esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design and Results
### Results
#### 4.5.2. Bits per Pixel (BPP)  
The BPP metric represents the number of bits required to encode a single pixel. A lower BPP value indicates higher compression efficiency, as fewer bits are needed per pixel. Table 5 presents the BPP values calculated for all images in the datasets. For encoding algorithms, BPP values were computed for HCA, LPHEA, HBLIES, and the DEA. For compression algorithms, BPP values were calculated for the S+DEA compression algorithm and compared against BPG, JPEG-LS, and JPEG2000.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Encoding Algorithms**} & \multicolumn{4}{c}{**Compression Algorithms**} \\ \cline{2-10}
**Image** & \multirow{2}{*}{**HCA [23]**} & \multirow{2}{*}{**LPHEA**} & \multirow{2}{*}{**HBLIES**} & \multirow{2}{*}{**DEA**} & \multirow{2}{*}{**BPG**} & \multirow{2}{*}{**JPEG**} & \multirow{2}{*}{**LS**} & \multirow{2}{*}{**2000**} & \multirow{2}{*}{**S+DEA**} \\
**Name Tag** & & & & & & & & \\
3.1 & 1.05 & 1.06 & 1.92 & 1.96 & 1.96 & 2.31 & 2.30
## 3 Applications of Multimodal Fluorescent-Magnetic Nanoparticles
### Drug Delivery  
In the last decades, magnetic nanoparticles have become an important advanced tool for applications such as drug delivery. Magnetic nanoparticles can be manipulated with an external magnetic field and driven to the desired target area, i.e., cancerous tissue, where the drug can be released and act directly. This advance in medicine enables the reduction of the drug dosage and potential elimination of side effects [97]. The success of this therapy strongly depends on the selection of the targeting moiety to have specific affinity of binding to the cell surface receptors [98].  
The high surface area of the nanoparticles allows for a large amount of drug to be adsorbed. However, magnetic nanoparticles used in drug delivery must be functionalized with groups that can assure their biocompatibility [99].  
Fluorescent magnetic nanoparticles provide the possibility of performing integrated functionalities, enabling them to perform both bioimaging and therapy functions. The incorporation of a fluorescent entity allows the tracking of the nanocomposite while different drugs can be released inside the tissue or in the target area under treatment.  
For example, [PERSON] et al. developed highly luminescent magnetic hybrid nanoparticles consisting of a magnetic core and mesoporous carbon as an outer layer. The resultant material showed magnetic resonance contrast behavior by affecting the proton relaxation with transverse relaxivity. In vitro cell experiments demonstrated that the material loaded with DOX can be endocyted by tumour cells through passive targeting [36]. Multifunctional fluorescent-magnetic polyethyleneimine functionalized Fe\({}_{3}\)O\({}_{4}\) with a mesoporous silica shell nanocapsules were prepared for fluorescent tracking and the delivery of RNA. The cavities of the mesoporous silica shell have the ability to carry \(\beta\)-actin siRNA into Hela cells for simultaneous visualization and magnetically guided gene delivery [40]. Magneto-thermally responsive DOX-encapsulated supramolecular magnetic nanoparticles were prepared by [PERSON] et al. The system was tested for the delivery and release of DOX in an optimized in vivo treatment. This system showed a considerable decrease of the drug dosage compared to existing protocols, which may increase the clinical opportunities for other drugs with good therapeutic efficacy but high toxicity with important side effects [54].  
A graphene oxide (GO)-based fluorescent magnetic hybrid was reported by [PERSON] et al. for loading and delivery water-soluble aromatic anti-cancer drugs such as DOX. The maximum loading capacity of DOX to the GO-based magnetic fluorescent hybrids was 91.9%. DOX preferred release from DOX loaded GO-based magnetic fluorescent hybrids under acidic conditions. A HepG2 cell line was used to evaluate the capability of the hybrids with respect todrug delivery and imaging, and their morphology after study indicated the DOX loaded GO-based magnetic fluorescent hybrids displayed an excellent therapeutic effect on the target tumours [30].
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 2 Cellular-Based Positioning Methods
### MSR-Based Location Methods  
The MSRs, which include received signal strength indication (RSSI), round-trip delay (RTD), and relative delay (RD), are analyzed for location determination. MSR-based location methods can be classified into three categories: angle of arrival (AoA) [13; 14; 15], time of arrival (ToA) [13; 14; 16], and time difference of arrival (TDoA) [9; 10; 13]. This approach requires higher computation power than other methods [12; 17; 18; 19].
