esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Normal Incidence on a Half Space of Metamaterial  
We consider the left half space to be free space and the right half space filled with metamaterial. The interface between half spaces is parallel to the \(\hat{z}\) axis and the normal to the interface is taken to be the \(\hat{x}\) axis and the interface between half spaces is given by the \(x=0\) plane. We first suppose a group of waves of different frequencies propagate along the \(\hat{x}\)-axis, and so the \(H\) field is along the \(\hat{z}\)-axis and the problem reduces to one dimension. First we consider waves inside the metamaterial. Since we have normal incidence we replace \(k_{m}\hat{k}\) by \(k_{m}\) and \(\mathbf{x}\) by \(x\). We let \(k=\frac{\omega}{c}\) and write  
\[H^{Hom}(k) = \frac{B^{Hom}(k)}{\mu_{eff}(ck)}=\frac{B_{0}}{\mu_{eff}(ck)}e^{i (k_{m}(k)x-kct)},\]  
Energy propagation is in the direction of increasing \(x\) so if \(\mu_{eff}>0\), \(\epsilon_{eff}>0\) then \(n_{eff}>0\) while if \(\mu_{eff}<0\), \(\epsilon_{eff}<0\) then \(n_{eff}<0\), see [36; 37]. On the other hand the vacuum is nonmagnetic, i.e., \(\mu=1\) so the \(B\) and \(H\) fields are the same and  
\[H^{vac}(k)=B_{0}e^{i(kx-kct)}, \tag{13}\]  
where \(k\) is the wave number in vacuum and the free space dispersion relation is \(k=\frac{\omega}{c}\). Figure 6: The crosssection of the coated fiber with core radius \(a\) and coating radius \(b\). The wave forms inside both media are a superposition of plane waves. For each \(k\) the plane waves are of the form \(B_{0}\big{[}e^{i(kx-kct)}-\mathcal{R}(ck)e^{i(-kx-kct)}\big{]}\) for \(x<0\) and \(\mathcal{T}(ck)\frac{B_{0}}{\mu_{eff}(ck)}e^{i(k_{m}(k)x-kct)}\) for \(x>0\). For each \(k\) the coefficients \(\mathcal{R}(ck)\) and \(\mathcal{T}(ck)\) are determined by the two transmission conditions  
\[[H_{3}]=0\text{ at }x=0 \tag{14}\]  
and  
\[[E_{2}]=0\text{ at }x=0. \tag{15}\]  
From [PERSON]'s equations we have the identity inside vacuum and metamaterial  
\[E_{2}=e^{-1}(x)\partial_{x}H_{3} \tag{16}\]  
where  
\[e(x)= \begin{cases}1&\text{ for }x<0,\\ \epsilon_{eff}(ck)&\text{ for }x>0.\end{cases} \]  
Then the last jump condition (15) is equivalent to  
\[[\epsilon^{-1}(x)\partial_{x}H_{3}]=0\text{ at }x=0. \tag{17}\]  
Collecting the results we give the waveforms incident reflected and transmitted across the interface \(x=0\) between vacuum \(x<0\) and effective material \(x>0\). Proposition 1: Waveforms in vacuum and effective material. _The 2 component of the \(H\) field is the only non zero component of the \(H\) field wave form in vacuum (\(x<0\)) and is_  
\[H_{\text{vac}}(k_{0},x,t) = \int_{-\infty}^{\infty}A(k)B_{0}\big{[}e^{i(kx-kct)}-\mathcal{R}( ck)e^{i(-kx-kct)}\big{]}\,dk, \tag{18}\]  
_where the amplitude \(A(k)\) decribes the properties of the linear superpostion of the different waves.
