esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characteristics of Silver Colloidal Solution  
The electrophoretic mobility (zeta potential) of an AgNP solution allows for a description of the electrical interaction forces between the dispersed particles in colloid solutions and the characterization of the silver nanoparticle system in regard to all compounds in the solution. The stabilization effect (resultant from electrostatic and steric stabilization) was estimated by measuring their electrophoretic mobility values for a fresh-prepared solution, as well as for the same solution after one month. The zeta potential values were found to be quite low, in the range of \(-3\times 10^{-1}\) mV to \(-7\times 10^{-1}\) mV without significant changes over a month. The silver concentration was determined as 43.5 mg/L by atomic absorption spectrometry. The spectroscopic characterization of the AgNP solution is shown in Figure 2. UV-visible spectra indicate the presence of AgNP in the solution phase with \(\lambda\) max= 400 nm (changes in the solution color from colorless (typical for the solution of silver precursor) to yellow-brown (typical for the silver nanophase)).The low half-width of the plasmonic peak and the lack of significant differences in its width for one month from the moment of synthesis suggest a stable system with low polydispersity.  
Figure 2: (**A**) UV-Visible Spectra of as-synthesized silver nanoparticles and the same solution after one month and (**B**,**C**) the corresponding TEM images of AgNP in AgNP solution.
# Functional Characterization of _Saccharomyces_ Yeasts from Cider Produced in Hardanger
## 2 Materials and Methods
### Determination of Sugars, Acids, and Ethanol  
Reagents, Materials, and Standards for HPLC-UV/RI Analyses  
Chemicals: we used glucose (99%) (Acros Organics, Fair Lawn, NJ, USA), fructose (99%) (Acros Organics, Fair Lawn, NJ, USA), sucrose (99.9%) (Acros Organics, NJ, USA), tartaric acid (Alfa Aesar, Karlsruhe, Germany), lactic acid (30%) (Sigma, Steinheim, Germany), D-L malic acid (99%) (Aldrich, Steinheim, Germany), and citric acid (99.9%) (Sigma, Steinheim, Germany). Concentrated sulfuric acid (VI) was purchased from VWR Chemicals (Leuven, Belgium).  
Three in-house developed HPLC-UV/RI methods were used for the determination of sugars, organic acids, and ethanol in cider samples, respectively. An Agilent 1100 series HPLC system (Agilent Technologies(r), Palo Alto, CA, USA) was equipped with Agilent OpenLab CDS ChemStation 2.3.54 software, a UV detector (G1314A VWD) for the analysis of organic acids (detection at 210 nm), and a refractive index detector (model G7162A) for the analysis of sugars (fructose and glucose) and ethanol. Samples were filtered using Polytetrafluoroethylene (PTFE) 0.45 \(\upmu\)m syringe filters (VWR(r) International, Radnor, PA, USA). For the determination of glucose and fructose, 4 \(\upmu\)L of each sample was injected onto a Phenomenex Luna(r) Omega Sugar HPLC column (150 mm long and \(\upsigma\) of 4.6 mm, particle size of 3 \(\upmu\)m) with a precolumn (5 mm long and \(\upsigma\) of 4.6 mm, particle size of 3 \(\upmu\)m) using a mobile phase of acetonitrile/water = 75:25 (\(v/v\)) at a flow rate of 0.9 mL/min and a run time of 15 min [21]. Separation of the organic acids was performed on two HPLC columns, which were coupled sequentially: Phenomenex C18 Kinetex F5 (dimensions150 \(\times\) 4.6 mm with a particle size of 2.6 \(\upmu\)m) with a precolumn (5 mm long and \(\omega\) of 4.6 mm, particle size of 3 \(\upmu\)m) and a Phenomenex C18 Kinetex EVO (dimensions 250 \(\times\) 4.6 mm with a particle size of 5 \(\upmu\)m) kept at 30 \({}^{\circ}\)C during analyses. The injection volume was 4 \(\upmu\)L, the mobile phase consisted of 5 mM H\({}_{2}\)SO\({}_{4}\), the flow rate was 0.7 mL/min, and the run time was 20 min [22]. Ethanol was evaluated on a multimodal ROA Organic Acid H+ (8%) column (Phenomenex) with a size of 300 \(\times\) 7.8 mm and a sample injection volume of 5 \(\upmu\)L. Isocratic elution was performed using 5 mM H\({}_{2}\)SO\({}_{4}\) as the mobile phase with a flow rate of 0.9 mL/min and a run time of 25 min [23]. All HPLC columns were kept at 30 \({}^{\circ}\)C during chromatographic analyses.  
