esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Genetic Approaches in Identification of mTOR Signaling Components
#### 4.2.8 Rab35 Was Identified as an Oncogenic GTPase Activating Akt by a Genetic Screen  
To search for potential regulators for the PI3K/Akt signaling, [PERSON] et al. used a collection of 7450 shRNAs targeting kinases or GTPases to perform a loss-of-function genetic screen in HeLa cells using an in-cell immunofluorescence assay to monitor Akt-pS473 signal changes [71]. Rab35 stood out from this screen as a hit necessary for Akt activation and subsequent genetic and biochemical assays indicated Rab35 works upstream of PDK1 and mTOR2 to mediate PI3K-controlled Akt activation [71], while the detailed molecular mechanism remains to be determined.  
2.9 Ribosome Association Was Identified to Control TORC2 Activation by a Reverse Suppress Genetic Screen in Yeast  
To identify mTOR2 upstream activators, [PERSON] et al. performed a reverse suppressor screen in budding yeast based on the observation that a yeast YPK2-D239A mutant [72] could rescue cell lethality caused by _TORC2_ defect [73; 74]. [PERSON] et al. hypothesized that potential mTOR2 activators are also depended upon YPK2-D239A for survival. This screen led to isolation of 44 independent mutants including mutants in core TORC2 complex such as TOR, AVO1, and AVO3, thus validating the screen [72]. More importantly, a _NIP7_ temperature-sensitive mutant was also identified as a hit. Further genetic and biochemical assays in yeast and mammalian cells demonstrated that NIP7 was necessary for mTOR2 activation upon growth factor stimulation by regulating ribosome biogenesis but not protein synthesis, and ribosome association with mTOR2 was critical for insulin-PI3K-induced mTOR2 activation both in vitro and in cells [72].
# Formation Control and Distributed Goal Assignment for Multi-Agent Non-Holonomic Systems
## 11 Simulation Results for Saturated Wheel Controls  
As the APFs used to avoid collisions are unbounded, the algorithm should be tested for the limited wheel velocities (resulting from the motor velocity limits). This section presents a numerical simulation for the mobile robots with the wheel diameter of 0.0245 m, the distance between wheels amounting to 0.148 m and the maximum angular velocity of 48.6 rd/s.  
A special scaling procedure was applied to the wheel controls. The desired wheel velocities were scaled down when at least one of the wheels exceeds the assumed limitation. The scaled control signal \(u_{\mathit{i  
\[\omega_{lo}=\max\{|\omega_{iR}|,|\omega_{iL}|\}, \tag{43}\]  
where \(\omega_{iR}\), \(\omega_{iL}\) denote right and left wheel angular velocity, \(\omega_{max}\) is the predefined maximum allowed angular velocity for each wheel.  
Figure 13a,b show time graphs of the right and left wheels of the platforms. As in the previous experiments, signals of three robots (out of 48) are highlighted in black. It can be clearly observed that both of them were limited to \(\pm\)48.6 rd/s. Linear and angular velocities of the platform are shown in Figure 13c,d. Their velocities are lower in comparison to the non-limited case (refer to Figure 12e,f). Figure 13e presents relative distances between the robots. The area below dashed line represents the collision region. It can be seen that no pair of robots has reached it--no collision occurred during this experiment.  
Figure 13: Numerical simulation 3: trajectory tracking for \(N=48\) robots with goal exchange and saturated wheel controls. (**a**) right wheel velocities, (**b**) left wheel velocities, (**c**) linear velocity controls, (**d**) angular velocity controls, (**e**) distances between robots.
# Bridge Crack Detection Based on SSENets
## 2 Materials and Methods
### Atrous Spatial Pyramid Pooling Module  
In crack detection task, cracks only occupy a small proportion of the image, and the width of cracks is quite different. Conventional convolution cannot be used for multi-scale analysis of cracks with different widths, which is not conducive to fully capturing the features of cracks. The Atrous Spatial Pyramid Pooling (ASPP) module [35] uses atrous convolutions with different rates to extract multi-scale features of cracks. As shown in Figure 5, the structure of ASPP module contains 5 parallel sub-networks. The first part obtains global information through the global average pooling while the remaining four parts use atrous convolutions with multi-sample rates of 1, 3, 7, and 11. The parallel atrous convolutions are processed by depthwise separable convolution in order to reduce the model complexity. Since the ASPP module captures the contextual information of cracks on multiple scale, it could improve the detection accuracy.
