esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, in general practice, medical devices must be at a long-time communication status (as is the case with Electrocardiograph (ECG) monitoring); (c) compared to a general population group, the target population subject to electromagnetic radiation of medical devices is usually more sensitive and susceptible to electromagnetic exposure. Body Area Network (BAN) refers to a system of devices in close proximity to the human body as well as the radiation network formed from the devices, which are located in different body parts for carrying out either active radiofrequency therapy using radio frequency energy or for communication interactions. For instance, the medical sensor applied in wearable BAN is usually stuck on the body surface or implanted to a superficial depth for short-term monitoring or treatment (within 14 days); sensors and actuators used in implanted BAN are installed relatively deeper into the human body, such as the heart, brain, or spinal cord. Enjoying functions of both actual stimulation and physiological status monitoring, implanted BAN is an ideal choice for chronic diseases and monitoring purposes. The medical environment of BAN is an area characterized by high-intensity electromagnetic energy generated by medical devices, where the human body is exposed to electromagnetic waves in close proximity [12]. In contrast to the study in the telecommunication sector, cases are more complicated when the electromagnetic radio frequency energy of medical devices is studied in the context of specific local environments of the human body. Therefore, the study on electromagnetic radiation SAR of BAN medical devices is of representative significance. In view of the foregoing background, in this paper, electromagnetic radiation SAR of medical devices was measured in the following steps: Firstly, a model was set up to study electromagnetic parameters of different tissues when subjected to electromagnetic exposures within the range of 10 MHz-10 GHz with correction measures employed. Secondly, simulated body liquids were formulated and an anthropomorphic phantom was designed on the basis of electromagnetic parameters acquired through calculations. Finally, regular medical devices and wearable medical devices which could actively emit electromagnetic energy were deployed in the anthropomorphic phantom.
# Construction Progress and Aviation Flight Test of BDSBAS
## 3 BDSBAS Service Performance Evaluation  
BDSBAS service performance is described by four types of indicators: positioning accuracy, continuity, availability, and integrity. Combined with the known location reference benchmarks, the system service performance can be evaluated.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 6 Future Directions
### Distributing FL over Multi-EC Enabled Wireless Networks  
Though FL has several advantages in terms of privacy, security, and resource requirements, implementing the FL over resource-constrained, dynamic wireless communication nodes can be challenging, especially in cases of latency-critical applications. Compared to the previous generations, the 6G technology has set high goals, and fulfilling them will be challenging, especially with the traditional centralized FL process. Many researchers in recent times have proposed different FL algorithms by optimizing the various aspects of the FL process. Most of the work done so far is closely related to the FL device selection, finding a suitable averaging process for FL servers, resource allocation, analyzing the tradeoff between FL process performance and resource requirements, and analyzing the tradeoff between global and local FL iterations. Given the availability of various edge computing platforms, a distributed FL process can be useful for implementing the FL over resource-constrained wireless nodes. In particular, the overall FL process can be distributed among several edge computing nodes for limiting the FL communication costs. Figure 10 shows one such case for vehicular networks where the FL process is distributed among terrestrial and non-terrestrial networking platforms. In this scenario, the traditional centralized process can require FL devices, i.e., vehicles, to send their model updates to the centralized nodes, i.e., satellites. This can add larger communication delay and noise during each iteration. Due to this, many devices may not be able to participate in the process due to various reasons, such as insufficient resources, unbearable communication  
Figure 9: Vehicular-communication-based collaborative MARL process.  
delay, and noise. By distributing the FL averaging process over various intermediate layers, these problems can be tackled. However, this could add additional computation delay over the intermediate layers, and proper node selection is required for having better results.