Method validation data are summarized in Table S2 (in Supplementary Materials).
## 2 Background on the Calibration of Satellite Sensors
### Compensation Factors  
The reduction of uncertainties in a cross-calibration procedure depends on the compensation of a series of variables, such as differences in illumination geometry, reflectance anisotropy effects and discrepancies between the Spectral Response Function (SRF) of the instruments, amongst others [19; 21; 61; 62; 63; 64].  
According to the cross-calibration approach proposed by [PERSON] et al. [19; 20], two main compensations must be conducted. The first one is performed applying an illumination adjustment factor that includes the solar zenith angle at the time of acquisitions and the exo-atmospheric solar irradiance coefficients of both sensors. Since there is evidence that SRF is of critical significance, the second compensation is related to the spectral characteristics of the instruments. Two sensors that simultaneously observe the same land cover, at equivalent wavelengths, will offer different results, and a Spectral Band Adjustment Factor (SBAF), as proposed by [PERSON] et al. [21], can be used to solve this source of mismatch.  
Several studies have assessed the impact of the SRF differences on reflectance and spectral indices [2; 17; 61; 62; 63; 64; 65; 66; 67]; systematic biases of up to 30% in reflectance and 10% for vegetation indices have been reported, depending on the chosen sensors and their spectral bands. Data acquired by hyperspectral satellite and airborne sensors, such as EO-1 Hyperion, Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) and Airborne Prism Experiment (APEX), along with radiative transfer modeling, have been utilized to compensate existing differences.  
A detailed study on the assessment of other sources of uncertainty and their influence on the radiometric cross-calibration can be found in [PERSON] et al. [8].
# DAB Signal Preprocessing for Passive Coherent Location
## 2 Materials and Methods
### DAB Signal Preprocessing
#### 2.4.2 Insertion of Synthetic Noise  
As described previously, the time instant of the start of the frame (\(n_{0}\)) is determined with envelope detection. After that, the signal samples carrying the null, PR, and FIC symbols from both the reference and the surveillance channel are replaced by the complex noise  
\[\begin{split} x(n)w(n),\\ x_{\mathrm{s}}(n)w_{\mathrm{s}}(n),\\ n=n_{0}-N_{\mathrm{margin}}-N_{\mathrm{null}},\ldots,n_{0}+4 \times N_{\mathrm{symb}}\end{split} \tag{6}\]  
where \(w(n)\) and \(w_{\mathrm{s}}(n)\) are statistically independent realizations of the Gaussian noise (with their variances scaled to the estimated power level of the DAB signal), and \(N_{\mathrm{null}}\) is the duration of the null symbol (in samples) that is slightly longer than in case of the data symbol (\(N_{\mathrm{symb}}\)) [14]. The frame structure after this modification is shown in Figure 8.  
The received signals have constant power and no emission discontinuities, thanks to this modification. The periodical peaks are not visible in the signal's autocorrelation function, as shown in Figure 7.  
Figure 8: DAB frame structure with Null, PR, and FIC symbols substituted by noise.  
Figure 7: Autocorrelation function of DAB signal after clearing of PR and FIC symbols (**a**) and its close-up (**b**).
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 5 Conclusions  
In this paper, a wireless communication system adopting I/Q modulation to correct the impairment on transmitted OFDM symbols is proposed, while the FH-BOC signal behaving as quadrature branch is capable of providing sufficient measured information on prompt channel conditions, where the signals are designed to be overlapping in the power spectrum to efficiently exploit valuable bandwidth resources. Notably, a large Doppler shift with the symbol distortion of ISI has been considered, since relative movements nearly always happen during space-to-air propagation and a situation of the relative  
Figure 16: Bit error rate performance of transmitted signal.  
velocity between the transceiver assumed as 12.5 km/s. Based on theoretical analysis and experiment results of the established communication system model, the Doppler effect imposed distortion on the transmitted signal waveform, which is mitigated from near 75% to 3.42% under different Doppler situations, in comparison with the RMSE performance. The effectiveness of the proposed signal has been shown through simulation, in order to prove it retains better resilience against channel interference, requiring about 8 dB less energy at the desired BER level per original, unmodulated, and uncoded information bits in the OFDM symbols, without any pilot symbol appended, referring to the advancement on data bits transmission capacity.  
