esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-Activating PROTACs in Cancer: Chemical Design, Challenges, and Applications
## 3 Intracellular Delivery and Controlling Activation/Deactivation ProtACs
### Photocaging PROTACs of Bromodomain Proteins
To expand the antiproliferative spectrum of **pc-PROTAC1**, various concentrations of **pc-PROTAC1** and **dBET1**, with or without irradiation on BRD4-dependent human [PERSON]'s lymphoma cells (Namalwa cells), were examined. Interestingly, without irradiation, **pc-PROTAC1** (GI\({}_{50}\) = 3.1 \(\upmu\)M) showed a weaker antiproliferative activity than **dBET1** (GI\({}_{50}\) = 0.34 \(\upmu\)M) and was unable to achieve reasonable antiproliferative, even at 50 \(\upmu\)M (**pc-PROTAC1**). However, upon irradiation, **pc-PROTAC1** showed a comparative antiproliferative activity (GI\({}_{50}\) = 0.4 \(\upmu\)M) compared to **dBET1**. Furthermore, a 10-day colony-forming assay was investigated on a liver cell line (UHH7 hepatocellular carcinoma cells) to assess the long-term antiproliferative effects of **pc-PROTAC1**. Without irradiation, **pc-PROTAC1** exhibited no change in colony density, while upon irradiation, **pc-PROTAC1** (at 5 \(\upmu\)M) achieved a maximum inhibition. These studies showed that UV-light-basedintracellular photoactivation of **pc-PROTAC1** into a PROTAC form (**dBET1**) degrades the BRD4 cellular protein level in cancer cells [41]. The authors explored in vivo activity of **pc-PROTAC1** using zebrafish as a model organism. Structurally, the BRD4 protein has interspecies conserved domains (domains: BD1, BD2, and ET) [44], where the primary binding site of JQ1 is characterized by BD1 and BD2 domains, which are importantly highly conserved between zebrafish and humans [43]. Additionally, CRBN of zebrafish has a high sequence similarity with the human ortholog protein. Zebrafish embryo has a higher level of BRD4 [44], which led the authors to use 12 h post-fertilization zebrafish embryos. Those embryos were treated with **pc-PROTAC1** (100 uM in embryo medium), irradiated (\(\lambda\) = 365 nm, for 10 min), and incubated at 28.5 \({}^{\circ}\)C. The following samples were prepared: DMSO (1% in embryo medium)-treated embryos as a blank control, **dBET1** (50 uM in embryo medium)-treated embryos as a positive control, and **pc-PROTAC1** (100 uM in embryo medium)-treated embryos with no irradiation as a negative control. **pc-PROTAC1** with irradiation or **dBET1**-treated embryos showed a thinner yolk extension with respect to both the controls (DMSO and **pc-PROTAC1** without irradiation) at 24 h post-fertilization. Quantitative cellular BRD4 protein levels were studied by Western blotting from whole-cell extracts of the treated embryos at 36 h post-fertilization to evaluate the degradation activity of **pc-PROTAC1**. In the study, substantial BRD4 protein degradations in embryos treated with **dBET1** and **pc-PROTAC1** (50 or 100 uM) with irradiation were observed, demonstrating the light-activating degradation activity of **pc-PROTAC1** in zebrafish [41].
## 4 Simulation and Evaluation
### BER Comparison  
Figures 5 and 6 compare the BER performance of our KDCD with different SCMA decoders, including a TB-RMPA [10], and deep learning decoder (DLD) [15], over the AWGN and downlink Rayleigh fading channel, respectively. According to Figure 5, Our KDCD consistently outperforms the deep learning decoder for the AWGN channel (DLD-A) [15] and TB-RMPA across different \(E_{b}/N_{0}\) values. It is noteworthy that when the value of \(E_{b}/N_{0}\) exceeds 10 dB, as \(E_{b}/N_{0}\) increases, the BER performance of the KDCD becomes more significant. In addition, as shown in Figure 6, our KDCD also exhibits better BER performance than the TB-RMPA and deep learning decoder for the downlink Rayleigh fading channel (DLD-R) [15]. Based on this analysis, the explanations for the superior decoding performance of our proposed KDCD in various channel environments compared with other decoders are as follows.  