## 1 Introduction
The proposed system consists of three main modules: the master domain, the network domain, and the slave domain, as depicted in Figure 1. A human operator on the master side uses a haptic display/device to monitor and control the slave domain. The network domain connects the master and slave domains, enabling bidirectional communication over the network. The core network of the proposed ECL-TeleCaring system includes SDN and MEC technologies to decrease network latency and support haptic-driven remote caring service. Slave domains comprise controller robots that are used for elder caring and are directly controlled and monitored by the master-domain-side human operator via control signals. Figure 1: An overview of the architecture design of the proposed elderly ECI-TeleCaring system. The master domain receives the activity sensing along with haptic and audio-visual signals. The edge server on the master side predicts the activity of the caring robots and shows the performance to the human operator at the haptic display. Human operators can monitor the activity of multiple robots and, in the case of anomaly and undesirable events that degrade the Quality of Task (QoT), provide feedback to the slave domain using a haptic device or haptic data over the communication network. The proposed system employs a dual Long Short-Term Memory (LSTM) model at the edge for real-time remote activity prediction and critical decision making, significantly reducing latency and enhancing system responsiveness. The ability to process and analyze data at the edge ensures that the system can continuously learn and improve, providing a personalized care experience. The use of deep learning methods at the edge for data processing ensures that the system can handle large volumes of data, including haptic data, efficiently, providing timely responses and reducing the burden on human caregivers. Edge intelligence also facilitates the dynamic adaptation of the system to changing conditions and individual needs, thus overcoming the limitations of traditional remote care methods. The next section discusses the primary contribution of the ECI-TeleCaring system.
## 8 Performance Evaluation
### 6G-Assisted UAVs Network Characteristics  
6G offers improved network QoS and reliability over 5G services. The proposed UAV and 6G-based architecture are implemented by impersonating the behavior of a 5G network in the 5G toolbox of the MATLAB tool. Here, a different simulation parameter of the 5G network is configured to simulate a 6G waveform. For that, we first referred to the diverse literature to acquire 6G simulation parameters and based on those parameters, we configured the 5G toolbox to simulate a 6G network [55; 56; 57]. Parameters such as frequency range, channel bandwidth, network impairments, modulation, channel coding, and sub-carrier spacing are modified in the 5G toolbox to achieve the indispensable characteristics of the 6G network. Table 4 shows the simulation parameters used to simulate the 6G network for the proposed architecture.  
Figure 8a shows the improved UAV mobility of 6G-ERLLC, which is \(\approx\)1000 km/h in the best case, over the 5G mobility of 550 km/h. Figure 8b shows the radio loss of 6G-based antennas that communicate with UAVs inside a spatial range. As 6G spectrum efficiency is \(\approx\)5-10 times better than 5G channels, and the path loss is minimized, resulting in an improved lifetime of UAV communications. The user-experienced (UE) data rate of 6G channels is 1 Gbps, compared with 100 Mbps in 5G, and 10 Mbps in 4G, as shown in Figure 8c. Moreover, a connection density of 6G is \(\approx\)10\({}^{7}\) devices/km\({}^{2}\), which is ten times that of 5G. Thus, as more devices are added to 6G ecosystems, the UE data rates do not drop drastically, resulting in a better experience for 6G users. This is useful for UAVs, as they have to send ultra-HD high-resolution images of infected COVID-19 hotspots to GS.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Network Parameters** & **Value** \\ \hline Frequency Range & 102.1 GHz \\ Channel bandwidth & 2.3 GHz \\ Delay Spread & 0.56 (ns) \\ Channel model & Rayleigh Fading \\ Modulation & Orthogonal Time Frequency Space (OTFS) \\ Channel coding & Polar codes \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters of 6G-based communication layer.  
Figure 7: Comparative analysis of communication latency, density, and spectral efficiency of various communication networks in UAV deployment. (**a**) 6G service benefits for handling high traffic volumes. (**b**) Processing latency of UAV connection links of 6G over 5G. (**c**) Spectral efficiency of 6G over 5G channels.
# Measurement of Quasiparticle Diffusion in a Superconducting Transmon Qubit
## 1 Introduction  
The superconducting quantum circuit has become a promising candidate for realizing universal quantum computation for rapid development in recent years. At this stage, the prospective superconducting qubits are aimed at longer coherence times and higher readout fidelities. It has been known that some loss channels--such as dielectric loss from two-level defects [1]--and quasiparticles [2; 3; 4; 5; 6; 7; 8] can set limits on qubit coherence. Among them, the quasiparticles that may absorb the qubit energy when tunneling through the junction are regarded as the dominant loss for the microsecond limit on the energy relaxation time \(T_{1}\)[9; 10].  