Z.R.: conceptualization, formal analysis, methodology, software, validation, writing--original draft, writing--review and editing; XL. and LX.: conceptualization, formal analysis, methodology, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (No. 2021 YFB3900300) and the Fundamental Research Funds for the Central Universities (No. 2022 CDJKYJH036).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Triaxial Compression Calibration Test and Verification
### Test and Numerical Simulation of Triaxial Compression in Wet Sand  
#### 3.2.1 Triaxial Compression Test in Wet Sand  
A triaxial test was conducted using the electronic dynamic-static fatigue testing machine at the Huazhong University of Science and Technology, as illustrated in Figure 6. The experimental material chosen consisted of wet sand characterized by particle sizes ranging from 1 mm to 2 mm with an aggregate weight of 80 g. The moisture content of the sand was measured to be 6.98%, with a pH value of 7.2. The moisture content is used to characterize the aggregation of the system. The density of the sand was measured before the experiment and found to be 2 g/cm\({}^{3}\). The samples were loaded into acrylic cylinders with an outer diameter of 45 mm, an inner diameter of 35 mm, and a height of 85 mm, with a loading height of 60 mm. The loading and unloading speeds during the experiment were set to 0.5 mm/s and 0.2 mm/s, respectively. The data were sampled at a frequency of 200 Hz, and the load threshold of the testing machine was set to 600 N. Based on the experimental results, it was observed that the sand exhibited significant differences in stiffness between loading and unloading, with the nonlinear behavior being more pronounced during the loading phase.
## 3 Methodology
### Experimental Design
#### 3.3.2. In Situ SSM Assimilation  
As a first step before using remote sensing-derived SSM products, the irrigation retrieval approach is tested using in situ measurements of SSM. The algorithm is evaluated over three (3-, 6- and 12-day) window lengths to mimic the temporal resolution of the available SSM products over the entire growing season. The in situ data base is composed of ten growing seasons, including 4 drip-irrigated, 3 flood-irrigated and 4 rainfed seasons (one of which 2015-2016 being bare soil). For the four rainfed seasons, the same irrigation routine (amounts, particles... ) as the flood technique is applied but without any constraint on the timing for the three windows.
## 5 Experiment Verifications  
### Kinematics Experiment  
The CDPS mechanism is constructed to verify the inverse kinematic and dynamic models. The upper platform and lower platform are made of steel, while the upper shaft is made of epoxy to provide the electrical insulation. The cables are made of polypropylene to provide a strong endurance of the pulling stress and hydrophobic surface to prevent water films. Four motors with 2.12 Nm torque drive four cables to rotate the upper platform in three axes. The cable forces are measured to compare with the simulation results. The system setup is shown in Figure 10. The radius of the upper and lower platforms is 74 mm. The length of the upper and lower shafts is 50 mm. The insulating plate is 15 mm thick (Figure 10).  
The forces of the cables are measured when the mechanism is set to different attitudes to compare the results of the simulation model. The attitude combinations are \(\theta_{m}\) = \(\pi\)/2, \(\theta_{b}\) = \(\pi\)/30, \(\theta_{r}\) = 0; \(\theta_{m}\) = 0, \(\theta_{b}\) = \(\pi\)/10, and \(\theta_{r}\) = 0; and \(\theta_{m}\) = 0, \(\theta_{b}\) = 0, and \(\theta_{r}\) = \(\pi\)/4, respectively (Figure 11). The four  
Figure 10: The force measuring experiment setup.  
Figure 9: Electric field distribution along the field line.  
The errors between measured values and simulation results are calculated in Figure 13 when the mechanism maintains the attitude in Figure 11b.  
The errors between measured values and simulation results are calculated in Figure 14 when the mechanism maintains the attitude in Figure 11c.  
In Figures 12-14, the average error between simulation and experiment results is 7.18%. The error increases to 43.7% when the cable force increases to a relatively large value (\(T_{2}\) and \(T_{4}\)). This increase occurs because the increasing friction between cables and platforms is neglected, and the cable elongation due to the large stress of the bending spring is not included in the model.