## 2 Results and Discussion
### Antioxidant Capacity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Tissue** & **Temperature** & **TP (mg GAE/100 DW g)** & **TF (mg RE/100 DW g)** & **Flavanols (mg/100g DW)** & **\(\omega\)-HP (mg/100 g DW)** \\ \hline \multirow{4}{*}{Leaves} & 25 \({}^{\circ}\)C & 844.21 \(\pm\) 8.92 \({}^{\text{b}}\) & 39.24 \(\pm\) 0.22 \({}^{\text{b}}\) & 0.195 \(\pm\) 0.22 \({}^{\text{b}}\) & 0.874 \(\pm\) 0.23 \({}^{\text{b}}\) \\ \cline{2-6}  & 30 \({}^{\circ}\)C & 940.73 \(\pm\) 9.7 \({}^{\text{a}}\) & 45.34 \(\pm\) 0.35 \({}^{\text{a}}\) & 0.292 \(\pm\) 0.052 \({}^{\text{a}}\) & 0.924 \(\pm\) 0.21 \({}^{\text{a}}\) \\ \cline{2-6}  & 35 \({}^{\circ}\)C & 652.49 \(\pm\) 4.8 \({}^{\text{c}}\) & 17.23 \(\pm\) 0.11 \({}^{\text{c}}\) & 0.117 \(\pm\) 0.23 \({}^{\text{c}}\) & 0.243 \(\pm\) 0.11 \({}^{\text{c}}\) \\ \hline \multirow{4}{*}{Whole seedling} & 25 \({}^{\circ}\)C & 615.26 \(\pm\) 3.8 \({}^{\text{b}}\) & 26.87 \(\pm\) 0.23 \({}^{\text{b}}\) & 0.211 \(\pm\) 0.23 \({}^{\text{b}}\) & 0.854 \(\pm\) 0.25 \({}^{\text{b}}\) \\ \cline{2-6}  & 30 \({}^{\circ}\)C & 785.26 \(\pm\) 6.4 \({}^{\text{a}}\) & 35.24 \(\pm\) 0.29 \({}^{\text{a}}\) & 0.255 \(\pm\) 0.32 \({}^{\text{a}}\) & 0.875 \(\pm\) 0.29 \({}^{\text{a}}\) \\ \cline{2-6}  & 35 \({}^{\circ}\)C & 524.36 \(\pm\) 4.6 \({}^{\text{c}}\) & 18.69 \(\pm\) 0.12 \({}^{\text{c}}\) & 0.184 \(\pm\) 0.36 \({}^{\text{c}}\) & 0.714 \(\pm\) 0.22 \({}^{\text{c}}\) \\ \hline \multirow{4}{*}{Roots} & 25 \({}^{\circ}\)C & 154.26 \(\pm\) 1.4 \({}^{\text{b}}\) & 20.15 \(\pm\) 0.12 \({}^{\text{b}}\) & 0.194 \(\pm\) 0.16 \({}^{\text{b}}\) & 0.711 \(\pm\) 0.23 \({}^{\text{b}}\) \\ \cline{2-6}  & 30 \({}^{\circ}\)C & 191.24 \(\pm\) 1.7 \({}^{\text{a}}\) & 26.25 \(\pm\) 0.13 \({}^{\text{a}}\) & 0.212 \(\pm\) 0.15 \({}^{\text{a}}\) & 0.741 \(\pm\) 0.31 \({}^{\text{a}}\) \\ \cline{2-6}  & 35 \({}^{\circ}\)C & 142.24 \(\pm\) 1.1 \({}^{\text{c}}\) & 14.25 \(\pm\) 3.10 \({}^{\text{c}}\) & 0.165 \(\pm\) 0.11 \({}^{\text{c}}\) & 0.658 \(\pm\) 0.26 \({}^{\text{c}}\) \\ \hline \multirow{4}{*}{Seeds} & 25 \({}^{\circ}\)C & 185.23 \(\pm\) 2.6 \({}^{\text{b}}\) & 22.12 \(\pm\) 0.22 \({}^{\text{b}}\) & 0.225 \(\pm\) 0.22 \({}^{\text{b}}\) & 0.789 \(\pm\) 0.31 \({}^{\text{b}}\) \\ \cline{2-6}  & 30 \({}^{\circ}\)C & 221.18 \(\pm\) 3.1 \({}^{\text{a}}\) & 32.21 \(\pm\) 0.13 \({}^{\text{a}}\) & 0.235 \(\pm\) 0.26 \({}^{\text{a}}\) & 0.815 \(\pm\) 0.36 \({}^{\text{a}}\) \\ \cline{2-6}  & 35 \({}^{\circ}\)C & 165.28 \(\pm\) 2.7 \({}^{\text{c}}\) & 16.54 \(\pm\) 0.14 \({}^{\text{c}}\) & 0.174 \(\pm\) 0.21 \({}^{\text{c}}\) & 0.615 \(\pm\) 0.28 \({}^{\text{c}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Polyphenic composition of _Moringa oleifera_ grown under different temperatures. endosperm or the cotyledons of the embryo, seeds can accumulate secondary metabolites that will spread in the sprout during its development [44].
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 2 PLL Principle and Chip Introduction