* Compared with the conventional decoding algorithm (TB-RMPA): Our KDCD can directly utilize the effective information of the received signals and channel characteristics to accomplish the decoding operations. However, the TB-RMPA relies on a large number of iterations. During every iteration, both valid and invalid information participate in the information exchange between resource nodes and user nodes, which reduces the effectiveness of message updates. In addition, our KDKD provides extra exploitable features (the features of connecting lines in the \(K\)-Polygon diagram), which facilitate the image classification.
* In contrast to previous DL-based decoders (DLDs): Our KDCD allows the neural network to leverage more features. Moreover, our KDCD can directly accomplish the  
Figure 4: The impact of the choice of \(E_{b}/N_{0}\) values for training on the BER over AWGN and downlink Rayleigh fading channel in our decoder.  
signal detection tasks with image classification, fully utilizing the potential of neural networks' capability for feature extraction.  
It should be noted that MobileNet performs much better than it did in the ImageNet competition [22]. This improvement is due to the appropriate selection of \(E_{b}/N_{0}\) values for training, which prevents the received signal \(\mathbf{y}\) from being significantly affected by AWGN, leading to a high degree of similarity in the distribution of feature points on quadrilateral diagrams of the same type, which is beneficial for the image classification.  
However, our KDCD shares the same communication limitations as those in [15; 16]. Since channel estimation is not our main focus, our KDCD requires the strong assumption of perfect CSI. Additionally, our KDCD only contributes to the decoding of downlink SCMA  
Figure 5: BER comparison of TB-RMPA, DLD-A, and KDCD over AWGN channel.  
Figure 6: BER comparison of TB-RMPA, DLD-R, and KDCD over downlink Rayleigh fading channel.  
systems, lacking the ability for integrated encoding and decoding, and thus requiring a well-configured codebook at the transmitter.
# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
## 4 Experiments and Analysis
### Comparative Experimental Results and Analysis  
#### 4.4.1 Analysis of Training Process  
Our EER-DETR model has demonstrated excellent performance in detecting three types of defects, and the verification result is shown in Figure 6. Among them, the Precision rate for the spot is as high as 0.943, while the rates for crack and grid reach 0.891 and 0.882, respectively, indicating that the detection results are highly reliable, with an extremely low false detection rate. At the same time, the model also has strong Recall capabilities. The Recall rates for the three types of defects all exceed 0.85, proving that the model can effectively capture the vast majority of defects and significantly reduce the risk of missed detections. Under the conventional IoU threshold (0.5), the mAP50 for the grid and spot both exceed 0.9, and cracks are close to this level. This demonstrates the model's balanced advantages in detection accuracy and coverage. The model maintains stable performance for different types of defects, verifying its practicality and generalization ability in complex industrial quality inspection scenarios. In summary, the EER-DETR model shows high accuracy, strong robustness, and comprehensiveness in detecting defects on photovoltaic panels, providing efficient and reliable technical support for industrial automated quality inspection.  
Table 1 presents the detection results for three different types of defects.
## 3 Literature Review  
The previous study in [25] has already utilized neural ODE to model a motor-driven propeller dynamical system via simulation. In this research, the fitting performance of the neural ODE model is compared with a physical model and a NARX model, which represent physically based and discrete-time data-driven methods, respectively. Furthermore, NARX models of the 1 st order and 10 th order are both applied to highlight that the neural ODE model can be closer to real system dynamics. The system dynamics is simulated using MATLAB/Simulink with Equation (10), employing RK4 as the solver with a numerical time step of \(5\times 10^{-4}\) s. The measurement sampling frequency is set to 0.01 s and measurement noise follows normal distribution with a mean of 0 and a standard deviation of \(\sqrt{10}\). The reference parameters are \(J=0.15,\alpha=0.012,C_{D}=0.0025\). Figures 5 and 6 show the simulation data for training and testing. The fitting results of different models are displayed in Figure 7, which shows the neural ODE model has a better fitting performance and generalization compared to others. Therefore, the previous study has proved that the continuous structure in the neural network can not only enhance the modeling accuracy but also reduce the model's complexity. To further improve the feasibility and interpretability, this paper combines neural ODE with physics-informed terms to investigate its fitting performance on real-world data.  
Figure 4: The training process of the hybrid neural ODE model.  
Figure 5: Simulation data for training. (**a**) Excitation input torque; (**b**) the response of fan speed.