# Optimal Base Station Density of Dense Network:
## Appendix D  
Since \(\kappa(\lambda_{\mathrm{m}})\) is the infinite double integral of a transcendental function, it does not have a simple form for fast calculation. We perform curve fitting to approach \(\kappa(\lambda_{\mathrm{m}})\) and thus get the empirical formula. Observing the data of \(\kappa(\lambda_{\mathrm{m}})\), we choose the fitting function as  
\[\kappa_{\mathrm{fit}}=C_{1}\lambda_{\mathrm{m}}^{C_{2}}-C_{3}\lambda_{ \mathrm{m}}^{C_{4}}, \tag{15}\]  
where \(C_{1},\ C_{2},\ C_{3}\) and \(C_{4}\) are the functions of \(P_{\mathrm{m}}\). Then, we conduct polynomial fitting to get the functions of \(C_{1},\ C_{2},\ C_{3}\) and \(C_{4}\). Since the maximum power of SmBS (e.g., WLAN) is limited by local regulations, the power range of SmBS that we consider in curve fitting is \(P_{\mathrm{m}}\in[0.001P_{\mathrm{M}},0.006P_{\mathrm{M}}]\). The order of polynomial is carefully selected to avoid both underfitting and overfitting. The results are achieved via MATLAB (R2016b, MathWorks Inc., Natick, MA, USA) and given as follows:  
\[\begin{split} C_{1}=&-8.5658232\times 10^{14}P_{ \mathrm{m}}^{6}+1.7940284\times 10^{13}P_{\mathrm{m}}^{5}-1.4773754\times 10^{11}P_{ \mathrm{m}}^{4}+6.2145778\times 10^{8}P_{\mathrm{m}}^{3}\\ &-1.3725986\times 10^{6}P_{\mathrm{m}}^{2}+1.2067786\times 10^{3}P _{\mathrm{m}}+1.1487479,\end{split} \tag{16}\]  
\[C_{2}=-5.9245046\times 10^{6}P_{\mathrm{m}}^{3}+7.9721220\times 10^{4}P_{ \mathrm{m}}^{2}-2.6894727\times 10^{2}P_{\mathrm{m}}-1.7117077, \tag{17}\]\[C_{3} = 5.1151868\times 10^{14}P_{\rm m}^{6}-9.2334191\times 10^{12}P_{\rm m }^{5}+6.0667929\times 10^{10}P_{\rm m}^{4}-1.8492164\times 10^{8}P_{\rm m}^{3} \tag{100}\] \[+2.6211819\times 10^{5}P_{\rm m}^{2}-1.3845104\times 10^{2}P_{\rm m}- 0.003196,\] \[C_{4} = -2.6498472\times 10^{15}P_{\rm m}^{6}+6.4364873\times 10^{13}P_{\rm m }^{5}-6.1989791\times 10^{11}P_{\rm m}^{4}+3.0240235\times 10^{9}P_{\rm m}^{3}\] (101) \[-7.8793336\times 10^{6}P_{\rm m}^{2}+1.0192004\times 10^{4}P_{\rm m }-5.7462256.\]  
The goodness of fit is 92.42-99.78% in the range of \(P_{\rm m}\).
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 3 Multi-Network Prior Reconstruction Method
### Unet Network  
The structure of Unet is shown in Figure 3. In order to improve the feature extraction ability of the network, the network is designed in three aspects: (1) In coding, three down-sampling branches perform multi-feature extraction on images from two aspects: different feature map scales and receptive sizes of different convolution kernels. Each down-sampling branch contains three down-sampling residual blocks. Although these three branches have the same structure, the convolution kernel sizes in the network are 3, 5 and 7, respectively, and the weights between them are not shared. (2) In decoding, it is composed of three up-sampling sub-pixel blocks, and sub-pixel interpolation with a magnification of 2 is used instead of deconvolution to improve resolution. Sub-pixel interpolation combines the pixel values of the corresponding positions of the adjacent four channels into a 2 \(\times\) 2 pixel block in order. (3) Residual network and attention mechanism are also used to improve the reconstruction efficiency. The residual structure usually adds the shallowest information of the network directly to the deepest layer of the network. The excessive information difference between the two may increase the training difficulty of the network. Therefore, the shallow layer of the network is fused with the middle layer of the network, and the fused information is sent to the next up module. Each layer of the up module contains sub-pixel convolution, Mish activation function and attention mechanism. The role of sub-pixel convolution is to improve image resolution and avoid image blurring caused by traditional interpolation methods. The role of the attention mechanism focuses on key information, dynamically allocates weights, and enhances the interpretability of the model. The role of the residual network is to accelerate the convergence speed and avoid gradient disappearance or gradient explosion.  