## 3 Results and Discussion
### Application of MISPE
The average particle size of bulk particles from our previous paper is 250 \(\upmu\)m for MIPs and 428 \(\upmu\)m for NIPs, and for precipitation the particle size is 175 \(\upmu\)m for MIPs and 224 \(\upmu\)m for NIPs [7]. The SEM micrograph (Figure 10) showed that the surface of the MIPs was crude and contained countless pores, but the surface of the NIP was smooth and less porous. The irregular, rough, porous surface of the MIPs is most likely formed by the template removal creating the particular sites of the rebinding cavities [67]. The structural properties of the MIPs indicate a higher adsorption capability [68]. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Polymer**} & \multicolumn{3}{c}{**Surface Area (m\({}^{2}\)/g)**} \\ \cline{2-3}  & **Precipitation Method** & **Bulk Method**[7] \\ \hline MIP1 & 254.419 & 42.297 \\ NIP1 & 23.051 & 18.367 \\ MIP2 & 211.486 & 40.674 \\ NIP2 & 35.393 & 6.033 \\ MIP3 & 291.706 & 221.757 \\ NIP3 & 40.216 & 61.381 \\ MIP4 & 230.160 & 141.370 \\ NIP4 & 32.882 & 37.131 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Surface area of MIP and NIP. Figure 9: Chromatogram of SLX in serum pre-treated with MIP 3 and C-18 sorbent. Figure 11 shows the MIP spectrum before extraction (red line), MIP after extraction (black line), and NIP (blue line). All the polymers, which are MIP1-4, MIP after extraction, and NIP1-4, have a similar backbone and are identical. MIP before extraction has different peaks from wavenumber 3300-3500 cm\({}^{-1}\) (N-H stretch) and 3010-3100 cm\({}^{-1}\) (C-H stretch for aromatic benzene ring) indicating the present of SLX, and this peak was not found in MIP after extraction and NIP, indicating the omission of SLX for NIP and the complete removal of SLX as a template in MIP after extraction. Figure 10: Scanning electron microscope images of (**a**) MIP1, (**b**) NIP1, (**c**) MIP2, (**d**) NIP2, (**e**) MIP3, (**f**) NIP3, (**g**) MIP4, and (**h**) NIP4.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 2 The Direct (Physical) Model of Radiation Thermometry
### Thermal Radiation Emission, Transfer and Absorption  
Every black body with a thermodynamic temperature \(T\) emits thermal radiation in the form of spectral radiance \(M_{\text{BB}}(T,\lambda)\) (Figure 2) in accordance with [PERSON]'s law (1),  
\[M_{\text{BB}}(T,\lambda)=\frac{2 hc^{2}}{\lambda^{5}}\Big{[}e^{\frac{hc}{4b_{ \text{B}}T}}-1\Big{]}^{-1} \tag{1}\]  
where \(h\) is the [PERSON] constant, \(k_{B}\) is the [PERSON] constant, \(c\) is the speed of light in the medium and \(\lambda\) is the wavelength.  
The actual emission of any real surface is the emission of the black body (2), spectrally reduced by the spectral emissivity of the surface.  
\[M_{\text{BB,obj}}\Big{(}T_{obj},\lambda\Big{)}=M_{\text{BB}}\Big{(}T_{obj}, \lambda\Big{)} \tag{2}\]  
A general form for radiation of any real object \(M_{obj}(\lambda)\) is described by Equation (3), where \(M_{\text{BB,obj}}\Big{(}T_{\text{obj}},\lambda\Big{)}\) represents object's raw black body emission (2) under an assumption of homogenous temperature; \(M_{\text{env}}(\lambda)\) represents incident environmental radiation which is reflected from the surface of a medium; \(M_{\text{bg}}(\lambda)\) represents spectral radiance behind the object in the case of transmission through a translucent material; and \(\epsilon(\lambda)\), \(\tau(\lambda)\)  
Figure 2: Spectral radiance emitted by the black body, according to [PERSON]’s law (1).  
and \(\rho(\lambda)\) represent spectral transmissivity, spectral emissivity and spectral reflectivity of the object, respectively.  
\[M_{obj}(\lambda)=\underbrace{M_{\text{BB,obj}}\Big{(}T_{obj},\lambda\Big{)} \epsilon(\lambda)}_{\text{obj}}+\underbrace{M_{\text{bg}}(\lambda)\tau( \lambda)}_{\text{transmission}}+\underbrace{M_{\text{env}}(\lambda)\rho(\lambda)}_{ \text{reflection}} \tag{3}\]  
Interaction of incident radiation with a medium at any wavelength is described by the equilibrium Equation (4).  