### Introduction of the HMC830 Chip  
The HMC830 is a broadband PLL chip from Hittite. The chip has an integrated voltage-controlled oscillator (VCO), a VCO output divider, a phase detector (PD), and a \(\delta\)-\(\sigma\) modulator.  
Its output frequency range is 25 MHz\(\sim\)3 GHz. It can work in integer or fractional frequency divider mode. It has industry-leading, ultra-low noise, and ultra-low spurious signal characteristics. The output power range of the chip is 0\(\sim\)9 dBm (adjustable in 3 dB steps), and the typical value of the output power is 6 dBm. The phase detection frequency inside the chip is up to 100 MHz. Increasing the phase detection frequency reduces the phase noise and shortens the loop capture time to obtain a high-quality output signal. The chip also has a \(\delta\)-\(\sigma\) modulator for accurate frequency modes, which allows the output frequency of the chip to perform without a frequency error.
# Correction of Aero-Optical Effect with Blow-Suction Control for Hypersonic Vehicles
## 3 Methods and Validation
### Evaluation Parameters for Aero-Optical Effect  
From a physical point of view, the aero-optical effect is essentially a phenomenon of fluid-optical interaction occurring in an optically turbulent flow such as a mixing layer over an optical window, as shown in Figure 6 (Schematic diagram of aero-optical effects).  
The distance a beam travels in a flow field is usually expressed as an optical path length (_OPL_). The _OPL_ is calculated based on geometric optics theory and can be obtained using the ray-tracing method, which mainly assumes that the rays composing the beam propagate through a grid, and that a single grid is taken to be a cell with an isotropic homogeneous fluid inside. For an arbitrary ray, its _OPL_ can be expressed as the following equation [24]:  
\[OPL=\sum n_{i}\cdot L_{i} \tag{1}\]  
where \(n_{i}\) is refractive index of the fluid in the \(i\)-th grid on the ray transmission path, \(L_{i}\) is the ray transmission distance in the \(i\)-th grid, and the refractive index of the fluid can be converted from its density by the Gladstone-Dale relation, as follows:  
\[n=1+\rho K_{GD} \tag{2}\]  
\[K_{GD}=2.2244\times 10^{-4}\bigg{(}1+\frac{4.51\times 10^{-3}}{\lambda^{2}} \bigg{)} \tag{3}\]  
Figure 5: Flowchart of ray tracing for aero-optical effects.  
where \(n\) is the refractive index, \(\rho\) the density, and \(K_{GD}\) represents the Gladstone-Dale coefficient that weakly depends on \(\lambda\). With density expressed in m\({}^{3}\)/kg and wavelength expressed in \(\upmu\)m, \(K_{GD}\) can be approximately acquired by \(\lambda\). In this study, the infrared image sensor is sensitive to wavelengths ranging from 3 to 5 \(\upmu\)m, so a 4 \(\upmu\)m wavelength was computed.  
