esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application  
Figure 7 shows the experimental setup for the dual-polarization QAM data format transmission. A 28 Gbd 16 QAM base-band signal is created by using an arbitrary waveform generator (AWG, 65 GSa/s, 25 GHz bandwidth) with a pseudo-random bit sequence with a pattern length of 215-1 on four channels (IX/TY/QX/QY). A root-raised-cosine filter is applied with the roll-factor of 0.35 for Nyquist pulse filtering. Thermally stable nested double polarization lithium niobate Mach-Zehnder modulators that constitute two _I_/_Q_ optical modulators are driven by the AWG for data transmission. The encoded optical signal is transmitted for both back-to-back (B2B) configuration and over 100 km SSMF, respectively. The encoded signal is amplified by an erbium doped fiber amplifier (EDFA) and then an optical band pass filter is used to filter out the amplified spontaneous emission from the EDFA. At the receiver side, an optical modulation analyzer (OMA, 63 GSa/s, 23 GHz bandwidth) coherently receives the signals using a free-running local oscillator. Combined with the vector signal analyzer software package offered by the OMA, the sampled waveforms are processed with front-end correction and de-skewing, then compensated for chromatic dispersion and frequency offset. Afterwards, the signals are match-filtered and synchronized for time-domain equalization. Finally, the output 16 QAM signal is decoded for error-vector-magnitude (EVM) measurement and BER evaluation.  
Figure 8a shows the BER performance of each channel at the same received optical power of \(-\)10 dBm for 16 randomly selected channels in the B2B system and all individual channels after inserting 100 km SSMF. All BER values are exhibited below the hard-decision FEC (HD-FEC) limit (BER = 3.8 \(\times\) 10\({}^{-3}\)) [50] with B2B and after 100 km SSMF transmission.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **Vendor** & **Module** \\ \hline RF Signal Generator & Keysight & M8195A \\ RF Amplifier & Centellax & OA3 MHQM \\ EDFA & Amonics & AEDFA-PA-35-B-FA \\ Optical Filter & Santec & OTF-350 \\ DP-QPSK Modulator & Fujitsu & FTM7977 HQA \\ Modulator Bias Controller & ID Photonics & MBX \\ Optical Modulator Analyzer & Keysight & N4392A \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main equipment used in our DP-QAM format data transmission system.  
Figure 7: Setup schematic of a dual polarization QAM data format transmission system. The laser studied in this paper is used as the coherent transmitter source. All other equipment used is listed in Table 1.  
Figure 8b shows constellation diagrams for the channel at 1552.180 nm wavelength with B2B and after 100 km SSMF transmission. In the B2B configuration, a signal quality (\(Q^{2}[\text{dB}]\approx 20 log_{10}\Big{(}\frac{1}{EVM}\Big{)}\)) [44] higher than 20 dB is achieved. It drops to 18.7 dB when adding 100 km SSMF, which is probably due to the dispersion in the SSMF. The achieved aggregate line-rate data transmission capacity of the system is 12.5 Terabit/s (16 QAM 56 \(\times\) 28 GBd PDM).
## 2 Materials and Methods
### AF Analysis; HPLC  
For AF analysis, _A. flavus_ and _A. parasiticus_ were treated with thirteen compounds (described above) at the respective \"sub-inhibitory\" concentrations (Table 1), 28 \({}^{\circ}\)C, for 7 days. Results were based on three replicate PDA plates (60 mm \(\times\) 15 mm; Corning Inc. Life Sciences (Tewksbury, MA, USA)). The level of fungal growth at sub-inhibitory concentrations was initially determined based on fungal radial growth (% compared to untreated control) on PDA plates (average of triplicate) (See Section 3). AF analysis (AFB1, AFB2 for _A. flavus_; AFB1, AFB2, AFG1, AFG2 for _A. parasiticus_) was performed using an Agilent 1260 system (Palo Alto, CA, USA) with fluorescence detection at 365 nm excitation and 455 nm emission, as previously described [51]. Analysis was performed using OpenLAB CDS Chemstation Edition for LC & LC/MS Systems (Rev. C.01.08) (Agilent Technologies (Palo Alto, CA, USA)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NPs/Derivatives** & **Test Concentrations (mM)** & _A. flavus_ & _A. parasiticus_ \\ \hline
2.3-Dihydroxybenzaldehyde (2,3-D) & 0.1, 0.2 & B1, B2 & -1 \\
2.4-Dihydroxybenzaldehyde (2,4-D) & 0.5, 1.0 & -1 & G2 \\
2.5-Dihydroxybenzaldehyde (2,5-D) & 0.5, 1.0 & B1, B2 & -1 \\
3.5-Dimethoxybenzaldehyde (3,5-D) & 0.5, 0.75 & B1, B2 & G2 \\
2-Hydroxy-4-methoxybenzaldehyde (2H4M) & 0.1, 0.2 & B2 & -1 \\
2-Hydroxy-5-methoxybenzaldehyde (2H8M) & 0.1, 0.2 & B1, B2 & G2 \\ Salicylaldehyde (SLD) & 0.5, 1.0 & B2 & -2 \\ Salicylicylic acid (SA) & 0.5, 1.0 & B1 & G1, G2 \\
4-Isopropyl-3-methylphenol (4I3M) & 0.2, 0.4 & -1 & -1 \\ Thymol (THY) & 0.2, 0.4 & B2 & B1, B2, G1, G2 \\ Caravucci (CARV) & 0.2, 0.4 & B2 & B2, G2 \\
4-Methylcinamic acid (4-MEC) & 0.5, 1.0 & B2 & -1 \\
4-Methoxycinnamic acid (4-MOC) & 0.5, 1.0 & -1 & G1, G2 \\ \hline \hline \end{tabular}  
* No enhancement. 2 No growth.  