While the quasiparticle density, which is assumed to be in thermal equilibrium, is suppressed exponentially as the bath temperature decreases, recently observed experimental results have shown that the normalized nonequilibrium quasiparticle density is in a range of \(10^{-8}-10^{-6}\), which is several orders of magnitude larger than the \"cold\" equilibrium one at a 20 mK bath temperature [9; 11; 12; 13; 14; 15; 16]. The nonequilibrium quasiparticles are attributed as a source of the loss mechanism and residual excited-state population. As illustrated in Figure 1a, quasiparticles near the junction with energies \(E_{qp}\) of all ranges can absorb the qubit-transition energy and transfer the qubit from the excited state to the ground state, and the \"hot\" ones that have energies greater than \(\Delta+E_{ge}\) may release energy and excite the qubit [17; 18].  
Several techniques have been proposed to depict the nonequilibrium quasiparticles in superconducting qubits, such as treating the transmon qubit as a real-time quasiparticle-tunneling detector [9; 19], the charge-parity correlation measurement [10], and direct dispersive monitoring [20]. However, most of these measurements require high-fidelity dispersive readouts or the transmon qubit in a low \(E_{J}/E_{C}\) regime. In this letter, we propose a simple and practical protocol, which is demonstrated on a normally anharmonic (\(E_{J}/E_{C}\gtrsim 50\)) single-junction transmon qubit. We study the features of quasiparticles in the sample with the contactless quasiparticle injection technique [21] and read out with the routine called \"high-power readout\" [22], in which the high demand for dispersive readout fidelities is not necessary. In the experiment, the energy relaxation time \(T_{1}\) and the residual excited-state population \(P_{e}^{r}\) are measured with different parameters of injected quasiparticles. From thedown-transition rates of the qubit and the energy distribution of nonequilibrium quasiparticles extracted from the experimental data, we phenomenologically model the quasiparticle diffusion [23,24].
# Strategies for Post-Translational Control of Protein Expression and Their Applications
## 4 Conditional Control of Protein Stability Using Genetic Manipulation
### Nanobody-Based Degrons  
Degradation tools that use specific reagents for the detection of the target protein have been emerging. These methods rely on single-domain-binding proteins, such as the heavy-chain antibodies from _Camelidae_ sp. (called nanobodies) [69] or the designed ankyrin repeat proteins (DARPins) [70]. Unlike antibodies, these single-domain proteins can be expressed by a single vector system, which is useful for biological studies. [PERSON] et al. developed a genetically encoded method for the direct and fast depletion of the target green fluorescent protein (GFP), called degrade green fluorescent protein (deGradFP) (Figure 5a) [71]. The deGradFP system utilizes a nanobody against GFP, called VhGFP4, which can be fused with NSLmb, an F-box domain included in the N-terminal part of the F-box protein supernumerary limbs (Slmb) from _D. melanogaster_. The engineered NSLmb-VhGFP protein recruits the SCF complex to the GFP-tagged protein and subsequently ubiquitinates it, thus allowing easy monitoring of the protein removal process. [PERSON] et al. reported a similar approach, named ubiquitody, that uses a fusion protein comprising E3 ligase mimics from a bacterial pathogen and a fibronectin type III monobody that binds to GFP [72]. This achieves the selective degradation of GFP-tagged proteins in conjunction with hijacking of the mammalian ubiquitin-proteasome pathway. Furthermore, [PERSON] et al. combined the advantages of the auxin- and nanobody-based degradation technologies to create an AID-VhhGFP fusion for the degradation of GFP-tagged proteins in different cellular structures in a conditional and reversible manner in human cells [73].  
Figure 5: Nanobody-based degrons. Concepts of (**a**) deGradFP, (**b**) LiPD, and (**c**) DiPD. Nanobody (Nb).  
