esa-sceva/satcom-chunk-collection huggingface dataset:
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 6 Conclusions  
This paper presents a 79 GHz mmWave radar system installed on the top of the tower with a pair of circularly polarized antenna to monitor the distance between wind turbine blades and the tower. To mitigate the low channel SNR caused by a small-RCS blade obliquely incident from more than 70 m, high-gain antennas and SIMO radar configurations are designed to increase the P\({}_{\text{t}}\) of the link. In addition, a pair of circularly polarized antenna is used to reduce the high level of weather interference from an unfriendly environment. In this paper, the radar architecture design is introduced, and the circularly polarized antenna is simulated and measured. Finally, the experimental results demonstrate the performance of the circularly polarized mmWave radar in various weather conditions, including sunny, cloudy, overcast, light rain, and thundershower, proving the feasibility of the circularly polarized mmWave radar in detecting blade clearance with good performance.  
Conceptualization, J.C., B.G., Y.J. and Y.L.; validation, J.C., Y.J., and Z.B.; writing--original draft preparation, J.C.; writing--review and editing, Y.L.; project administration, Y.L., L.W., S.W. and R.W. funding acquisition, G.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Suzhou Dufeng Technology Co., Ltd. under Grant No. D71-0107-19-115.  
**Data Availability Statement:** The data presented in this study are private, and it comes from the school of Communication and Information Engineering, Shanghai University, and Suzhou Dufeng Technology Co. Ltd.  
**Conflicts of Interest:** The Author [PERSON] was employed by the CGN Digital Technology Co., Ltd. Authors [PERSON], [PERSON] and [PERSON] was employed by the Suzhou Dufeng Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict 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.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Method** & **Detection Range** & **Weather** \\ \hline
[1] & one laser &
\begin{tabular}{c} 5 to 20 m \\ (middle of the tower to the blade tip) \\ \end{tabular} & N/A \\ \hline
[2] & one camera &
\begin{tabular}{c} 30 to 40 m \\ (ground to the blade tip) \\ \end{tabular} & N/A \\ \hline
[3] & eight 24 GHz mmWave radar & \begin{tabular}{c} 5 to 20 m \\ (middle of the tower to the blade tip) \\ \end{tabular} & N/A \\ \hline This work & one 79 GHz mmWave radar & \
\begin{tabular}{c} 70 to 80 m \\ (top of the tower to the blade tip) \\ \end{tabular} &
\begin{tabular}{c} sunny, cloudy, overcast, light \\ rain, thunderstorm \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of other published literature.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 4 Experimental Evaluations  
In this section, we evaluate the NMSE of the precoding matrices with different CRs and the influence of the feedback accuracy on the BLER performance. The comprehensive performance comparisons of the uplink MU-MIMO system with different number of UEs, different modulation orders, and different channel models are provided. The uplink MU-MIMO system parameters and the coefficients of the channel coding are listed in Table 2. The SNR is generally defined as the ratio of the signal power to the noise power at the receiver  
\[\text{SNR}=\frac{\mathbb{E}\Big{[}\big{\|}\mathbf{H}\mathbf{F}\mathbf{s} \big{\|}_{2}^{2}\Big{]}}{\mathbb{E}\Big{[}\big{\|}\mathbf{n}\big{\|}_{2}^{2} \Big{]}}=\frac{N_{t}K}{\sigma^{2}} \tag{13}\]
## 3 Results and Discussion
### 3.3. USeNPs Cytotoxicity
#### 3.3.2 Mechanism of USeNPs Cytotoxicity by EGFR Inhibition Evaluation  
EGFR is an epidermal growth factor family member cell surface receptor, and was the first receptor tyrosine kinase identified, and the first one linked to cancer [59]. Several human cancers are associated with hyperepression and/or hyperactivation of EGFR kinase, so searching for anticancer therapeutics targeting EGFR has become very important [37]. Investigation of USeNPs inhibitory effects on EGFR kinase enzyme was the aim of this study and used Sorafenib as a reference drug. Here, these nanoparticles were found to have inhibited  
Figure 11: Effect of USeNPS on normal Vero cell line (**control1**,**A1**,**B1**,**C1**) and HepG2 cell line (**control2**,**A2**,**B2**,**C2**). (**A1**,**A2**) at concentration 125 \(\upmu\)g/mL, (**B1**,**B2**) at concentration 250 \(\upmu\)g/mL and (**C1**,**C2**) at concentration 500 \(\upmu\)g/mL.  