\end{table}
Table 1: Types of AF enhanced by NP or structural derivatives applied at sub-inhibitory concentrations to _A. flavus_ or _A. parasilicus_.
## 3 Results and Discussions
### Outdoor Performance
The red line indicates the front temperature of the simple panel, the blue one the enhanced panel. The black line is the average of the two thermocouples dipped into the PCM. The same analysis was conducted in regards to the thermal aspect. Table 4 summarized the results. The incident radiation was determined starting from data collected by the pyranometer. The heat absorbed (\(Q\)) was calculated with the following equations:  
\[Q(i)=m\times c_{p}\times\Delta T(i), \tag{2}\]  
where \(m\) is the mass and \(c_{p}\) is the specific heat of the PCM. \(\Delta T\)_(i)_ is the difference of temperature at the time \(i\) measured by the thermocouples. The thermal yield was defined as:  
Figure 12.0  
where the two summations were calculated over the reference period (solid state, phase change and liquid state). Results, as reported in Table 4, showed that the higher yield was obtained during the solid-liquid phase change (65%). The solid state presented a 30% yield and the worst case was the liquid phase with only 6%. Globally, the thermal yield of the proposed system was 30%. Worth noting is the correlation between the thermal and electrical efficiency. During the phase change, the enhanced panel could absorb a relevant amount of the incident radiation (65%). This allowed the panel to remain colder; in fact, during the same period, the largest increase in electrical efficiency was obtained (almost 2.5%, as reported in Table 3). The same way, once the melting process was completed, the thermal and electric yield reached the minimum level. The liquid PCM could no longer absorb as much heat as previously to keep the panel cool. As a result, the performance precipitated. The thermal aspect confirmed that, at the end of the phase change, an extraction system would allow the maximization of both the thermal and electric yield. Figure 16 shows the trend of temperature for all the thermocouples mounted on the proposed graphene/fins/phase change materials PV panel. The melting process during the morning hours and solidification process at night were clearly visible, highlighted in the figure. Both occurred at a nearly constant temperature, around 35 \({}^{\circ}\)C. This meant that, the day after, the PCM could start a new melting cycle, as it was completely solid. \begin{table}
\begin{tabular}{l c c c} \hline  & **Radiation (MJ)** & **Heat Absorbed (MJ)** & **Yield (\%)** \\ \hline Solid state & 0.376 & 0.117 & 31.1\% \\ Phase change & 1.062 & 0.906 & 65.6\% \\ Liquid state & 2.609 & 0.176 & 6.6\% \\ Global & 4.028 & 1.199 & 29.7\% \\ \hline \end{tabular}
\end{table} 
Table 4: Results of the thermal recovery in the different states. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Tavg PV** & **Tavg PV–PCM** & **AT** & **\(\Delta\) yield (\%)** \\ \hline Solid state & 32.1 & 30.9 & 1.12 & 0.56 \\ Phase change & 53.09 & 48.2 & 4.87 & 2.44 \\ Solid state & 47.09 & 49.8 & \(-\)2.79 & \(-\)1.40 \\ Global & 43.3 & 43.8 & \(-\)0.49 & \(-\)0.25 \\ \hline \end{tabular}
\end{table} 
Table 3: Results of the electric yield in the different states.