In practical applications, the optical path difference (\(OPD\)) is a quantity of more interest than the \(OPL\), and moreover, it is easier to obtain, which is defined as follows:  
\[OPD(x,y,z,t)=OPL(x,y,z,t)-\langle OPL(x,y,z,t)\rangle \tag{4}\]  
where the angle brackets denote the spatial average of the \(OPL\)s of all rays, where \(OPD_{\text{rms}}\) is the root mean square of the \(OPD\)s for all rays. From the equations listed above, it can be seen that the image deviation, BSE, and \(OPD\) are three key metrics to evaluate the aero-optical transmission effects, so all of them are used as evaluation parameters in this work. Figure 7 shows the distorted wavefronts, the \(<\)\(OPL\)\(>\), and the \(OPD\) formed by the rays passing through the inhomogeneous flow field.  
The difference in \(OPL\) directly indicates the extent of wavefront distortion. A larger \(OPD\) corresponds to a greater degree of wavefront distortion. By multiplying the wave number with the optical path difference, we can obtain a measure of the phase difference, which consequently reflects the degree of wavefront phase distortion, or simply put, the wavefront distortion itself. The formula below can be employed for quantifying wavefront distortion.  
\[w(x,y,z,t)=\frac{2\pi}{\lambda}OPD(x,y,z,t) \tag{5}\]
## 4 Structural Sensitivity Analysis
### Sensitivity Analysis of the Maximum Deflection to the Pipeline Deviation Coefficient  
The pipeline deviation coefficient is determined by the material itself, so it will inevitably lead to the change in tension control stress, which directly affects the change in structural deflection. Therefore, the control analysis of the pipeline deviation coefficient is essential in the monitoring.  
As shown in Figure 13, under the condition of the maximum cantilever, the maximum deflection at the cantilever end remains unchanged with the variation of the pipeline deviation coefficient. After changing the pipeline deviation coefficient, the maximum variation value of the deflection of each span occurs at the mid-span position, as shown in Figure 14. Under the condition of the maximum cantilever, the maximum variation value of the deflection at the mid-span position of each span varies linearly with the variation of the  
Figure 11: Variation of the maximum deflection at the cantilever end per span.  
Figure 12: Relationship between the change in pipe friction coefficient and the maximum variation value of the deflection per span.  
pipeline deviation coefficient. The variation ranges of the deflection corresponding to the first and second spans are smaller than that corresponding to the other three spans. Among them, the deflection corresponding to the fourth span is most sensitive to the variation of the pipeline deviation coefficient. When the pipeline deviation coefficient changes by 5%, the deflection changes by 0.2%. Above all, the variation of the pipeline deviation coefficient has almost no impact on the bridge alignment, which is also a non-sensitive parameter.
# Privacy and Security in Federated Learning: A Survey
## 3 Security in Federated Learning
### Vulnerabilities in the Ecosystem  
A vulnerability can be defined as a weakness in a system that provides an opportunity for curious/malicious attackers to gain unauthorized access [20]. Scanning all sources of vulnerabilities and tightening defenses are thus mandatory steps needed to build up a federated learning-based system while ensuring the security and privacy of the data. Based on the studied publications, we answer the RQ1 question below. Vulnerability sources are actually similar to those of distributed applications. We categorize them as follows:  
* Compromised communication channels: an insecure communication channel is an open vulnerability in the FL process that could be addressed using a cryptography public key, which keeps message content secure and safe throughout the communication.
* A large group of FL clients: The number of clients participating in the model is large, so the general model is likely to receive models from Byzantine nodes. This type of safety threat is called Data Poisoning [21].
* A single point of failure: The central parameter server, which is at the heart of the network, must be robust and secure to prevent intrusions. Then, its vulnerabilities must be checked to ensure that they are not exploited. Furthermore, the security updates and all the security recommendations must be followed to limit the risk of intrusions [22].