\[\epsilon(\lambda)+\tau(\lambda)+\rho(\lambda)=1 \tag{4}\]  
According to [PERSON]'s law [24], emissivity \(\epsilon(\lambda)\) is equal to absorptivity \(\alpha(\lambda)\) at individual wavelengths (5).  
\[\epsilon(\lambda)=\alpha(\lambda) \tag{5}\]
## 2 Materials and Methods  
The research was carried out in industrial conditions at a poultry plant in the central-eastern part of Poland. The experimental material consisted of fillets of broiler chickens of the Ross breed, approx. 6 weeks old, whose average live carcass weights were between 1.60 and 2.10 kg. Live birds were delivered to the slaughterhouse from a farm approx. 100 km away from the plant by road. The plant assessed the raw material as having good quality based on its adopted criteria. The experimental slaughter process--using own-construction (OC) and commercial (PLC) devices--was carried out on approximately 1000 live birds (of the same batch, from the same flock) in accordance with the requirements of humane slaughter [6]. Experimental stunning procedures were carried out by employees of the poultry plant with the appropriate level of qualifications to perform activities related to slaughter. A sufficient degree of animal stunning has been confirmed by a veterinarian based on his competence and subjective visual assessment. The production line was able to produce 6000 pieces per hour. The broiler stunning process was carried out using an electric method in a water bath, using two devices:own-construction (OC) [12; 13]: rectangular wave AC, f = 400 Hz, U = 100 V, commercial, Polish company (PLC): sine wave AC, f = 200 Hz, U = 120 V.  
Moreover, the OC stunning method did not subject the live birds to any conditions that are different from other commonly practiced methods.  
The process of experimental and control stunning of broilers was carried out with 5 min breaks between trials.  
On a cooled batch of experimental and control carcasses (air method with water spray, 2 \({}^{\circ}\)C/2 h), industrial cutting into portions, including fillets, was carried out (without cleaning). One hundred pieces of left fillets were randomly taken from the obtained experimental (OC) and control (PLC) meat. The assessment of the impact of the broiler stunning devices on meat quality was carried out on fresh fillets (approx. 9 h after slaughter), vacuum packed and stored in polyethylene bags for 7, 9, 12, and 14 days in a cooling chamber (temp. 4 \(\pm\) 0.1 \({}^{\circ}\)C, Memmert ICP 500, Schwabach, Germany).
## 3 Simulation Results
### Air-Incident Reflection Pattern  
The energy pattern in different azimuth and zenith angles of polarized lidar was analyzed. Figure 8a presents the percentage of the air-incident energy, which was reflected into each direction for quads of angular size \(10^{\circ}\times 15^{\circ}\) in zenith and azimuthal angles when wind speed was 10 m/s. The incident light was completely polarized, i.e., \(S_{0}=[1,1,0,0]^{\mathrm{T}}\). The incident direction was \(45^{\circ}\) from the zenith (i.e., \(\theta^{\prime}=135^{\circ}\)). Compared with unpolarized light whose reflectivity was 3.1%, only 0.7% of the air-incident energy was reflected, and the remaining 99.3% was transmitted into water. The maximum value was in the specular quad near \(\theta=45^{\circ}\). Figure 8b presents horizontal polarization (\(>\)0) and vertical polarization (\(<\)0). An evident depolarization is shown in Figure 8b. The \(45^{\circ}\) polarization shown in Figure 8c arose because of reflected light titled by wave facets. As shown in Figure 8d, the circular polarized light was nearly zero.  