###### Abstract
Even if cloud services specified their privacy policies, it is not possible to guarantee that they follow their policies and will not (accidentally) transfer user data to another party [8]. In Figure 1b, Facebook is a public IdP that collects PII about users. After a user authenticates to Facebook, he can access an application in this domain (steps 1 and 2). Facebook may also disseminate user identity to the application on demand (step 3). According to the Facebook data scandal in early 2018 [9], an application was allowed to collect PII of 50 million users for \"academic\" use but gave the collected data further to a company, Cambridge Analytica, for \"analysis\" purpose. This example shows that users typically disclose their identities with an SP (e.g., SP1) over the frontend. In the backend, SP1 may consume another service (e.g., SP2) in a B2B relationship (step 4). SP1 may be dishonest or accidentally forward PII to SP2 without user control. This scenario raises a question: how do we support identity propagation between intermediaries but also protect unauthorised access at the same time? In the third scenario (Figure 1c), a company (e.g., Deutsche Telekom) offers its employees several applications hosted by an SaaS (e.g., Salesforce). Cloud services on Salesforce may require user identity exclusively. Thus, it may be necessary to provision user identity from Telekom to Salesforce,  
Figure 1: Motivation scenarios. (**a**) Cloud migration; (**b**) Identity propagation; (**c**) Identity federation. Note: Throughout this paper, we will use Salesforce, Facebook, and Telekom in our examples since they represent familiar examples, but we note that our examples are not tied specifically to these organisations and our concepts are equally applicable to other vendors and services. while keeping the authentication credential (e.g., username and password) locally at Telekom. After a user authenticates successfully at Telekom (step 1), he can access cloud services at Salesforce (step 2). This is a typical solution for a local company that uses a SaaS provider [10]. Thanks to identity federation, cloud services can query PII from the IdP of Salesforce without contacting Telekom and gain performance (step 3). The disadvantage is that Telekom must completely trust and delegate the control for disclosing its employee's data to Salesforce. This scenario raises a question: how do we protect PII when we disseminate it from a trusted domain to an _honest-but-curious_ one in identity federation? It means Salesforce may follow the protocol and computation that Telekom delegates to it, but may try to learn more information about the stored data. Even if the two companies specify privacy policies in their business contract, we cannot prevent a malicious host, malware, or an insider attack. Indeed, attackers have stolen 1.5 million personal data from the health authority in 2018 because the IdP had malware injected in it [11]. This scenario raises a question: how do we protect PII from an untrusted host?
## 2 Results
### Knocking Out the G56R Allele in Patient iPSCs  
We transfected G56R iPSCs with the gRNA1-expressing Cas9 plasmid to generate a clonal genome-edited G56R iPSC line. The EGFP-positive cells were single cell-sorted by FACS 48-h post-transfection. Two weeks post-sorting, four surviving iPSC colonies emerged. We expanded these clones and [PERSON] sequenced the mutational site in exon 2 of _NR2E3_ (Figure 3A). One clone (G56R-CRISPR1) showed no change following genome-editing, whereas the other three clones (G56R-CRISPR2, -3 and -4) showed the introduction of indels in the mutant allele (Figure 3B). The mutations were introduced 3-bp upstream of the PAM sequence, at the exact spot where Cas9 is predicted to induce a DSB. The clone G56R-CRISPR2 carried a deletion of two nucleotides (c.173_174 delAC), whereas the clones G56R-CRISPR3 and -4 had the same insertion of one nucleotide (c.174 insA). Thus, we only retained the clones G56R-CRISPR2 and -3 for further experiments. We verified the sequencing results by individually sub-cloning each allele of the CRISPR clones into the pGEM-T Easy vector system and then re-sequencing. We sequenced at least 10 colonies for each clone and identified a 1:1 detection ratio of G56R to WT alleles (Supplementary Figure S1).  
Figure 2: Design and efficiency testing of gRNAs. **(A)** Sequence of exon 2 of _NR2E3_ in the DNA of the _NR2E3_-adRP patient showing the position of the c.166G>A mutational site in red. The position of gRNA1 is indicated by a green line and the corresponding NGG PAM sequence is in bold and underlined in green. The position of gRNA2 is indicated by a blue line and the corresponding NGA PAM sequence is indicated in bold and underlined in blue. **(B)** Gel electrophoresis of the T7E1 assay in G56R and WT iPSCs that were mock-transfected or transfected with gRNA1 or gRNA2. The minus sign indicates the absence, and the plus sign indicates the presence, of T7E1. Molecular weight (MW) marker, 100 bp ladder.  