## 3 Verifications of the Models
### Verification with [PERSON]. _et al._  
[PERSON]. _et al._ [19] tested 12 simply supported beam specimens with T-sections over a 3.0 m span, as shown in Figure 11. The specimens were divided into four groups, each containing two externally pre-stressed beams and one without pre-stressing. Two externally pre-stressed RC beams, T2S and T4S, were used for the analysis and verification. Beam T2S was from the second group, while beam T4S was from the fourth group. No deviator is provided in beams T2S and T4S. Beams T2S and T4S feature a straight tendon profile with eccentricity equal to 84 mm along the full span. A summary of the material parameters of these beam specimens is provided in Table 1.  
The comparisons between the analytical predictions and the experimental results of the entire load-midspan deflection response of the beam specimens are presented in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Beam** \\ **Specimen** \\ \end{tabular} & \begin{tabular}{c} **As (mm2)** \\ **For Steel** \\ \end{tabular} & \begin{tabular}{c} **f\({}_{\text{y}}\) (MPa)** \\ **For Steel** \\ \end{tabular} & \begin{tabular}{c} **A\({}_{\text{pe}}\) (mm2)** \\ **For Tend** & \
\begin{tabular}{c} **f\({}_{\text{pu}}\) (MPa)** \\ **For Tend** &
\begin{tabular}{c} **f\({}^{\prime}\) (MPa)** \\ **For Concrete** \\ \end{tabular} \\ \hline T2S & 340 & 612 & 39 & 935 & 1607 & 40.1 \\ T4S & 603 & 413.7 & 75 & 994 & 1986 & 41.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of parameters of beam specimens [19].  
Figure 11: Profile of the external tendon and loading: (a) finite element; (b) laboratory.  
Figure 10: Load–deflection curves of experimental tests [1] and FE models: (a) sample S\({}_{1}\), S\({}_{2}\); (b) sample S\({}_{3}\), S\({}_{4}\); (c) sample S\({}_{5}\), S\({}_{6}\).  
Figure 12. It can be seen from Figure 12 that the FE model reproduced the experimental load-deflection response from zero loads up to ultimate with considerable agreement. It can also be seen that the curves exhibit approximately three different stages during the loading process. The transition from the first stage to the second stage was the result of the cracking of the concrete, while the transition from the second stage to the third stage was the result of the yielding of ordinary reinforcing steel. The third stage was the failure stage, with maximum mid-span deflection, as shown in Figure 13. These stages of loading and failure were presented in the previous validation.
## 4 Experimental Results and Discussion
### Assessment Methods  
To assess the performance of different image denoising methods, PSNR and SSIM indexes can be utilized to effectively evaluate denoising algorithms. The indexes consider the overall quantification and perception of image quality.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 4 Experimental Results
### Rate-Distortion Efficiency
#### 4.3.1 Test Dataset and Anchors  
To demonstrate the performance of the proposed method, 8i Voxelized Full Bodies [15] are selected as test dataset. As shown in Figure 6, the geometry and attribute characteristics are completely different from the training dataset, which demonstrates the model generalization. We compare with two learning-based methods Deep-PCAC and SparsePCAC, and two different G-PCC versions: TMC13v6 and the latest and state-of-the-art TMC13v14.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 2 Proposed REC Model
### Parameters--Data to Be Provided by the User  
* _E\({}_{i}^{load}(t)\)_: Reference yearly load profile for user \(i\) [kWh];
* _E\({}_{j}(t,y)\)_: Energy produced by plant \(j\) normalized for the rated power [kWh/kW\({}_{rated}\)];