Figure 2: This is the structure of the SAE Network.
# A Reconfigurable Radio-Frequency Converter IC in 0.18 \(\upmu\)m CMOS
## 3 System Architecture
### Frequency Synthesizer
For this reason, the oscillator in this work will is composed of two independent LC-VCOs that cover, respectively, the range from 1.35 GHz to 2 GHz and from 2 GHz to 2.7 GHz, and that incorporate a 7-level switched capacitor bank for coarse tuning. Figure 5b shows the circuit diagram of the oscillator in this work.
## 1 Introduction
In this paper, we leverage deep reinforcement learning (DRL) to enhance network performance, eliminate long-term cumulative rewards, and design an efficient, jointly optimal approach to address resource priority and demanding resource management. Hence, the system utilizes the deep q-network (DQN) algorithm. This approach tackles the challenge of managing system states and actions. The DQN selects optimal actions within a continuous space, making it perfect for this dynamic environment. The prioritized tasks by learning from a reward system are considered successful task completion and efficient resource usage. As demands change, DQN constantly re-evaluates the situation, adapting resource allocation to meet immediate needs while keeping long-term priorities. DQN bridges priority and demand management, ensuring critical tasks are completed while optimizing resource utilization for IIoT tasks. This paper's contributions are as follows:  
* We study efficiency resource management, which enables O-RAN to provide gratitude support for multi-vendor and scalable deployment of MEC servers and improve resources based on the task demands and service priority. * Then, the problem of resource and energy minimization is conducted to transform into a Markov decision process (MDP). After, we design a novelty distributed DRL-driven resource management policy in the proposed model, which jointly optimal resource and priority/demand based on IIoT criteria usage. Our proposed DQG-PD algorithm improves resource management efficiency and reduces task processing time and latency to enhance efficient resource awareness of IIoT applications. * We enhance network energy efficiency optimization based on the DQN approach. Leveraging the DQN approach, which decouples two stages (e.g., online network and target network) to respond to the network performance by stabilizing long-term learning while enabling rapid adaptation to immediate demands. * Lastly, we conduct experiments to evaluate and show the witness that our network scenario outperforms reference schemes. In the rest of the paper, Section 2 gathers the previous studies and motivation to address our work. Section 3 is considered a problematic formulation for optimal resource efficiency while computing and communicating IIoT device tasks. Moreover, we consider the characteristics of different rewards and three setting network scenarios. Simulation results and further analysis are demonstrated in Section 4. Section 5 summarizes the conclusion.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 2 Cognitive Radio Networks  
In contrast to classical ad-hoc networks, CRAHNs have some key distinguishing factors. The most important factor is changing the spectrum environment over an ample frequency range, varying over time and space. Classical ad hoc networks use pre-chosen channels that stay unaltered for a duration of time [28]. This also creates a problem for CRAHNs to control the topology of the network. In traditional ad hoc networks, topology control has been done through the use of beacons [29]. Though, in CRAHNs, due to a large range of possible spectrum, this is not practical, because it is more likely that CRAHNs have incomplete topology information. Moreover, being multi-hop and multi-channel make the arrangement of routing in CRAHNs a very challenging task compared to the classical ad hoc networks [30; 31; 32].  
A CR device must be able to collect information on the spectrum usage in its surroundings to know the set of available channels to be used for communications in an opportunistic way. A cognitive user can only access the idle frequency band, and when a signal from a PU is detected, the cognitive user must vacate the allocated spectrum in order to avoid the generation of interference to PUs, this is called spectrum mobility. As the control and management of communication over wireless channels occur at the MAC layer, other relevant tasks for CRs as sensing the channel, spectrum sharing, resource allocation and spectrum handoffs become essential for connectivity.  
Since there is no central entity for the operation of ad hoc networks, the previously-mentioned MAC functionalities impose an extra challenge on ad hoc networks. For example, spectrum sensing in CRAHNs is not controlled and synchronized by the focal network element, but by each user, i.e., in a distributed way. Thus, in CRAHNs, the need for cooperation with neighboring nodes for distributed functionality (e.g., spectrum sensing, sharing and access) is increased.