Figure 6: Reflection and transmission of ocean surface for the air-incident case and lidar Stokes status of \(S_{0}=[1,1,0,0]^{\mathrm{T}}\): (**a**) I of reflected light, (**b**) Q of reflected light, (**c**) U of reflected light, (**d**) degree of polarization (DoP) of reflected light, (**e**) I of transmission light, (**f**) Q of transmission light, (**g**) U of transmission light, and (**h**) DoP of transmission light. I, Q, and U are Stokes parameters.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 2 GPS/BDS Combined PPP Models
### Traditional GPS/BDS Combined PPP Model  
The ionosphere-free pseudo-range and phase observations for the GPS/BDS combined PPP can be written as:  
\[\begin{array}{l}P^{\rm G}=\rho^{\rm G}+{\rm c}\cdot d\overline{t}_{\rm G}+ ZPD^{\rm G}+\zeta^{\rm G}\\ L^{\rm G}=\rho^{\rm G}+{\rm c}\cdot d\overline{t}_{\rm G}+\overline{N}^{\rm G} +ZPD^{\rm G}+\epsilon^{\rm G}\\ P^{\rm C}=\rho^{\rm C}+{\rm c}\cdot d\overline{t}_{\rm C}+ZPD^{\rm C}+\zeta^{ \rm C}\\ L^{\rm C}=\rho^{\rm C}+{\rm c}\cdot d\overline{t}_{\rm G}+\overline{N}^{\rm C} +ZPD^{\rm C}+\epsilon^{\rm C}\end{array} \tag{4}\]  
In Equation (4), the superscript and subscript \({\rm G},{\rm C}\) refers to GPS and BDS satellites. The slant tropospheric delay term ZPD could be modelled as the hydrostatic and wet parts with following:  
\[ZPD=mf_{\rm h}\cdot ZHD+mf_{\rm w}\cdot ZWD \tag{5}\]  
In Equation (5), \(ZHD,ZWD\) are the hydrostatic and wet zenith path delays, \(mf_{\rm h},mf_{\rm w}\) are the mapping functions. The hydrostatic part of ZPD is normally corrected using empirical models and the term \(ZTD_{\rm w}\) is estimated.  
The traditional GPS/BDS combined PPP model requires the estimation of an additional inter-system bias parameter. Defining GPS as the reference system, the inter-system bias could be written as,  
\[\begin{array}{l}ISB=c\cdot d\overline{t}_{\rm C}-c\cdot d\overline{t}_{\rm G }\\ =c\cdot dt_{\rm C}-c\cdot dt_{\rm G}+b_{\rm C}-b_{\rm G}\\ =c\cdot\Delta dt+\Delta b\end{array} \tag{6}\]  
Considering Equations (5) and (6), the traditional GPS/BDS combined PPP equations are written as,  
\[\begin{array}{l}P^{\rm G}=\rho^{\rm G}+{\rm c}\cdot d\overline{t}_{\rm G}+ mf_{\rm w}^{\rm G}\cdot ZWD+\zeta^{\rm G}\\ L^{\rm G}=\rho^{\rm G}+{\rm c}\cdot d\overline{t}_{\rm G}+\overline{N}^{\rm G} +mf_{\rm w}^{\rm G}\cdot ZWD+\epsilon^{\rm G}\\ P^{\rm C}=\rho^{\rm C}+{\rm c}\cdot d\overline{t}_{\rm G}+ISB+mf_{\rm w}^{\rm C }\cdot ZWD+\epsilon^{\rm C}\\ L^{\rm C}=\rho^{\rm C}+{\rm c}\cdot d\overline{t}_{\rm G}+ISB+\overline{N}^{ \rm C}+mf_{\rm w}^{\rm C}\cdot ZWD+\epsilon^{\rm C}\end{array} \tag{7}\]
# Radio Channel Scattering in a 28 GHz Small Cell at a Bus Stop: Characterization and Modelling
## 2 Experimental Set-Up  
### Channel Sounder  
The experimental set-up was based on a sweep time delay cross-correlation channel sounder. This sounder consisted of a signal transmitter and a receiver. At the transmitter, we first generated a pseudorandom binary sequence of \(2^{12}-1\) bits at a 900 Mbps rate, using an 81180A arbitrary waveform generator from Agilent. Figure 1 shows a capture of its spectrum. Then, we used this sequence to phase modulate a carrier at 28.65 GHz as follows. The output of the arbitrary generator was introduced as an input of an SMR40 microwave signal generator from Rohde & Schwarz, obtaining a modulated radio frequency (RF) signal with the spectrum in Figure 2. An ERZ-HPA-2700-4200-27 power amplifier from Erzia was used to reach a signal with a +27 dBm level, and it was finally transmitted with a directional pyramidal horn antenna with a gain of 20 dBi. This results in a high transmitted power for a small cell, but provides a good dynamic range to be able to analyze in detail small power contributions.  