Recently, [PERSON] et al. developed alternative nanobody-based approaches to control endogenous molecules in cells and organisms via light- or drug-induced protein depletion (LiPD or DiPD, respectively) [74]. These systems comprise two recombinant elements, i.e., the E3 ligase and the nanobody (or DARPin) that target the POI. The two proteins are conjugated with heterodimerization tags, such as cryptochrome 2 (CRY2) and CIBN, thus allowing light-induced heterodimerization between the two fusion proteins (Figure 5b). This brings together the nanobody-bound POI and the E3 Ub ligase, thus triggering ubiquitination and degradation of the target protein. The RING domain of the E3 Ub ligase and nanobody for POIs can also be fused to FRB and FKBP12, respectively. Upon addition of rapamycin, FRB and FKBP12 heterodimerize, which leads to the ubiquitination and degradation of the POIs (Figure 5c).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 2 Materials and Methods
### Methods of Measurements  
For cosmic-neutron measurements, we employed an extended energy-range rem meter coupled with a tungsten-powder-mixed moderator (FHT762 Wendi-2, Thermo Fisher Scientific K.K., Franklin, MA, USA). WENDI-II has functions of continuously counting the pulses generated from the \({}^{3}\)He(\(n\), \(\alpha\))Li reaction with moderated thermal neutrons and calculating the hourly rate of 1 cm ambient dose equivalent, H*(10), multiplying the one-hour integrated pulse counts and a constant conversion coefficient. Selected properties of WENDI-II are indicated in Table 1. More details on the dosimetric properties of WENDI-II were presented by [PERSON] et al. [43; 44].  
WENDI-II has been employed by the authors for measurements of cosmic neutrons on high mountains and onboard aircraft [45; 46; 47; 48; 49] and was carefully calibrated with the monitoring data acquired through the repeated neutron measurements at the high-altitude station at Mt. Fuji (altitude: 3776 m) in Japan [46; 48]. It was confirmed that WENDI-II had negligibly low sensitivity to photons from \({}^{60}\)Co and \({}^{137}\)Cs sources and thus its threshold for n/\(\gamma\) discrimination was set as a default value. In addition, its negligible response to energetic protons (\(\sim\)230 MeV) was roughly confirmed at the Heavy Ion Medical Accelerator in Chiba (HIMAC) in Japan.  
WENDI-II was connected to a small, battery-powered data logger (NM10) with dimensions of W21 cm \(\times\) H6.6 cm \(\times\) D16 cm and weight of 2.4 kg. NM10 was originally made by the authors for long-term mobile use in recording the pulse counts from WENDI-II with adjustable time intervals [45; 49]. A set of WENDI-II and NM10 was placed on the desk near a front window of the snow vehicle (Figure 2) over the whole monitoring period. The signals of neutron-induced pulses from WENDI-II were automatically recorded by NM10 with 1 h intervals and converted to the hourly H*(10) rates. The temperature at the location where the neutron-monitoring system was placed varied from \(-\)10 \({}^{\circ}\)C to 20 \({}^{\circ}\)C during the measurement period (i.e., November 2018 to January 2019); this variation was within the temperature-applicable range of WENDI-II (\(-\)30 \({}^{\circ}\)C to 50 \({}^{\circ}\)C) indicated in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Detector** & \(\mathbf{\Phi}\)**2.5 cm \({}^{3}\)He Proportional Counter** \\ \hline Moderator & Polyethylene with tungsten powder shell \\ Applicable neutron energy & 0.025 eV to 5 GeV \\ Applicable temperature range & \(-\)30 \({}^{\circ}\)C to 50 \({}^{\circ}\)C \\ Applicable atmospheric pressure & 500 hPa to 1500 hPa \\ Dimensions & \(\mathbf{\phi}\)23 cm \(\times\) H32 cm \\ Weight & 13.5 kg \\ \hline \end{tabular}
\end{table}
Table 1: Selected properties of the portable neutron monitor WENDI-II.  
Figure 2: A photograph of the snow vehicle (left) and the portable neutron monitor (WENDI-II) connected to an exclusive data logger (NM10) (right) originally made for mobile use. The neutron monitor was placed near the front window inside the snow vehicle.