* _Rel\({}_{ij}\)_: Relationship between users and generators. 1: user \(i\) and generator \(j\) are under the same POD. 0: user \(i\) and generator \(j\) are under different PODs;
* _PE\({}^{\sim market}(t)\)_: Reference yearly price profile for the energy sold to the market [E\(\in\)/kWh];
* _PE\({}^{\sim market}(t)\)_: Reference yearly price profile for the energy bought from the market [E\(\in\)/kWh];
* _P\({}_{j}^{max}\)_: Maximum rated power for generator \(j\);
* _Capex\({}_{j}\)_: Investment cost for generator \(j\) [E\(\in\)/kW\({}_{rated}\)];
* _Opex\({}_{j}\)_: Operational cost for generator \(j\) [E\(\in\)/kW\({}_{rated}\)/year];
* _ESS\({}_{cap}^{max}\)_: Maximum storage capacity [kWh];
* _EPR_: Minimum Energy to Power Ratio [h];
* _SOC\({}_{min}\)_: Minimum state of charge of the ESS;
* _Cycles\({}_{max}\)_: Maximum number of cycle for the ESS;
* _Capex\({}_{ESS}\)_: Investment cost of the energy storage system [E\(\in\)/kWh\({}_{rated}\)] ;
* _Opex\({}_{ESS}\)_: Operational cost of the energy storage system [E\(\in\)/kWh\({}_{rated}\)];
* _RepESS_: Replacement cost of the energy storage system [E\(\in\)/kWh\({}_{rated}\)];
* _EC\({}_{cost}\)_: Administrative cost for the energy community [E\(\in\)/year];
* _\(\eta_{CH}\)_: Charge efficiency;
* _\(\eta_{DIS}\)_: Discharge efficiency;
* _AF\((y)\)_: Actualization factor;
* _Inc_: Incentive on shared energy [E\(\in\)/kWh];
* _EL_: Expected life of the investments [year];
* _DR_: Discount rate or return that could be earned in alternative investments [%];
* _ESsi_: Shared Energy saving index. Saving achieved thanks to energy sharing [E\(\in\)/kWh];
* _SCsi_: Self-Consumption saving index. Saving achieved thanks to self-consumption [E\(\in\)/kWh];
* _load\({}_{trend}\)_: Yearly growth of the load [%/year];
* _price\({}_{trend}\)_: Yearly growth of the price [%/year];
* _%\({}_{ded}\)_: Percentage of the investment expense that can be deduced;
* _year\({}_{ded}\)_: Number of years in which the deduction has to be split.
## 5 Formal Modeling and Analysis of SAPS
### Perfect Forward Secrecy  
In our scheme, the un-traceability of CDH plays a vital role in resisting guessing previous session keys by the adversary. The session key is generated using \(\mathrm{H}(\mathrm{d}_{\mathrm{A}},\mathrm{d}_{\mathrm{B}}\mathrm{P},\ \mathrm{ID}_{ \mathrm{B}},\ \mathrm{ID}_{\mathrm{A}})\), where \(\mathrm{d}_{\mathrm{A}},\mathrm{d}_{\mathrm{B}}\) are nonce numbers chosen by both participants specially for the specific session and are different from the nonce numbers used during the authentication process, such as \(\mathrm{N}_{1},\ \mathrm{N}_{2},\mathrm{N}_{3}\). In the proposed protocol, the end parties compute the session key, SK, as \(\mathrm{H}(\mathrm{d}\mathrm{X}_{\mathrm{B}},\mathrm{ID}_{\mathrm{B}},\ \mathrm{ID}_{ \mathrm{A}})\) and \(\mathrm{H}(\mathrm{d}\mathrm{X}_{\mathrm{A}},\mathrm{ID}_{\mathrm{B}},\ \mathrm{ID}_{ \mathrm{A}})\), where \(\mathrm{d}\mathrm{X}_{\mathrm{A}}\) and \(\mathrm{d}\mathrm{X}_{\mathrm{B}}\) are computed by randomly chosen numbers by both parties at their end with the help of the trusted server. Knowing the secret key of the server and the password does not help the adversary to compute a previously established session key, because the secret credentials used to compute the secure key are not based on the password and server's public key. If the adversary wants to know an old session key he /she must compute \(\mathrm{d}\mathrm{X}_{\mathrm{A}},\ \mathrm{d}\mathrm{X}_{\mathrm{B}}\). However, since the adversary does not know the \(\mathrm{R}_{\mathrm{P}}\) due to the hardness of ECDLP, the adversary cannot compute the secret credentials. Therefore, the proposed scheme provides perfect forward secrecy.