We used an omnidirectional antenna at the receiver to capture the RF signal. The signal passed first through an AMF-3F-26004000-25-13P low-noise amplifier from Miteq, and thereafter it was mixed  
Figure 1: Measured baseband spectrum of the pseudorandom binary sequence.  
Figure 2: Measured spectrum of the modulated signal.  
in a Miteq M1826W1 double-balanced mixer with a 28.15 GHz signal from a local oscillator to achieve an intermediate frequency (IF) of 500 MHz. The 28.15 GHz signal was generated from a 10 MHz signal we got from a PRS10 Rubidium clock from SRS Inc. that fed a DLCRO-10-14075, and then a MAX2M200400-20P frequency doubler, both from Miteq. Figure 3 shows a picture of the receiver, identifying each element. The IF signal was sampled at 5G samples/s using a digitizing oscilloscope, model DSO-S-104A from Keysight, and stored for further off-line processing. Each signal record lasted for 0.1058 s and therefore contained 529 M samples.  
All signal generators and oscillators at the transmitter and the receiver were locked to the rubidium frequency standard. This was a very stable clock that guaranteed the measured delays could not be affected by any frequency variation of any oscillator, so they corresponded to actual wave propagation delays.
## 1 Introduction
The research introduces a back-to-back converter system with a low-voltage DC-link (LVDC), which functions to separate residential loads from power grids with multiple frequency components. The system enables controlled power transfer across a three-phase AC network, which contains both the 50 Hz fundamental component and the 5 th and 7 th harmonic voltages. The simulation includes two operating modes. The first one is called grid-connected mode, and the second one is called storage-supported mode, which combines a grid-connected multi-frequency inverter with a household inverter for three-phase load supply, and the second mode includes a battery energy storage system (BESS) connected to the DC-link for power flow regulation and voltage stability during dynamic load conditions. The system architecture enables support for upcoming multi-frequency power systems and maintains optimal power quality for connected loads. The main contributions of this work are as follows:  
1. Development and laboratory implementation of a back-to-back converter system with LVDC-link, which is able to isolate the non-standard-compliant voltage used in multi-frequency power systems from the standard-compliant voltage required by the residential loads, while transferring the total multi-frequency power to the load side. 2. Validation of two operating modes: (i) a baseline mode with a three-phase balanced load fed through a clean inverter output and (ii) a BESS-supported mode where the battery system interacts with the DC-link to absorb or supply power dynamically under changing load conditions.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 3 System Model
### End-to-End Hops in LSN  
In the LSN, there is a large distance and propagation delay between satellite nodes. The remaining hops to the destination can be used to make packets propagate along a shorter path to improve network delay performance [35; 36]. In this subsection, we will analyze the shortest propagation hops between any nodes in the inclined orbit constellation, so as to provide a basis for the improvement of the back-pressure routing strategy.  
Given a Walker-delta constellation composed of \(m\times n\) satellites, as shown in Figure 3, the dip angles of all orbits are \(\alpha\). The ascending points of all orbits are uniformly distributed along the equator. The right ascension of the ascending node (RAAN) of the adjacent tracks is \(\Delta\Omega=2\pi/n\). The phase difference between adjacent satellites on the same orbit is \(\Delta\Phi=2\pi/m\). Given that the phase factor of the constellation is \(F\), the phase difference of the adjacent satellites in adjacent orbits is \(\Delta f=2\pi F/mn\). Figure 4 demonstrates the trajectory of the substellar points of the LSN. The hops count required for the path from  
Figure 2: Inter-satellite links of the LEO satellite network.  
source to destination satellites (purple arrow) consists of two parts: inter-plane hops \(H_{h}\) in the horizontal direction and intra-plane hops \(H_{\triangleright}\) in the vertical direction.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background
### Applicability of TCP in the IoT  
The main current transport layer protocols in IP-based IoT scenarios are UDP and TCP. Due to TCP's complexity, header size, acknowledgment packet overhead, and unsuitability for multicast traffic, many IoT deployments prefer UDP. However, the basic TCP protocol is extendable with many options. Through careful selection of several TCP options, an implementation can become more lightweight [16]. The Lightweight Implementation Guide (LWIG) group at the IETF defined a set of TCP options to improve TCP's performance in constrained networks [16]. Here, we briefly describe the options we use during our performance evaluation.