###### Abstract  
Low-level warm clouds are a major component in multilayered cloud systems and they are generally hidden from the top-down view of satellites with passive measurements. This study conducts an investigation on oceanic warm clouds embedded in multilayered structures by using spaceborne radar data with fine vertical resolution. The occurrences of warm cloud overlapping and the geometric features of several kinds of warm cloud layers are examined. It is found that there are three main types of cloud systems that involve warm cloud layers, including warm single layer clouds, cold-warm double layer clouds, and warm-warm double layer clouds. The two types of double layer clouds account for 23% and in the double layer occurrences warm-warm double layer subsets contribute about 13%. The global distribution patterns of these three types differ from each other. Single-layer warm clouds and the lower warm clouds in the cold-warm double layer system they have nearly identical geometric parameters, while the upper and lower layer warm clouds in the warm-warm double layer system are distinct from the previous two forms of warm cloud layers. In contrast to the independence of the two cloud layers in cold-warm double layer system, the two kinds of warm cloud layers in the warm-warm double layer system may be coupled. The distance between the two layers in the warm-warm double layer system is weakly dependent on cloud thickness. Given the upper and lower cloud layer with moderate thickness of around 1 km, the cloudless gap reaches its maximum when exceeding 600 m. The cloudless gap decreases in thickness as the two cloud layers become even thinner or thicker.  
overlapping; multilayered clouds; warm clouds; warm-warm double layer clouds; geometric features +
Footnote †: journal: _arxiv.org_  
0000-0002-4309-2291][PERSON]
## 2 Inter-DCI Link Modeling and OP Estimation
### Optical Receiver  
At the optical receiver, the PIN photodetector is modeled as a square-law device with responsivity \(R_{\lambda}\). The electrical filter of the optical receiver is modeled by a 3 rd order Bessel filter with \(-3\) dB bandwidth, \(B_{e,RX}\). The electrical noise power after the receiver electrical filter is given by [39]  
\[\sigma_{c}^{2}=R_{\lambda}^{2}NEP^{2}B_{e,n} \tag{7}\]  
where \(NEP\) corresponds to the noise equivalent power [39] and \(B_{e,n}\) is the noise equivalent bandwidth of the receiver electrical filter [43].  
The power of the detected ASE noise for the \(k\)-th received PAM4 signal at the electrical filter output is given by [38]  
\[\sigma_{ASE,k}^{2}=4S_{ASE}B_{e,n}R_{\lambda}^{2}(gP_{k}+B_{o,n}S_{ASE}) \tag{8}\]where \(B_{0,n}\) is the noise equivalent bandwidth of the optical filter. At the decision circuit, the received signal impaired by ICXT, ASE noise, and electrical noise is processed to estimate the BER and OP.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 3 Components of Co-Simulation Tool
### Simulator System--Hardware  
The driving simulator consists of a high-performance PC, visual display equipment, a simulator chassis, and a motion platform. The PC is equipped with 16 GB RAM, an 8 GB GPU, and an Intel i7-10700 processor, ensuring the smooth operation and real-time rendering of the driving environment. The visual system includes three 55-inch screens, providing an immersive field of view for participants. The simulator chassis features a Sparco Chrono Road seat, a Cube Controls GT Pro Momo steering wheel, and Fanatec Clubsport V3 pedals, allowing for a realistic driving experience. The chassis is mounted on a D-BOX motion platform with four 1.5-inch actuators, enabling three degrees of freedom (roll, yaw, and heave).  
Motion cues are synchronized with the simulation through the D-BOX Live Motion SDK, ensuring that participants experience realistic vehicle dynamics.  
The bicycle simulator consists of a standard bicycle mounted on a Tacc Neo 2T smart trainer, which transmits speed and acceleration data to the simulation via Bluetooth. Steering input is captured using an Elite Sterzo smart steering block, also connected via Bluetooth. This setup ensures that bicyclists can interact naturally with the virtual environment, making precise steering and pedaling adjustments based on the simulated traffic conditions. The system is compatible with the pycycling Python package 3.9. which facilitates communication between smart cycling equipment and the simulation software.  
The pedestrian simulator consists of a manual treadmill, a VIVE Pro virtual reality (VR) headset, and a speed sensor to measure treadmill belt speed. The treadmill allows participants to walk in place while navigating through the virtual environment, ensuring a natural walking experience. The VIVE Pro headset provides an immersive VR experience, allowing pedestrians to perceive vehicles and road infrastructure in three dimensions. The speed sensor continuously monitors the treadmill belt speed and transmits real-time walking speed data to the simulation, ensuring accurate movement synchronization between the pedestrian's physical actions and their virtual representation. This setup allows for naturalistic pedestrian behavior in the simulation, enabling precise measurements of gap acceptance, crossing decisions, and physiological responses to approaching vehicles.  