HepG2 by inducing the degradation of EGFR through the inhibition of the EGFR kinase enzyme. These findings agree with those of [PERSON] et al. [60] who found promising results for the cytotoxicity of Saccharina japonica extract against stemness of glioma stem cells by degrading EGFR variant III. Figure 12 illustrates that USeNPs has an EGFR enzyme inhibitory effect at all the tested concentrations (0.25-500 ug/mL) in a concentration-dependent manner, with IC\({}_{50}\) = 230.79 \(\pm\) 3.47 ug/mL, and IC\({}_{50}\) = 0.76 \(\pm\) 0.18 ug/mL for USeNPs and Sorafenib, respectively, but does not exceed the reference drug at any concentration. Concentrations of 250 and 500 ug/mL have a strong inhibitory effect on the EGFR kinase enzyme with enzyme activity inhibition percentages of at least 50% (51.28 \(\pm\) 3.06 and 76.31 \(\pm\) 2.47, respectively,) and their effects were near that of the reference drug. This inhibitory effect may be due to USeNPs' hydrogen bond formation with the EGFR, providing stronger binding affinity and ensuring a promising enzyme inhibitory activity [61; 62; 23].
# Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
###### Abstract  
The satellite Internet can cover up to 70% of the surface of our planet Earth to provide network services for nearly 3 billion people. As such, it is promising to become the building block of future 6G networks. The satellite Internet is capable of providing uniform communication capacity to every part of the Earth's surface, due to its uniform and symmetrical constellation structure, while the uneven distribution of ground populations leads to globally uneven traffic delivery requests, incurring a mismatch between the capacity and traffic transmission demands. As such, traditional single-criteria (e.g., shortest delay) routing algorithms can lead to severe network congestion and cannot provision delay-deterministic data delivery. To overcome this bottleneck, we propose a multi-criteria routing and scheduling scheme to redirect time-tolerant data, thus preventing congestion for time-sensitive data, based on the spatiotemporal distribution of data traffic. First, we construct a traffic spatiotemporal distribution model, to indicate the network load status. Next, we model the satellite network multi-criteria routing problem as an integer linear programming one, which is NP-hard and challenging to solve within polynomial time. A novel link weight design based on both the link delay and load is introduced, transforming the mathematical programming problem into a routing optimization problem. The proposed correlation scheduling algorithm fully utilizes idle network link resources, significantly improving network resource utilization and eliminating resource competition between non-time-sensitive and time-sensitive services. Simulation results show that compared with traditional algorithms, the proposed method can increase the throughput of time-sensitive data by up to 20.8% and reduce the packet loss rate of time-sensitive services by up to 76.8%.  
s +
Footnote †: journal: Electronics  
article Internet; routing algorithm; time-sensitive services; quality of service (QoS); differentiated service assurance
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 4 Experimental Validation and Evaluation
### Validation in Simple Scenarios  
We first present several experiments in simple scenarios to validate the feasibility and usability of the implementation. The movement of nodes is orchestrated to create the planned contact chances. The contents requested are vector map tiles and in future we will support more types of informative geographical data that can be overlaid on maps.
## 5 Structural Design and Systems Integration
### Actuation Layout  
A further step of the integrated design of a morphing winglet involves the preliminary assessment of the actuation system, including both the installation and performance requirements. It is worth noting that both influence the effective reproduction of the given aeroshapes under aerodynamic loads.  
In light of the limited space available within the winglet box to accommodate both the winglet actuators and actuation chains, several installation trade-off studies were carried out to achieve the best configuration. A linear electromechanical actuation was considered for each mechanical drive of the two independent surfaces. Such an approach assumes that, in principle, a flight-worthy actuator of adequate size, weight, and power will be developed to withstand the hinge moments due to the aerodynamic loads.  
The actuation chains addressed in this paper are controlled by two separate actuators, one for each surface, positioned at different winglet heights [17] (see Figure 28).  