A predictive analysis of the protein sequence coded by the G56R-CRISPR2 and -3 clones identified a frameshift that resulted in a PTC, p.(His58 Leufts*82) for G56R-CRISPR2 and p.(His58 Glnrs*83) for G56R-CRISPR3. It is noteworthy that the new open reading frame was predicted to be identical for both G56R-CRISPR2 and -3, with the exception that the latter contained one extra amino acid (aa) residue at the point of insertion. According to the predicted protein sequences of the G56R-CRISPR2 and -3 clones, over half of the DBD (residues 44-120; www.uniprot.org) was removed, as well as the entire LBD (residues 192-410; 193-410; [12]) (Figure 3C).  
Taken together, the genome-edited G56R-CRISPR iPSC clones carried allele-specific indels, which were predicted to disrupt the formation of a full-length protein.
## 3 Design of Beam Steering and Adjustment Structure
### Analysis of Beam Adjustment Capability
#### 3.2.2 Tilt Error Correction Simulation  
When there is no tilt error in the subaperture, the light path is \(abcd\), as shown by the solid lines in Figure 14. When there is a tilt error \(\alpha\) in the subaperture, the light path is \(a^{\prime}bc^{\prime}d^{\prime}\), as shown by the dotted lines in Figure 14. B1B2 is an \(FSM\), and its tilting motion is driven by piezoelectric ceramics, with a compensation of \(\alpha^{\prime}\).  
According to the relationship in the figure, \(\angle cbb_{2}{}^{\prime}+\angle B_{2}{}^{\prime}bB_{2}=45^{\circ}\), we can derive \(45^{\circ}-\alpha+\alpha^{\prime}+\alpha^{\prime}=45^{\circ}\), i.e., \(\alpha^{\prime}=\alpha/2\). Therefore, when there is a tilt error \(\alpha\) in the subaperture, the tilt angle stroke \(\alpha^{\prime}\) that the piezoelectric ceramics can compensate for in the tilt error is:  
\[\alpha^{\prime}=\frac{1}{2}\alpha \tag{8}\]  
With the tilt error \(\alpha=1\)\(\mu\)rad image as the correction object, according to Formula (8), the piezoelectric ceramic motion for correcting the tilt error is:  
\[\alpha^{\prime}=\frac{1}{2}\alpha=0.5\)\(\mu\)rad (9)  
The tilt error correction commissioning is carried out by gradually adjusting the tilt of piezoelectric ceramics from \(\alpha^{\prime}=0\)\(\mu\)rad to \(\alpha^{\prime}=0.5\)\(\mu\)rad, and the corrected image is shown in Figure 15. It can be seen from Figure 14 that the tilt error is corrected when \(\alpha^{\prime}=0.5\)\(\mu\)rad.
# Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
## 3 Materials and Methods
### Multiple Polarization Roughness Retrieval
For the coherent component, the surface roughness (rms height) is computed isolating \(\sigma_{h}\) from Equation (4):  
\[\sigma_{h}=\sqrt{-\mathrm{ln}\left(\frac{\vec{\Gamma}_{0}^{coh}}{\left|R_{p} (\theta)\right|^{2}\cdot e^{-\frac{2\pi}{\cos(\theta)}}}\right)\cdot\frac{1}{ 4\cdot k^{2}\mathrm{cos}^{2}\theta}}. \tag{24}\]  
For the incoherent component, the squared mean square slope parameter is retrieved by isolating Equation (7):  
\[s=\sqrt{\frac{\left|R_{p}(\theta)\right|^{2}e^{-2\pi/\cos(\theta)}}{\vec{ \Gamma}_{0}^{inc}}}, \tag{25}\]  
\(\left|R_{p}(\theta)\right|^{2}\) is estimated using the SMAP L3 enhanced soil moisture product [48], and using the soil fraction from GLDAS [54]. This methodology assumes that the vegetation attenuation, modeled through VOD, is the same for all polarizations, and it is estimated from the SMAP L3 product. The two methodologies described in Equations (24) and (25) are able to link the coherent and the incoherent regime to a magnitude that is proportional to the Fresnel reflection coefficient, as also modeled in [33]. The physical meaning of the rms height (\(\sigma_{h}\)) is, as defined in the literature [42], the 2-D standard deviation, using a certain window, of the digital elevation model (DEM). However, it should be noted that computing this value directly from a DEM map may result in scaling effects if pixel upscaling is performed, as detailed in [55]. The units that we will use to express the rms height in centimeters. Additionally, the rms slope (\(s\)), which is a unitless quantity, represents the standard deviation of the surface slope, defined as the derivative of a given pixel with respect to its neighboring pixels [42].