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 3 LRI Ranging Phase
### Model Errors: Scale Factor and Light Propagation Time
Thus, Equation (17) is approximately equivalent to a formula with static or mean frequency and the dominant error in Equation (17) is the product \(L(t=0)\cdot\epsilon_{\text{SCF}}(t)\), which is very similar to the scale factor error \(\langle L\rangle\cdot\epsilon_{\text{SCF}}(t)\) discussed here. Any static error in the scale factor, e.g., \(\langle\epsilon_{\text{SCF}}\rangle=10^{-8}\) corresponding to 2.81 MHz frequency error of the LRI cavity, produces an error proportional to the separation \(L(t)\). If one assumes a drift of 0.01 m/s or 864 m per day and a 1/rev amplitude of 400 m (peak), the scale factor errors correspond to ranging errors of 8.6 \(\upmu\)m per day and 4 \(\upmu\)m at 1/rev frequency. Although the derivation of the scale factor error was based on the LRI, we anticipate that the KBR instrument features a similar error coupling. The sinusoidal modulation amplitude of approx. \(4\cdot 10^{-12}\) from the proper time is a fractional quantity and appears with the same amplitude in the microwave (cf. Section 2). Thus, the 0.9 \(\upmu\)mpeak error would be applicable to the KBR as well. However, day-to-day changes in the USO frequency are usually a factor 10 or more lower than \(3.1\cdot 10^{-10}\) (cf. left plot of Figure 1). Thus, the ranging error from linear drift should be at the level of a few micron level instead of 68 \(\upmu\)m per day. In addition, static scale factor errors are expected to be much smaller in KBR, as the microwave frequency can be determined with high accuracy from GPS data in post-processing during orbit determination.
# Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials
## 4 Numerical Results
### Effect of Interval Unbalance Phase  
Taking the unbalance phase of Disc 1 as a reference and assuming that the unbalance phase of Disc 1 is 0\({}^{\circ}\), the unbalance phase shifts of Discs 2 and 3 fluctuate within the range of [0, 360\({}^{\circ}\)]. The convergence results of the relative error at different speeds are presented in Figure 9.  
Figure 8: Dynamic response range of Disc 1, with intervals of mass unbalance.  
Figure 9: Convergence analysis of posterior error in chaotic polynomials due to phase angle uncertainty.  
As shown in Figure 9, to consider the convergence of the relative error, a Chebyshev orthogonal polynomial of order (\(p=8\)) is constructed. This forms the basis for conducting an uncertainty propagation analysis of the phase angle of the mass imbalance across the entire speed range. Figure 10 illustrates the variation range of the steady-state response at Disc 1.  
As shown in Figure 10, uncertainty in the unbalance phase significantly impacts the steady-state response of the rotor, particularly at the third-order critical speed, at which the maximum amplitude increases by nearly threefold compared to the deterministic case. Changes in the unbalance phase alter the direction of the corresponding centrifugal force, consequently modifying the total centrifugal force on the rotor and the force couples. To ensure that the rotor operates reliably and safely, it is essential to account for the uncertainty of the unbalance phase during the design and assembly process, thereby preventing excessive unbalance-phase differences between the discs due to oversight.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.3 Resistance to Off-Line Guessing Attacks  
Through offline guessing attacks, adversary \(\mathcal{A}\) tries to reveal the user's identity and password. \(\mathcal{A}\) can obtain the hash value \(R_{1}=h(ID_{i}||PW_{i}||r_{i})\) by extracting data from the stolen or lost mobile device. However, because \(\mathcal{A}\) lacks knowledge of the random number \(r_{i}\), it is computationally infeasible to recover \(ID_{i}\) and \(PW_{i}\) via off-line guessing attacks.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 2 Materials and Methods
### EC-LUE Model  
The EC-LUE model is used to calculate the potential LUE, which is reduced by non-optimal temperature or water stress [32]:  
\[\varepsilon=\varepsilon_{max}\times C_{s}\times\min(T_{s},W_{s}) \tag{1}\]  
where \(C_{s}\), \(T_{s}\) and \(W_{s}\) are the downward-regulation scalars attenuating the maximum LUE for the respective effects of atmospheric CO\({}_{2}\) concentration, temperature, and moisture of different vegetation types. The three scalars vary between 0 and 1 with smaller values indicating a stronger negative impact. Min denotes the minimum value of \(T_{s}\) and \(W_{s}\).  