## 3 Experimental Setup  
The setup used in our experiment for cascaded MgO:PPLN single-pass sum-frequency generation is shown in Figure 1. The output of wavelength division multiplexer (WDM) tunable laser source (TLS) with a linewidth of \(<\)100 kHz (FWHM) in the C-band (1527-1565 nm) is amplified using an erbium-doped fiber amplifier (EDFA). It is combined with a \(\sim\)980 nm module diode laser (RMS centre wavelength 973.9 nm and 0.20 nm spectral width) by a 980/1550 nm wavelength division multiplexer (WDM) with 0.26 and 0.28 dB insertion losses at 980 nm and 1550 nm respectively. The output from the single mode fiber (SMF) that is terminated with a collimator is passed through the first nonlinear uniformly poled MgO:PPLN crystal. The two overlapped input beams are focused into the first crystal and the SFG of the first crystal together with the input beams are re-focused into the second SC-PPLN nonlinear crystal with a 125 mm focal length plano-convex lenses.  
Here, the period of the first MgO:PPLN crystal is 10.3 \(\upmu\)m and the length is 50 mm. The second MgO:PPLN crystal is a step-chirped structure with poling periods from 10.1 to 10.5 \(\upmu\)m with a precision of 0.1 \(\upmu\)m increase and the length is 20 mm with five equal sections. The SFG phase-matching wavelength for Crystal 1 is 1544.9 nm, while Crystal 2 showed two phase matching wavelengths at 1545.7 nm and 1556.6 nm. Both crystals were temperature-stabilized close to 31 \({}^{\circ}\)C and 25 \({}^{\circ}\)C for QPM, respectively. The output power and spectra of the up-converted orange output beam after an optical filter was measured using a laser power meter (PM100D, Thorlabs, Newton, NJ, USA) and a fiber spectrometer (BIM-6001, Brolight, Hangzhou, China) with a resolution of 0.35-1 nm, respectively, whereas the M\({}^{2}\) factor was measured using knife-edge method with Beamgage (Ophir-Spiricon, Inc., Jerusalem, Israel) laser beam analyzer.
## 2 Materials and Methods
### Definition and Design of the System's Components
#### 2.2.3 Internal Interfaces to Connect FPGA/LED Driver and FPGA/PD Driver
This is as described by Equation (13), derived from the analysis of the circuit shown in Figure 18:  
\[\frac{V_{A}-V_{IN}}{R_{1}}=\frac{V_{IN}}{R_{2}} \tag{12}\]  
\[\frac{V_{A}}{V_{IN}}=\left(1+\frac{R_{1}}{R_{2}}\right) \tag{13}\]  
By setting R\({}_{2}\) = R\({}_{1}\) = 100 \(\Omega\)[38], the voltage at node \(V_{A}\) is demonstrated as \(V_{A}\) = 2\(\cdot\)\(V_{IN}\). Following this stage, a high-pass filter is employed to eliminate low-frequency components from the signal. The gain value is acquired using Equation (14):  
\[\frac{V_{\text{B}}}{V_{\text{A}}}=\frac{j\cdot\!w\cdot\!R_{4}\!\text{-}C_{1}}{ 1+j\cdot\!w\cdot\!R_{4}\!\text{-}C_{1}} \tag{14}\]  
We selected R\({}_{4}\) = 1 k\(\Omega\) and C\({}_{1}\) = 100 nF. The cut-off frequency is defined by f\({}_{\text{c,HPF}}\) = \(\frac{1}{2\pi\cdot\!R_{4}\!\text{-}C_{1}}\)\(\simeq\) 1.6 kHz [39]. Following the high-pass filtering, a low-pass filter is introduced. The obtained signal from the previous stage -detailed in Equation (14)--is connected to the positive differential input V\({}_{\text{IN+}}\) of the MAX1448 ADC. This results in the expression for \(\frac{V_{IN+}}{V_{B}}\), as shown in Equation (15):  
\[\frac{V_{IN+}}{V_{B}}=\frac{1}{1+j\cdot\!w\cdot\!R_{5}\!\text{-}C_{2}} \tag{15}\]  