Since its inception, HYPAD has been verified in several engineering applications, such as linear elastic analysis [50], fatigue [51; 52], thermoelasticity [53; 54], fracture mechanics [55; 56; 57], plasticity [58], viscoelasticity[59], residual stresses [60], creep [61], heat transfer [62], variance estimates [63], localization analysis [64] and system reliability analysis [65], among others. In this work, we introduce a novel differentiation method for eigenvalues and eigenvectors that employs a single expression to accurately compute the machine precision sensitivities, regardless of the order of derivatives involved. This approach stands in contrast to existing methods, which often prove cumbersome to implement, particularly when dealing with higher-order sensitivities. By introducing this novel approach, we aim to simplify the implementation process and overcome the challenges associated with computing higher-order sensitivities in eigenvalue problems. Here, HYPAD is formulated within a discrete continuum approach based on the Finite Element Method (FEM) to obtain highly accurate arbitrary-order sensitivities of the mass and stiffness matrices with respect to any input parameter. Then, this sensitivity information is linked with the expressions to calculate the sensitivities of eigenvalues and eigenvectors presented by [PERSON] and [PERSON] [17] and [PERSON] et al. [33], which are also numerically differentiated using HYPAD. It should be noted that although the method is presented using the FEM, it can be implemented using any discretization and spatial integration, such as the Boundary Element Method (BEM), isogeometric analysis (IGA) or the Spectral Finite Element Method (SFEM). The outline of the paper is as follows: Section 2 provides background information, including the expressions for the calculation of sensitivities in eigenfrequency problems derived by [PERSON] and [PERSON] [17] and [PERSON] et al. [33], and a brief introduction to HYPAD. Section 3 introduces the new methodology to calculate arbitrary-order sensitivities of the eigenvalues and eigenvectors on eigenfrequency problems using HYPAD. Section 4 provides an analytical and numerical verification example of the new methodology used to compute eigenvalue and eigenvector sensitivities using HYPAD. The analytical example corresponds to a simple harmonic oscillator, and the numerical example corresponds to the free vibration of a rectangular cross-section cantilevered beam. Section 5 discusses the key issues of the methodology and expansions to other fields. Finally, in Section 6, conclusions and future work are presented.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Mobility Management  
Sixth Generation networks will serve a spectrum of mobile applications such as the IoV, IoRT, and IoMT that require low latency and highly reliable services. To guarantee the QoS for these applications while improving the resource utilization and network bottleneck, it becomes crucial to learn and predict users' movements. DL-based approaches can be an alternative solution, as shown in Figure 10. [PERSON] et al. [63] proposed a mobile user trajectory prediction algorithm by combining LSTM with RL. LSTM is used to predict the trajectories of mobile users, whereas RL is used to improve the model training time of LSTM by finding the most accurate neural network architecture for the given problem without human intervention. An initial learning rate of 0.002 is selected for LSTM and a Q-learning rate and discount factor of 0.001 and 1 are used for RL. [PERSON] et al. [64] proposed ANN models for cell-level and beam-level mobility management optimization in the wireless network. The results showed that DL-based approaches outperform the conventional 3 GPP approach for mobility management.  
Figure 10: Deep Learning (DL)-based mobility management for Internet of Things (IoT) devices and users.
# General Moving Object Localization from a Single Flying Camera
## System Design
### Calibration
#### 3.2.2 Baseline Calibration
To measure the length of the baselines, we made the drone fly parallel to a wall with a specific tiling pattern, taking images as mentioned above. From the images, we can determine the positions of the drone where the images were taken and measure the distances between them. We repeat the experiment five times and take the average of these distances as our calibrated baselines. The baseline between any two images in the sequence is simply the sum of the distance values between the two drone positions.
## 3 Video Application Based on Deep Learning Algorithm
### Discriminant Deep Architecture
#### 3.2.1 CNN
In order to improve the frame rate of edge processing, this paper further proposed a depth object tracking algorithm based on an adaptive multimodal model which is robust to object occlusion and changing lighting conditions. [PERSON] et al. [55] aimed to develop an efficient system that can meet the needs of video analysis based on dynamic content and expand to large-scale traffic camera video data. The proposed system used a YOLO-based deep learning method to identify objects in video data. This information was then processed and analyzed by the analysis layer implemented using Spark and Hive. Compared with the traditional method, the accuracy of this system can reach more than 80%.