The effect of the atmospheric CO\({}_{2}\) concentration on GPP is determined by the following equations [45; 46]:  
\[C_{s}=\frac{C_{i}-\varphi}{C_{i}+2\varphi} \tag{2}\]  
\[C_{i}=C_{a}\times\chi \tag{3}\]  
where \(\varphi\) is the CO\({}_{2}\) compensation point in the absence of dark respiration (ppm), \(C_{i}\) is the leaf internal CO\({}_{2}\) concentration, \(C_{a}\) is the atmospheric CO\({}_{2}\) concentration, and \(\chi\) is the ratio of leaf internal to atmospheric CO\({}_{2}\) concentration, which can be estimated as follows [47; 48]:  
\[\chi=\frac{\delta}{\varepsilon+\sqrt{VPD}} \tag{4}\]  
\[\delta=\sqrt{\frac{356.51K}{1.6\eta^{*}}} \tag{5}\]  
where \(\varepsilon\) is a parameter related to the carbon cost of water, which determines the sensitivity of VPD to \(\chi\); \(K\) is the Michaelis-Menten coefficient of Rubisco; and \(\eta^{*}\) is the viscosity of water relative to its value at 25 \({}^{\circ}\)C [49].  
\[K=K_{c}\bigg{(}1+\frac{P_{0}}{K_{0}}\bigg{)} \tag{6}\]  
where \(P_{0}\) is the partial pressure of O\({}_{2}\), and \(K_{c}\) and \(K_{0}\) are the Michaelis-Menten constants for CO\({}_{2}\) and O\({}_{2}\)[48]:  
\[K_{c}=39.97\times e^{\frac{79.43\times(T_{s}-298.15)}{298.15\times K\times T_{ s}}} \tag{7}\]  
\[K_{0}=27480\times e^{\frac{36.38\times(T_{s}-298.15)}{298.15\times K\times T_{ s}}} \tag{8}\]  
where \(T_{a}\) is the air temperature (unit: K) and \(R\) is the molar gas constant (8.314 J/mol/K).  
\(T_{s}\) is estimated based on the equation developed for the Terrestrial Ecosystem Model (TEM) [50]:  
\[T_{s}=\frac{(T-T_{min})(T-T_{max})}{\big{[}(T-T_{min})(T-T_{max})\big{]}-\big{(} T-T_{opt}\big{)}^{2}} \tag{9}\]  
where \(T_{min}\), \(T_{max}\) and \(T_{opt}\) are the minimum, maximum, and optimum air temperatures (8 \({}^{\circ}\)C) for photosynthetic activity, respectively. If the air temperature falls below \(T_{min}\) or increases beyond \(T_{max}\), \(T_{s}\) is set to zero. In this study, \(T_{min}\) and \(T_{max}\) are set to 0 and 40 \({}^{\circ}\)C, respectively, while \(T_{opt}\) is determined as 20.33 \({}^{\circ}\)C using nonlinear optimization [9].  
The impact of moisture on photosynthesis (\(W_{s}\)) is defined as follows:  
\[W_{s}=\frac{VPD_{0}}{VPD_{0}+VPD} \tag{10}\]  
\(VPD_{0}\) is the half-saturation coefficient of the \(VPD\) constraint equation (k Pa), which varies with vegetation types (Table 2):
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 2 Materials and Methods
### Microstructure Characterization Methods
#### 2.4.4 Electron Backscattered Diffraction (EBSD) Analysis  
Backscattered electrons (BSE) are energetic electrons produced using elastic or inelastic scattering of an incident electron beam with the nucleus of an atom. Electron Backscattered Diffraction (EBSD) can be used to analyze the microstructure and orientation of solid crystalline materials.  
Using a Thermo Scientific Helios 5 UX (Thermo Fisher Scientific, Waltham, MA, USA) focused ion beam electron microscope in combination with EBSD, the size and distribution of the phases prior to and following the addition of graphene were further observed, as well as the changes in the size and orientation of the grains. The possible effects of these changes on the properties were analyzed.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M  
The early satellite solutions traditionally employed protocols mainly based on fixed assignment (e.g., CDMA, FDMA, and TDMA). In some cases, the protocol was combined with a random access scheme to perform the adaptive assignment according to the demand of the nodes. An example of an early protocol is the demand assignment multiple access [40]. Nonetheless, as mentioned before, considering the nature of our study scenario, protocols in the random access category are more relevant and suitable for the comparative evaluation.  
The protocols selected in this section included several descendants of the well-known Aloha protocol, since such derivations are present in current satellite systems and modern IoT technologies such as LoRa and Sigfox. The selection also included other significant--and more modern--random access protocols that were considered suitable for the IoT scenario described in Section 3, all applicable to satellite environments and other IoT technologies. Such protocols make use of advanced techniques like interference cancellation, and adaptiveness, among others.
