S.K. ([PERSON]); methodology, S.K. ([PERSON]), S.K. ([PERSON]), and N.K.; software, J.C. and N.K.; validation, J.C. and S.K. ([PERSON]); writing--original draft preparation, S.K. ([PERSON]); writing--review and editing, J.C., N.K. and S.K. ([PERSON]); visualization, S.K. ([PERSON]) and J.C.; project administration, S.K. ([PERSON]); funding acquisition, S.K. ([PERSON]) and S.K. ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Incheon National University research fund (funding number: 2019-0195) in 2019.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.