An overview of the simulator hardware setup is presented in Figure 2. By integrating these three simulator components within a shared virtual environment, the system enables realistic multimodal interactions between drivers, bicyclists, and pedestrians. The combination of motion-based driving simulation, smart cycling equipment, and VR-enhanced pedestrian simulation provides a highly flexible and scalable platform for studying human behavior in complex traffic scenarios.
_Applied_  
_sciences_  
Variable Wavefront Curvature Phase Retrieval Compared to Off-Axis Holography and Its Useful Application to Support Intraoperative Tissue Discrimination  
[PERSON] 1,2,*, [PERSON] 3, [PERSON] 1, [PERSON] 1\({}^{\text{\textregistered}}\), [PERSON] 3 and [PERSON] 1  
1 Institut fur Technische Optik, Universitat Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany; [EMAIL_ADDRESS] (Q.L.); [EMAIL_ADDRESS] (G.P.); [EMAIL_ADDRESS] (W.O.)  
2 Institut fur Lasertechnologien in der Medizin und Messtechnik, Helmholtzstrasse 12, 89081 Ulm, Germany  
3 Universitatsklinik fur Urologie, Eberhard Karls Universitat Tubingen, Hoppe-Seyler Strasse 3, 72076 Tubingen, Germany; [EMAIL_ADDRESS] (J.H.); [EMAIL_ADDRESS] (A.S.)  
* Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +49-(0)73-1142-9225  
Received: 11 September 2018; Accepted: 28 October 2018; Published: 3 November 2018  
Featured Application: Phase retrieval via variable wave front curvature for intraoperative medical imaging.  
**Abstract:** Quantitative phase imaging can reveal morphological features without having to stain the biological sample. This property has important implications for intraoperative applications since the time spent during histopathology can be reduced from a few minutes to a few seconds. However, most common quantitative phase imaging techniques are based on the interferometric principle, which makes them more prone to disturbing environmental influences, such as temperature drift and air turbulence. In the last decade, with the advance of computing power, many different iterative quantitative phase imaging techniques, which only require the recording of the diffracted wavefield, and therefore offer increased robustness towards environmental disturbances, have been proposed. These are particularly well-suited for the application outside the well-controlled lab environment such as an operating theatre. The optical performance of our developed iterative phase retrieval method based on variable wavefront curvature will be evaluated by reference to off-axis digital holography and applied for intraoperative discrimination of tissue.  
**Keywords:** phase retrieval; quantitative phase imaging; biomedical imaging
## 2 Related Works
### IOS-Assisted NOMA  
This section discusses the advancement of IOSs in the NOMA system. The authors of [14] examined the performance of IOS-assisted DL NOMA networks with phase quantization and channel estimation errors. Simulation results show that IOS-assisted NOMA performs comparably to OMA with negligible impact from channel correlation for larger numbers of IOS components \((L)\). The authors of [16] investigated using IOSs under millimeter wave (mmWave) networks. The study formulated the SSE maximization problem and suggested an AO framework to design active and passive beamforming vectors and PA factors.  
In [20], the performance of vehicle-to-vehicle (V2V) communications using RISs and IOSs under NOMA and OMA schemes is investigated. The study derived outage probabilities, E-SSE, and energy efficiencies, showing that the RIS/IOS significantly enhances V2V communication performance. Table 1 compares the contributions of the proposed work compared to the existing works. The components considered are marked with a checkmark, while those not taken into account are marked with a cross.
# A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks
## 5 Conclusions  
In this paper, a hybrid handover scheme was put forward for vehicular VLC/RF communication networks. When a LoS link blockage happened during VLC signal transmission, the hybrid handover scheme aimed to make a decision about switching to the next VLC AP, to the RF AP, or waiting for link recovery, with full consideration of the state of the vehicle terminals. The handover decision problem was formulated as an MDP and the optimal policy was obtained using a dynamic programming method. The simulation results revealed that the proposed optimal handover policy achieved near-optimal performance with much lower VHO numbers, and thus was able to balance the QoS and the VHO signaling cost, and avoid the potential ping-pong effect in the heterogeneous systems. Furthermore, the performance of all hybrid handover strategies exceeded that of their VHO counterparts, benefiting from the newly introduced early HHO option.  