# Case Study of an Underpinning Pile Foundation for an Interval Tunnel Crossing an Existing Bridge
## 4 Three-Dimensional Numerical Model
### Numerical Model Loads  
(1) Self-weight  
The self-weight coefficient of the calculation model is taken as g = 9.81 m/s\({}^{2}\) (because the tunnel burial depth is not large, the influence of the tectonic stress field is small, and the ground stress field is considered using the self-weight stress field).  
(2) Bridge superstructure loading  
The superstructure load is the main factor causing the structure to settle during the construction period. In this project: the bridge reinforced concrete and prestressed concrete unit weight is 26 kN/m\({}^{3}\), and the left and right deck of the second-phase paving take the value of 107.3 kN/m. In order to simplify the calculation, the superstructure load of the bridge is converted to the self-weight of the bridge deck slab (concrete grade C40).  
(3) Lane load  
The automobile design load of Guihuang Road Viaduct is calculated according to the City--Class A load, where the automobile load includes the lane load and vehicle load. This paper aims to calculate and analyze the overall structure of the bridge, so the lane load is used. Referring to the relevant provisions of the national standard \"Code for design of urban bridges\" (CJJ11-2011), the lane load is composed of concentrated load and uniform load, the standard value of the uniform load is taken as q\({}_{\text{K}}\) = 10.5 kN/m, and the concentrated load is taken as PK = 302 kN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material Type** & **Concrete Grade** & **E (GPa)** & **\(\mu\)** & **\(\gamma\) (kN/m\({}^{3}\))** \\ \hline Cover beam & C50 & 34.5 & 0.2 & 23.5 \\ Bridge panel & C40 & 32.5 & 0.2 & 23.5 \\ Underpinned bearing & C35 & 31.5 & 0.2 & 23.5 \\ platform; underpinned pile & & & & \\ Existing pile; existing & & & & \\ bearing platform & & & & \\ Bridge pier; underground & & & & \\ continuous wall & C30 & 30 & 0.2 & 23.5 \\ Initial support & C25 & 29 & 0.2 & 23.5 \\ Foundation pit concrete & & & & \\ backfilling & & & & \\ \hline \hline \end{tabular} Note: The parameters in the table are the same as above except that E represents the modulus of elasticity.  
\end{table}
Table 2: Material parameters of the computational model.
## 2 Materials and Methods
### Definition and Design of the System's Components
#### 2.2.1 LED Driver
A review of the relevant literature revealed a straightforward passive equalizer circuit that introduces a pole and a zero to the system, achieving pole-zero compensation. This type of circuit, known as a phase-advance equalizer, is depicted in Figure 5.  
The phase-advance equalizer can be fine-tuned to introduce a zero at the point where the LED pole is located and a pole at a further point, subsequently defining the new bandwidth. Such tuning can be achieved by adjusting the values of passive elements. It is understood that the LED transfer function corresponds to a low-pass signal, where the pole frequency has been simplified to \(LED_{pole}\) in Equation (1). The parameters \(R_{d}\) = 81 m\(\Omega\) and \(C_{j}\) = 1.09 uF belong to the simplified equivalent circuit of the LED and were previously calculated and are shown in Figure 3.  
\[H_{LED}(s)=\Bigg{(}\frac{1}{R_{d}\cdot C_{j}}\Bigg{)}\cdot\Bigg{(}\frac{1}{s+s _{LED\_pole}}\Bigg{)} \tag{1}\]  
The transfer function of the phase-advance equalizer is represented by Equation (2), where \(equ_{zero}\) denotes the zero frequency and \(equ_{pole}\) indicates the pole frequency. \[H_{equ}(s)=\Bigg{(}\frac{R_{2}}{R_{1}+R_{2}}\Bigg{)}\cdot\Bigg{(}\frac{s+s_{ equ\_zero}}{s+s_{equ\_pole}}\Bigg{)} \tag{2}\]  
By integrating the equalizer module into the LED driver, the resultant transfer function is outlined in Equation (3). \[H_{LED\_driver}(s)=\Bigg{(}\frac{1}{R_{d}\cdot C_{j}}\Bigg{)}\cdot\Big{(}\frac {R_{2}}{R_{1}+R_{2}}\Big{)}\cdot\Bigg{(}\frac{1}{s+s_{LED\_pole}}\Bigg{)} \cdot\Bigg{(}\frac{s+s_{equ\_zero}}{s+s_{equ\_pole}}\Bigg{)} \tag{3}\]  
Figure 5: Phase-advance equalizer. As stated earlier, the zero frequency of the equalizer was set at the LED's pole frequency, while the pole frequency of the equalizer was adjusted to obtain the desired \(-3\) dB cutoff frequency in the resultant function. This adjustment led to Equation (4). Figure 4.0  