Such a direct actuation layout, based on a crankshaft design, has the advantages of relative installation simplicity, lower weight, and higher stiffness, with additional benefits in terms of assembly time and maintenance.  
Parameter studies and motion optimization were studied through a multibody model developed in MSC/Adams. The upper morphing surface deployment and its comparison with the given aeroshape is shown in Figure 29. This allowed for a comparison of the actual rotation of the tab with the nominal (expected) values enabling the load control function. A static actuation law was then implemented. As a result, the actuator rod stroke was correlated with the resulting tab rotation. Both functions are reported in Figures 30 and 31, respectively. For the given geometrical parameters, the actuators' performance requirements were then derived (see Table 2). Operating and maximum static axial loads were about 3.5 and 5 kN, respectively. The maximum speed foreseen for the load control function was in the order of 5 mm/s.  
Figure 28: Direct actuation of the two morphing winglet tabs [17].  
Figure 31: Actuation stroke during the upper Tab deployment.  
Figure 30: Upper Tab rotation function in the range [+5\({}^{\circ}-\)15\({}^{\circ}\)].  
Figure 29: Upper morphing tab deployment (\(-\)15 deg)—view from the bottom.
## 5 Simultaneous Calibration of Offsets and Gains
### Algorithm with Reference  
Analogously with the preceding sections, it is possible to construct an algorithm for simultaneous offset and gain calibration based on the introduction of a reference node, now characterized by the pair \((f^{1}_{ref},g^{1}_{ref})\). For gain calibration, we have again the recursion (21) for \(s_{t}\); meanwhile, in the case of offset calibration, we have the following recursion derived from (24):  
\[r_{t+1}= (I+\delta T^{-})r_{t}+\delta x_{t}\Gamma^{-}(I+V^{{}^{\prime}-}_{ t})s_{t} \tag{26}\] \[+\delta x_{t}^{2}\kappa^{-}\beta^{-}[(\Gamma^{w}_{t})^{-}s_{t}+ \Gamma^{-}(I+W^{-}_{t})w^{-}_{t}],\]  
where \(g^{1}_{ref}=1\) and \(f^{1}_{ref}=0\), \(\beta^{-}=\text{diag}\{\beta^{2},\ldots,\beta^{n}\}\) and \(V^{{}^{\prime}-}_{t}=\text{diag}\{\frac{v_{t}^{2}}{x_{t}},\ldots,\frac{v_{t}^{ n}}{x_{t}}\}\).  
The convergence properties of (26) depend on the convergence properties of (21).  
**Theorem 4**: _Let the assumptions of Theorems 2 and 3 hold. Then, \(r_{t}\), generated by (26), is B.I.B.O.-stable; also, \(\|r_{t}\|<\infty\) and \(\|r_{t}\|\to 0\), when \(t\to\infty\) and \(\|v_{t}\|,\|w_{t}\|\to 0\)._  
The proof can be derived as a straightforward technical extension of the presented results.
## 2 System Model and Problem Formulation
### Computation Model  
When a satellite receives a task from a user, it needs to allocate its cache and computational resources appropriately. The energy consumption of caching is mainly dependent on the size of the offloaded tasks, which is denoted as \(e_{adh}=\rho d\), where \(\rho\) is the energy conversion factor. The satellites are assumed to use the dynamic voltage and frequency scaling (DVFS) technique to adjust their core clock frequency [33], where the DVFS technology can adjust the computation frequency of the CPU according to the different demands of the applications. As a result, the energy consumption generated by the task computation is given by,  
\[e_{com}=\kappa a_{n}C_{n}^{2}, \tag{2}\]  
where \(a_{n}\) is the number of CPU cycles required by the satellite to complete the tasks, and \(\kappa\) is a coefficient value dependent on the CPU circuit structure [34]. After the computation, the results need to be returned to the users, the energy consumption generated by the return transmission is,  
\[e_{tran}=\frac{p_{u}\hat{d}}{r^{down}}, \tag{3}\]  
In this paper, when providing services to the ground users, the satellite is assumed to be in a stable state. It needs to consume additional energy to maintain a stable state. Therefore, additional energy consumption would be brought by the hovering state and is denoted by \(e_{b}ov\). In addition, there exists the circuit energy consumption in each satellite, which is the energy consumed by the on-board circuits, such as the computational chip, rotors, and gyroscopes. There, the circuit energy consumption of the satellite is denoted as \(e_{cir}\).