Conceptualization, L.J.; Data curation, S.F.; Formal analysis, L.J.; Funding acquisition, L.J.; Methodology, L.J. and S.F.; Project administration, L.J.; Resources, Y.Z.; Software, S.F.; Supervision, Y.Z.; Validation, Y.Z. and J.-Y.W.; Visualization, L.J. and S.F.; Writing--original draft, L.J.; Writing--review and editing, L.J., S.F. and J.-Y.W. All authors have read and agreed to the published version of the manuscript.  
Figure 4: The performance comparison of the proposed optimal handover scheme and other benchmarks with respect to the handover delays.  
**Funding:** This work was funded in part by National Natural Science Foundation of China under Grants 62301257, in part by Natural Science Foundation of Jiangsu Province under Grant BK20200488.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Materials and Methods
### Atmospheric Delay Model
#### 2.3.2 Neutral Atmosphere Delay  
The lower part of the earth's atmosphere, known as the neutral layer, extends from the ground surface to an altitude of about 60 km, located just beneath the ionosphere's lower boundary. Delays experienced at the zenith of the neutral atmosphere can be categorized into two types: the static zenith delay (commonly referred to as the dry delay) and the dynamic zenith delay (often termed the wet delay).  
Since the trajectory of radar electromagnetic waves essentially follows a straight line, the mapping functions applied to the neutral atmosphere closely resemble those used for the ionosphere. The delay model for the neutral atmosphere is delineated as follows:  
\[\Delta R_{neu}=\frac{1}{\sqrt{1-\left(\frac{R_{e}\sin a}{R_{e}+I_{i}}\right)^ {2}}}\{10^{-6}[F_{c}k_{1}\frac{R_{0}}{M_{d}}g_{m}^{-1}P_{s}+(k_{2}-F_{c}k_{1} \frac{M_{w}}{M_{d}})\frac{R_{0}}{M_{w}}PWV]\} \tag{16}\]  
In the above formulas, \(\Delta R_{neu}\) is neutral atmosphere delay; \(F_{c}\) is atmospheric correction factor; \(F_{c}=1+\frac{C_{2}-c_{1}}{c_{1}+18722\times 10^{6}}(c_{1}=300,c_{2}=375)\); \(k_{1},k_{2}\) are the function of wavelength \(\lambda\), \(k_{1}(\lambda)=164.63860\frac{238.0185+\lambda^{-2}}{(238.0185-\lambda^{-2})^{ 2}}+4.77299\frac{57.362+\lambda^{-2}}{(57.362-\lambda^{-2})^{2}}\), \(k_{2}(\lambda)=0.648731+0.0174174\lambda^{-2}+3.55750\times 10^{-4}\lambda^{-4}+6.1957 \times 10^{-5}\lambda^{-6}\); \(R_{0}\) is the universal gas; \(M_{w},M_{d}\) are molecular weight of the dry and wet atmosphere; \(P_{s}\) is the atmospheric pressure; \(g_{m}\) is the acceleration of gravity, which varies with latitude and elevation, \(g_{m}=9.8062(1-0.00265\cos(2\varphi)-3.1\times 10^{-7}(0.9h+7300))\); and \(PWV\) is the atmospheric precipitable water, \(PWV=g_{m}^{-1}\int_{P_{1}}^{P_{2}}q_{w}dp\), where \(q_{w}\) is specific humidity.  
ERA5 can provide isobaric surface data including pressure, specific humidity, and potential, where potential \(\phi\) can be converted into geometric elevation \(z\), \(z=\frac{\phi R_{c}}{\xi_{n}R_{c}-\phi}\). However, ERA5 only includes data from the surface up to about 48 km, with a small layer of neutral upper atmosphere above 48 km to be considered. This part has almost no wet delay effect and can be calculated by the Saastamoinen model.  
\[\Delta R_{neu}=\frac{1}{\sqrt{1-\big{(}\frac{R_{c}\sin\theta}{R_{c}+h_{i}} \big{)}^{2}}}\{10^{-6}[\frac{0.002276\cdot P_{\text{top}}}{1-0.002667\cdot\cos 2 \varphi-0.00000028\cdot h_{\text{top}}}\} \tag{17}\]