With \(LED_{pole\_1}\) = 1.8 MHz, the zero frequency of the equalizer (\(equ_{zero\_1}\)) and the pole frequency of the equalizer (\(equ_{pole_{1}}\)) were set to 1.8 MHz and 9 MHz, respectively. This configuration resulted in \(R_{1}\) = 3.9 k\(\Omega\), \(C_{1}\) = 22 pF, and \(R_{2}\) = 1 k\(\Omega\), as displayed in Figure 6a. Similarly, a second equalizer was proposed to increase the bandwidth from 9 MHz to 18 MHz. With \(LED_{pole\_2}\) = 9 MHz, the zero frequency of the equalizer (\(equ_{zero\_2}\)) and the pole frequency of the equalizer (\(equ_{pole_{2}}\)) were set to 9 MHz and 18 MHz, respectively. This set-up resulted in \(R_{3}\) = 1 k\(\Omega\), \(C_{3}\) = 25 pF, and \(R_{4}\) = 50 \(\Omega\), as shown in Figure 6b. After incorporating two equalization stages, a noticeable decrease in amplitude was observed. To rectify this effect, two amplification stages were introduced to balance the LED driver's frequency response, as displayed in Figure 4.1 The components selected for the LED driver are listed in Figure 4.2 The datasheets for the MOSFET transistor TN011 ON3-G and the amplifier LTC6268-10 can be found in [26], respectively.
# A Real Case of Implementation of the Future 5G City
## 1 Introduction
### Our Contribution  
This paper presents the testing and field trial environment for 5G that was launched in Italy at the end of 2017, in the cities of Prato and L'Aquila. Differently from other papers that present other field tests, this paper does not focus on a single key feature, on a specific type of scenario, or on an isolated network portion. The presented field trial environment is one of the first experiences of a complete integrated network implementing the most important 5G disruptive technologies. This paper presents a detailed description of the effective implementations, considered technologies, and incremental features that will be added during the project and 3 GPP standard evolution. Moreover, as 5G technology is not only an evolution of current mobile networks but also a platform for a wide range of smart services and applications, this paper shows how completely different use cases, with completely different KPIs, can be accommodated on the same platform thanks to 5G flexibility and reconfigurability. Indeed, the aim of the field trial environment is to test newly deployed technologies as well as the capability of 5G to create a new digital economy with a ubiquitous network that will connect people, things, and services. Vertical business use cases, specifically designed for the future 5G city that can have a strong economical and social impact, will be tested. In particular, the aim is to evaluate the capability of the 5G network of supporting innovative services with reference to suitably designed KPIs, and to evaluate the opportunities offered by these services. The tests are conducted using the experimental licenses on the 3.6-3.8 GHz frequency band released by the Economical Development Minister (MISE-Ministero per lo Sviluppo Economico) after a public competition responding to the European Commission's request for a timely and efficient 5G deployment as a strategic opportunity for Europe.  
The entire experimental project will last 2.5 years (up to June 2020) with a pre-commercial experimentation during the last year. In particular, according to the 3 GPP standardization process, the final 5G architecture will be tested starting from 2019 when almost all 5G features will be defined.  
The field trial is led by WindTre and OpenFiber, who joined their knowledge, infrastructures, and economical efforts. Both WindTre and OpenFiber are leading companies in the communication sector and own relevant infrastructures of radio networks and optical fiber backbones. Moreover, several other companies, utilities, research centers, and public administrations are involved as partners. Among these, ZTE provides NR access, core networks, and transport technologies.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Feed Configurations for Reflector Antenna
#### 3.2.4 Summary of Feed Configurations for Reflector Antennas  
Reflector antennas have various configurations, each with distinct advantages and trade-offs. The choice of feed architecture in spaceborne SAR systems depends on the specific mission requirements.  
(1) Single-beam feed is the most common, featuring a single horn antenna to generate a directional beam, while simple and cost-effective, it lacks beam-scanning capabilities, making it only suitable for basic SAR applications.  
(2) Multi-beam feeds can simultaneously generate multiple independent beams, expanding coverage, enhancing data transmission, and enabling beam scanning and reconfiguration. However, their scanning angles are limited and discontinuous, making them less flexible than PAF.  
(3) The phased array feed, combined with the beam scanning of the phased array antenna and the high-gain characteristics of the reflector antenna, can achieve flexible beam control. Therefore, they are suitable for HRWS imaging, avoid complex feed switching networks, improve system reliability and efficiency, and have broad potential in the future SAR field.  
Table 3 summarizes the characteristics and SAR applications of these three feed configurations.