###### Abstract  
Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.  
factor graph optimization; interactive multiple models; vehicle navigation; complex environment; fault detection; isolation and recovery 2024 16 1651 1
## 3 Results
To assess the ResNet18 network's capability in deciphering EEG emotion states, we conducted an analysis utilizing the ROC curve and AUC (Area Under the ROC Curve) for Subject 4. Each of the five emotion categories was treated as a positive example, while all other categories were deemed negative examples. By calculating the True Positive Rate (TPR) and False Positive Rate (FPR) for each category and plotting the curve, we obtained a comprehensive evaluation model, showcasing its performance across diverse categories. The multi-class ROC curve provides a holistic overview of performance indicators across all categories through the following two perspectives: micro average, and macro average. The micro average ROC curve computes the overall TPR and FPR by aggregating the number of true and false positive cases across all categories, thus treating each category with equal importance and consolidating their performance into a singular metric. Conversely, the macro average ROC curve evaluates the model's performance by calculating the TPR and FPR for each category separately and then averaging the values, assigning equal weight to each category. The significance of the multi-class ROC curve lies in its ability to offer a comprehensive evaluation perspective on the performance of multi-class classification models. By scrutinizing the TPR and FPR of different categories, one can gauge the model's recognition ability across each category. Both micro average and macro average ROC curves facilitate the comparison of overall performance among different models in multi-class classification tasks. As illustrated in Figure 8, we chose Subject 4 as the focal point of our experimentation. Across five distinct emotions--disgust, fear, sadness, neutrality, and happiness--the corresponding AUC values were 0.9871, 0.9886, 0.8976, 0.9330, and 0.9718, respectively. A higher AUC value indicates stronger discrimination capability within the network for different emotions. Figure 7: Feature distribution visualization by the t-SNE algorithm. The different colors stand for different emotions. Two-dimensional display of classification results from four subjects. (**a**) Subject 2, (**b**) Subject 5, (**c**) Subject 6, (**d**) Subject 9.
## 3 Research Results and Discussion
### Roughness Measurement
The surface affected by wear is distinguished by a greater core fluid retention index (Sci) and diminishing valley fluid retention index (Svi), with a growing bearing index compared to the unaffected surface. In this case, the liquid is the wear products in the form of iron oxides, Fe\({}_{2}\)O\({}_{3}\). Based on the quoted data and the review of the literature [31; 32; 33], it is appropriate to think that higher surface roughness reduces surface resistance to wear as a result of the greater actual contact area between the elements. For that reason, the biggest influence on fretting wear intensity is exerted by high surface roughness parameters and the radius of curvature of microirregularity pits.
# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
## 4 Deep Q-Network for Energy-Efficient Resource Allocation
### Training and Testing Algorithm
```
1:Initialize the online Q-network with random weights \(\theta\) and random biases \(\beta\);
2:Initialize the target Q-network with random weights \(\theta^{target}\) and random biases \(\beta^{target}\);
3:Generate Experience replay memory \(\mathcal{D}\);
4:for each episode \(\mathbf{e}\)do
5: Initialize environment;
6: Generate V2V and V2I networks;
7:for each time step \(t\)do
8: Generate a joint action group \(A\);
9:for each V2V links do
10: Get state \(\mathbf{s}_{t}\) from the environment
11: Choose an action \(a_{t}\) based on the \(\epsilon\)-greedy policy;
12: Append the \(a_{t}\) to \(A\);
13:endfor
14: Interact with the environment based on \(A\) and Calculate reward \(r_{t}\);
15: Get all V2V links state \(\mathbf{s}_{t+1}\) from the environment;