We selected \(R_{5}\) = 50 \(\Omega\) and \(C_{2}\) = 22 pF. The cut-off frequency is given by \(f_{c,LPF}\) = \(\frac{1}{2\pi\cdot\!R_{5}\!\text{-}C_{2}}\)\(\simeq\) 145 MHz [40]. The ADC voltage level, defined as \(V_{IN-}\) = \(I_{COM}\cdot R_{6}\) = 0.25 V, is connected to the negative differential input (\(V_{IN-}\)), where \(I_{COM}\) = 5 mA and \(R_{6}\) = 50 \(\Omega\). This ensures that \(V_{IN+}\gg V_{IN-}\), where \(V_{IN+}\) is the signal obtained after the \(V_{IN}\) signal from the PD driver has been amplified and filtered in two stages. To maintain synchronization between the PD driver/FPGA interface and the FPGA/LED driver interface, the same TXO crystal oscillator is connected to the MAX1448 ADC. Alternatively, the clock signal can be generated from the Xilinx Zynq 7020 FPGA [37; 38; 39; 40; 41] and connected to both the MAX5184 DAC and MAX1448 ADC by adjusting the jumper position. The components selected for the PD driver/FPGA connection are detailed in Table 4. The datasheets of the MAX1448 ADC, the MAX4105 amplifier, and the TXO crystal oscillator can be found in [35; 36; 38], respectively.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 2 Data and Methods
### Analytical Methods
#### 2.3.3 Variation Coefficients  
To quantitatively characterize the degree of interannual WUE variations, the variation coefficient (VC) is used as follows:  
\[\text{VC}_{\text{WUE}}=\frac{\sigma_{\text{WUE}}}{\text{WUE}}, \tag{7}\]  
where \(\sigma_{\text{WUE}}\) is the standard deviation of the WUE time series from 2001 to 2017, and \(\overline{\text{WUE}}\) is the average WUE over the time range. The smaller the value of \(\text{VC}_{\text{WUE}}\) is, the more stable the WUE time series is. Otherwise, the WUE time series has experienced large volatility.
###### Abstract  
Internet of Things (IoT) technology in healthcare has enabled innovative services that enhance patient monitoring, diagnostics and medical data management. However, securing sensitive health data while maintaining system efficiency of resource-constrained IoT devices remains a critical challenge. This work presents a comprehensive end-to-end IoT security framework for healthcare environments, addressing encryption at two key levels: lightweight encryption at the edge for resource-constrained devices and robust end-to-end encryption when transmitting data to the cloud via MQTT cloud brokers. The proposed system leverages multi-broker MQTT architecture to optimize resource utilization and enhance message reliability. At the edge, lightweight cryptographic techniques ensure low-latency encryption before transmitting data via a secure MQTT broker hosted within the hospital infrastructure. To safeguard data as it moves beyond the hospital to the cloud, stronger end-to-end encryption are applied to ensure end-to-end security, such as AES-256 and TLS 1.3, to ensure confidentiality and resilience over untrusted networks. A proof-of-concept Python 3.10 -based MQTT implementation is developed using open-source technologies. Security and performance evaluations demonstrate the feasibility of the multi-layer encryption approach, effectively balancing computational overhead with data protection. Security and performance evaluations demonstrate that our novel HECS4 MQTT (Health Edge Cloud Security for MQTT) framework achieves a unique balance between efficiency and security. Unlike existing solutions that either impose high computational overhead at the edge or rely solely on transport-layer protection, HECS4 MQTT introduces a layered encryption strategy that decouples edge and cloud security requirements. This design minimizes processing delays on constrained devices while maintaining strong cryptographic protection when data crosses trust boundaries. The framework also introduces a lightweight bridge component for re-encryption and integrity enforcement, thereby reducing broker compromise risk and supporting compliance with healthcare security regulations. Our HECS4 MQTT framework offers a scalable, adaptable, and trust-separated security model, ensuring enhanced confidentiality, integrity, and availability of healthcare data while remaining suitable for deployment in real-world, latency-sensitive, and resource-limited medical environments.  
Internet of Things (IoT); MQTT; healthcare security; lightweight cryptography; end-to-end encryption; cloud security; multi-broker architecture