16: Append the \(\mathbf{s}_{t}\), \(a_{t}\), \(r_{t}\), \(\mathbf{s}_{t+1}\) to \(\mathcal{D}\);
17:for each update step \(i\)do
18: Sample a mini-batch of experience set \(E\) from the \(\mathcal{D}\);
19: Calculate the loss:
20:\(y\)=\(r_{t}\)+\(\gamma\) max\({}_{a}(Q(\mathbf{s}_{t+1},a,\theta^{target},\beta^{target}))\);
21: Loss(\(\mathbf{\theta}\),\(\beta\))=\(\sum\
olimits_{\mathbf{s}_{t},a_{t}\in E}(y-Q(\mathbf{s}_{t},a_{t},\theta,\beta))^{2}\);
22: Update the online Q-network with \(\theta\), \(\beta\);
23:endfor
24: Update weights, \(\theta^{target}\leftarrow\theta\)
25: Update biases with \(\beta^{target}\leftarrow\beta\)
26:endfor
```  
**Algorithm 1** Training algorithm  
```
1:Load the Q-network with trained weights \(\theta\) and biases \(\beta\);
2:for each episode \(\mathbf{e}\)do
3: Initialize environment;
4: Generate V2V and V2I networks;
5:for each time step \(t\)do
6: Generate a joint action group \(A\);
7:for each V2V link \(\mathbf{do}\)
8: Get state \(\mathbf{s}_{t}\) from the environment
9: Choose the \(a_{t}\) with the maximum value among the estimated Q-values by inputting the \(\mathbf{s}_{t}\) into the Q-network;
10: Append the \(a_{t}\) to \(A\);
11:endfor
12: Interact with the environment based on \(A\);
13:endfor
14: Calculate the sum rate of V2I links;
15: Calculate the sum rate of V2V links;
16: Calculate the outage probability of V2V links;
17: Calculate the average transmit power of V2V links;
18:endfor
```  
**Algorithm 2** Testing algorithm
## 4 Biosynthesis and Health Implications of Pyrrolizidine Alkaloids
### Effect on Humans and Mechanism of Action  
PAs are considered among the most widespread natural toxins as they can affect wildlife, livestock, and humans through feed and food contamination. PA-poisoning in humans can cause acute (liver damage) and chronic (liver cirrhosis and pulmonary arterial hypertension) toxicity phenomena [57]. In 2011, the EFSA's Panel on Contaminants in the Food Chain (CONTAM) recognized the 1,2-unsaturated PAs as the most dangerous forms for human health and decided to focus on them for the risk assessment [48].  
After ingestion and absorption, PAs (pyrrolizidine alkaloids) undergo metabolic activation in the liver, catalyzed by cytochrome P450 enzymes (CYP3A4 and CYP2C19). For retronecine and heliotridine-type PAs, hydroxylation at the C-3 and C-8 positions forms hydroxynecine derivatives, while otonecine-type PAs undergo oxidative N-demethylation followed by ring closure and dehydration. These reactions produce reactive dehydro-rrolizidine intermediates, leading to the formation of pyrrolizinium ions. These metabolites bind to macromolecules like glutathione (GSH), DNA, and proteins, resulting in detoxification or further reactions causing DNA adducts and oxidative damage. Although GSH conjugates might be considered detoxification metabolites, it has been reported that they also act as reactive metabolites, causing DNA adduct formation [58]. Moreover, GSH conjugation is crucial for detoxification, but depletion of GSH can lead to severe liver damage. Reactive dehydro PAs bind to liver proteins and DNA, causing genotoxicity and carcinogenicity, potentially leading to veno-occlusive disease and tumors [59].
## 3 Case Analysis
### Prediction Accuracy with Factor Ablation
#### 3.2.4 Test Site GPP without NDVI  
In this experiment, all NDVI data were deleted from the training data, which were then trained for each site flux. The final prediction results are shown in Figure 12. Compared to the prediction results without any feature ablation of the training data, the prediction accuracy of the AR site is significantly improved and higher than that after removing Ta, Par, and EVI. The prediction accuracy of the DXST, DXSW, and ZF sites decreases, which is consistent with the results of the previous ablation experiments. For the ZF site, the prediction accuracy is only better than that after removing Ta and EVI. The NMC site has an abnormal increase in accuracy. The prediction accuracy of the GL site is higher than that after removing Ta, Par, and EVI. The prediction accuracy of the HBKO, HBSH, and HBSW sites does not change noticeably.  
Figure 11: Predicted GPP vs. labeled GPP at a single site (no EVI).